data/notebooks/Lab/5_Produce_mock_data.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Produce mock data\n",
"\n",
"Now that we have explore in the previous notebooks the workflow for the four different indicators, we are going to produce mock data to star exploring graphs and visualizations. The workflow that we are going to use would be covered in the table of contents:\n",
"\n",
"## Table of Contents\n",
"- ### [Python libraries](#libraries)\n",
"- ### [1. Explore user data](#importData)\n",
"- ### [2. Default crop and pasture data](#crop_data)\n",
"- ### [3. Generate risk map](#risk)\n",
" - #### [3.1 Unsustainable water use risk](#waterRisk)\n",
" - #### [3.2 Deforestation risk](#deforestationRisk)\n",
" - #### [3.3 Carbon emissions due to land use change risk](#carbonRisk)\n",
" - #### [3.4 Biodiverstity loss due to land use change risk](#biodiversityRisk)\n",
"- ### [4. Get metrics for user data](#metric)\n",
"- ### [5. Final notes](#finalNotes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='libraries'></a>\n",
"## Python libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"import pandas as pd\n",
"\n",
"from processing.geolocating_data import GeolocateAddress"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import rasterio as rio\n",
"import numpy as np\n",
"import rasterio.plot\n",
"import matplotlib.pyplot as plt\n",
"from rasterio.plot import show_hist\n",
"\n",
"\n",
"from rasterstats import gen_zonal_stats\n",
"from rasterstats import zonal_stats"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# import lib\n",
"import time\n",
"import os\n",
"import io\n",
"import requests\n",
"import zipfile"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='importData'></a>\n",
"## 1. Explore user data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"processed_path = '../../datasets/processed/'\n",
"raw_path = '../../datasets/raw/'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Material</th>\n",
" <th>Material d</th>\n",
" <th>Volume</th>\n",
" <th>Country</th>\n",
" <th>Address</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Location t</th>\n",
" <th>Accuracy</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>2400</td>\n",
" <td>China</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((73.49973 39.38174, 73.50468 39...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>1300</td>\n",
" <td>Malaysia</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((98.93721 5.68384, 98.93771 5.6...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>1000</td>\n",
" <td>United States</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((-180.00000 51.79409, -180.0000...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>730</td>\n",
" <td>Japan</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((122.71418 24.44983, 122.71457 ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>490</td>\n",
" <td>India</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((68.11138 23.60145, 68.13528 23...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Material Material d Volume Country Address Latitude Longitude \\\n",
"0 Rubber None 2400 China None None None \n",
"1 Rubber None 1300 Malaysia None None None \n",
"2 Rubber None 1000 United States None None None \n",
"3 Rubber None 730 Japan None None None \n",
"4 Rubber None 490 India None None None \n",
"\n",
" Location t Accuracy geometry \n",
"0 Unknown Low MULTIPOLYGON (((73.49973 39.38174, 73.50468 39... \n",
"1 Unknown Low MULTIPOLYGON (((98.93721 5.68384, 98.93771 5.6... \n",
"2 Unknown Low MULTIPOLYGON (((-180.00000 51.79409, -180.0000... \n",
"3 Unknown Low MULTIPOLYGON (((122.71418 24.44983, 122.71457 ... \n",
"4 Unknown Low MULTIPOLYGON (((68.11138 23.60145, 68.13528 23... "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#import user data\n",
"supply_data = gpd.read_file(processed_path + 'user_data/located_lg_data_polygon_v2.shp')\n",
"supply_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#user data is in the 4326 projection\n",
"supply_data = supply_data.set_crs(\"EPSG:4326\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Cotton', 'Leather', 'Rubber'}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#check unique commodities for calculating the risk maps\n",
"set(supply_data['Material'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='crop_data'></a>\n",
"## 2. Default crop and pasture data\n",
"\n",
"For the generation of the mock data we're going to work on a first iteration producing generic risk map which would be used for default values in other crops and pasture activities.\n",
"\n",
"The main datasets that have been identified for the calculation of either the default values (crop and pasture) or specific commodities (e.g. cotton) have been collected [here](https://docs.google.com/spreadsheets/d/1ehQi8_cvfN4OhTfULHNKegqlygWJrob27LaUmFstuZY/edit?usp=drive_web&ouid=107560875301636842650).\n",
"\n",
"For this mock data production we will use the following datasets:\n",
"\n",
"- generic crops:\n",
" - harvest area fraction 2000 rest of crops (MAPSPAM): https://www.mapspam.info/data/\n",
" - Yield 2000 rest of crops (MAPSPAM): https://www.mapspam.info/data/\n",
"\n",
"- generic pasture:\n",
" - harvest area fractio 2000 (Earthstat): http://www.earthstat.org/cropland-pasture-area-2000/\n",
" - Yield by country in 2000 (FAO): http://www.fao.org/faostat/en/#data/QL\n",
" \n",
"NOTE: We will need to produce a raster with the same resolution as the harvest area with the yield information by country."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Default crop data:\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading agr harvest area dataset...\n",
"Done harvest area!\n",
"Downloading agr yield dataset...\n",
"Done\n",
"Done yield!\n"
]
}
],
"source": [
"#DOWNLOAD DEFAULT CROP DATA\n",
"url_ag_ha_2010 = 'https://s3.amazonaws.com/mapspam/2010/v2.0/geotiff/spam2010v2r0_global_harv_area.geotiff.zip'\n",
"url__ag_yield_2010 = 'https://s3.amazonaws.com/mapspam/2017/ssa/v2.1/geotiff/spam2017v2r1_ssa_yield.geotiff.zip'\n",
"local_path = '../../datasets/raw/crop_data/default_crop'\n",
"\n",
"if not os.path.isdir(local_path):\n",
" os.mkdir(local_path)\n",
"print('Downloading agr harvest area dataset...')\n",
"r = requests.get(url_ag_ha_2010)\n",
"z = zipfile.ZipFile(io.BytesIO(r.content))\n",
"print(\"Done harvest area!\")\n",
"z.extractall(path=local_path) # extract to folder\n",
"\n",
"print('Downloading agr yield dataset...')\n",
"r = requests.get(url__ag_yield_2010)\n",
"z = zipfile.ZipFile(io.BytesIO(r.content))\n",
"print(\"Done\")\n",
"z.extractall(path=local_path) # extract to folder\n",
"print(\"Done yield!\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"default_ag_ha_2010 = \"../../datasets/raw/crop_data/default_crop/spam2010V2r0_global_H_REST_A.tif\"\n",
"default_ag_yield_2010 = \"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A.tif\""
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99928, right=179.99856, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -1.0, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.083333, 0.0, -180.0,\n",
" 0.0, -0.083333, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(default_ag_ha_2010)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99928, right=179.99856, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -1.0, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.083333, 0.0, -180.0,\n",
" 0.0, -0.083333, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area yield\n",
"a = rasterio.open(default_ag_yield_2010)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Default pasture data:"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading agr harvest area dataset...\n",
"Done harvest area pasture!\n"
]
}
],
"source": [
"#DOWNLOAD DEFAULT PASTURE DATA:\n",
"url_ps_ha_2000 = 'https://s3.us-east-2.amazonaws.com/earthstatdata/CroplandPastureArea2000_Geotiff.zip'\n",
"local_path = '../../datasets/raw/crop_data/default_pasture'\n",
"\n",
"if not os.path.isdir(local_path):\n",
" os.mkdir(local_path)\n",
"print('Downloading agr harvest area dataset...')\n",
"r = requests.get(url_ps_ha_2000)\n",
"z = zipfile.ZipFile(io.BytesIO(r.content))\n",
"print(\"Done harvest area pasture!\")\n",
"z.extractall(path=local_path) # extract to folder\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.402823e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'blockxsize': 128, 'blockysize': 128, 'tiled': True, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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QWWVZOFQnEdZw2gd1IMIac8RGVamrhO93z+Ka7je0TfdO/wH95aJlC5qkiJ2qg1m7v+bH7Sv4d/VWDqVna77DMpdmIUgI6hJJaZmdHl2TMZmMFBaU0KVDPN0jIgm1BtEptB2lrlJMiolBUYMINYdjVExVFoVIKXFJJwKFMncJB0pScapObEY/Xlj5ARd3O40h0SNIK95LUkB7cuyZGISREHMYBmHEqTqYvf9nXl/2GznZ+dhsFkwmI2s37MK5LQfMBk3h292aFVPigkgrSSkJXDpmOBd0OZMuwT1wqy6yyjPId+Qx5qXbKNiXA+VuLrhqAm+Nexw/YwAlriJWZS2nxFXKZ5v+ZtGKTbilxN9mJSoqhMiwYKxGI/1iEvni36VsnbcFSpyaDKEWKHVCsIWQuBAmDO/LlC7DOT357GMemXA84FZdlLpL2J6/BT+jH+tzNvDVln/Zve8gqelZBPhZKSkrp6SwlKDQQCaN6s+DQ68lyBREqCWiSVajKlWkVKuERzpVB6WuEkBiEEYsBit/pM4m3j+OSFsUFsWC1eiHzeCPUTGS78ily/RzKFqXARYDIikAmVPOrrd+5sJvH2Tll8s0Y0FWWt0A2AzEDW5HYXEpxRsztc9NitauDpZqVrfDXamQ61POzY1BaAsSS1yVcigCQsxaahJfWQTaSMEtweHpjExKZUdkFFonUO6uei7vdim16xg8navZQOLg9rx/6TSGRI3ALV04VQeKMGAUJgzCQIEjlzxHLu0DO7MkYwElrlLaBSZjUkwIBIHmIAJNIZgVLbw60BzcNpV+Sp8u8t/li7AZ/Btk4ZS7y9ict56DJRm8tuJXFny8oKqPsDrehlVfA9OGAxBhw+RvYfDArlzWdyQpoZ35J3UJc3fvINzfj70Hs9i7P4P8/bnaELbYCWEWkvq1Y3/aIdonxbBn1R5QBJ0GdiC5XSzDE5JJDorj++3LEQh6REbx7Ec/YA32IzQogHKHg5ioMEJCAxnRPhm3qvLv3v3YbBZu6DeBCYmTMAgjf6T+yjUfvEpCXCTvn3c3Esm1375A2oEs/KwW/rjpZWZuncUFnc+gW2hvip2FjH7varYs2EJIShQvXHIFUztfgsNtp8iZj0kx89Oen/hi01KWLt+MfUt2ZQO3GQjtE8dL/7mSyUmnE3gMJ6jaMqpUOViaSmbZIVZlruPJ777l0IEcTTFkllVtqxIIMZMyMoVtq3ZpnW+EDUwK/ft2ITDIj55RUfSO6sjJCeOJtsVVUebe0EekxGywogiFOWmzWXJgDfcNuBMhFMpdpZS6iom0xlDiKkYIQVrxPgyKgZzyHMY/ezfyQAkEmWmfksDkIX0YmzSIcGsY0355mzW/rNEUX6SVZc+9z+IDy/jo34WUFJdhNBm0UaS72oNl9hhwCQGab95fc9MoETbU9GIodGpK0XsPauJILP2asBg011JZDVFAvviOOGpDoBlJRo+MZR5jzc+oRRvllIPTDfEBWg6rQm205N8tkp49OmAwGFi2YjPunDLtXvmb8LdZKMkrYdjoPjw97koenv8xiz5coMnj1Ys2A8T60yExhpMH9GDG+FfbptIXwWZpvqYHQwZ1597h5zE4ckidCqbEVcw7G2bwzp9zSVu0q2E/UH3K3vdzk6I1kGKP49EgwKwQ1jee3C0ZmrWSqz1oFcfZDGDwNBaHW9vHqWqNINQCB0q0DsUlGXT2QJ44+TIUYaDMVUp2eS7x/rHE+8fzx/6/eWfuHBxON2cO60t8QDhjE0fiUl0UO4vJLs/h+b9/wGIx89yp1/LEkpnM/Wax1sgAc+9IHFtzuOuBi7i6xyXcNf8Vfp3xlyZLciC/PPw04+ImVHSu+4p3MeWT+5g6dAiDonuzOnMjD/3v/6DYpTVmPyN+KRFceMowTmk/kF5hPZsl2uN4xaW6KHeXsjF3HU/9+zmLlm6kfHOW1h7K3ZWuDa9to4iKpHYcKqu0it1SUwYOT3vKd0CcH137deScQf25rPuFJAd0RJUqn23/mGd//YEnp/yHM5OnkFqyl/npC/ho9UK2bN2HxWQkKNAPo8nI1jW7tGtmlUFioHbu1OLDlatBoHQNxWQ0Yl+XCRFWEIJ7bjqXa3tdRudp50B6CROuG8+fc1fCjvyan7FwKwSaCIkOJn9bJgSaK5+RPM/cl/AoTu898Z7Hz1j1nnlp7PMM2vMc56fF/TdEX/jKURNBJs2aL3Zq1jxoeiLEQkz3ODL2HIKschKGdyAt9RDsK9Y6CD8TlLu09lDoqP0aZo/+8N6b2vgrve0qfUbGaA3crND11J68MuV6FqQtJ8o/jPM6nkOoJaKKq6XcXUZGWTqrs1azIHUd38xbRt6+HG0I2VQUnxtcU29v8vgfDT7DWW8Di7JhjQmkPKcEXCrW6EC6dUmia2Is81du4sCBbDhUqlk4ZgU6BhPgZ6V4V47WaEwKScM6kpgUxeKPFlaOTPxNms9QRYt+sBi0xmZWSOnfkW1/ba46XAZtn0ATcT3iKSopw2Qy4nA4sVnMPHfx5VzY8ZIKpV/sLGR30Q7aB3TEZLDwR+pspj7zNENG9WLjpt2UbMiEMIt2b7LLIcaPkaf057K+I5nS/pyKVcQtjZSyIkukEKIim6NEUuYqwWr0OyaT0+XuMmZseo9nv/+J3E0Htd/WqIBF0X57X7xtzOvv9v7m0X70HdoVi8VMbk4BO3amaQqyxKllP7UoDL9wOD+e+zIAf6T+xh0z/4/7p0whuyyfp974ErLKK9tmiFkzUqpTm/IUVM75JAQQFB6I2+3miYun0iM8hetmvsK+udu0Y/2Mmuy+4Zhed0m5W2vrVmNlVlff7+5n1NquXa1bsTUHJoWA3tEUr8lo2LXq6lhqGwl43ThQ6cby6hTvuQJM2oRzQ7+vcpwq/fhu8bL30xNYtXY7hSVl2Ddna5OaueWE9I5l9OCezDjlcYLNoewt2snDi9/kj0VrKErPp8+IboSGBbFu/U7ydmRVWLz10py+RMWTntjfiLVDGKFBAZx/0iCu6H4eIZZQvt7xHbllBTz3/vdapySBUAuB7cMo2p2jKfGDpdqPW71DMXqGyR63Eznl2oOUa9calS9mA0Raad8tkT3b0ohOiuTR8y7Arar0jexJr7B+WAzWKoeUOIuQSIRQ+GTrJ9wx/U0AOgztxIHMXMrTCrjhujO4Y8C1fLrlK6Z/8A1RsWG8fMk1nNluSqtYzeuWbn7b/zOzti2kT1Qi8YHRfLv1X3JLSnE6XKxZv4OB/bvy7qS76RTU9ajJYXeXc82c+/n2m3+0/EZuqbk1As3ae4AYP8aeNZRof392Hsxk5Q8rNWMn2KyNFO0uQrpF42+z4nS6mDCsD8+Pvp8SZxGpxfspdpZQ7i5n5qZ5/P3vegoLSwgLCyL33/2Vgvi2bYPQ2k6+o1KBWAyHGwre40yKFi5Z5NBcFh7rlUAT/iF+lGzMrHT/1UZNFru/qWoYs1rNaGoM9SnC2o5JCUFRBOqm3EZesBq+Mlf/LmFWzQNQUydb/diGfmb2GATejsZ3v7aq9PsO6CMXL12oWWXuUl5Z8yazlq/CajXzy39eJtQSWWGlOVQHc9P+4O5Z/weA2WIiMNAP/wAb896fd7hvsbloyJDS2wCCzfQ5uRfl5Q5ycwvJ2nQQJSEAdU+hZq27PMrdZgA/EyLMgvSuQKy+6tDkUfqq1DrCAI+SzSrD2i6U8j152gPsUCsjHqJthCWG8/NNT3HtNy+yeek2YrrEcsUpo7i+1xUVkRv5jlzGzLiW8nIHHTrG43arLFi8jpAgfzq0i2P1dys1mQJMTLj0JB4ZfhkRtkhMwkScf1Lz3+MmokqVPHs2yzKXct4rTyF3FVZ2iBKwGTB0COGt667j0i5XNHtCOSkl+4p3MX3pO3zx8Z/aPSt1acN530lAi4EPX3yQCzpeTLm7jOdXv8Izb36tDeGLHBX7EOSZaCx04Nc9ktUPfojdXc6Hm76mc2giDreTaS+8q40cG+LmqN52fQ2Lmgg2a/LX5wapTm3KvLq79FhO3iqe39rPCGUuwockkZtfqLWRmlb4Ngc1eQIai++9NCuYu4bjSCvEGBdIx+RYgoL9OatXXx4a9HDbVPo1Lc5yqS5c0olTdZBTnoUilCpJwdyqi3K1HAFklB5g9t4/ufvBN2pvqMeyoXnjkgNNWkO3GjAkBuF2uLSUxt54YkVofkarQZvgg8NdAACRNoLbh1OQXUhIZBDldid2h4NAfz+klBRlF4HZQGhYIHeeNZlTk8cS6xdPsauIBxe+zs/zVvDMZZeQHJTA6kObOKfTJLqH9qHYWcihsoNEWqPxMwagCIVDZem8tHoGb7z1A+TZsfaJpl1iNFtX7sQY7sfaRz6kY1DKMbqRjSPPns0/B+bx685lLN24k6Bgfy4cMJixiSPpFJTS4ECBhuJUHewq3MbyQyt56c9f2Pbv9spJPe98j0sFm5HEoR24evxo/tvzahZlLOSWz94hY9V+rY1YDFUmzyl3a5Ziu0CiIkLIKywhpVMCZrNJ64hdnn2NtbgZjgSToo1OipyViss7QVnm0rZ5nyVFVLo6vFE61UcBXmMImtcgM3giZOpzlXh/bq+cRkXrjE2KNjJWpXY/y931j2AaK58itHM3x9c2+HQC/iZGnD+UX857g1BLxPGj9L1oiyPclLiK8DMGVISwpRbv4fQP7mT/gSzKd+dqE4++E6sNpS6/5pHcMm9j84a1Rdk06y3en9jocLJyC3AdKqH78BS6t49n9ebd7J6/vXLVoneoHWWjfbdEgoL9WbdyO5S4COsUSe7adIjxw+hvwXWgiP9cdRqXdJ/A8JiTMPuE+blUF3Z3Gakle/lr/3yu6n4FJsVMgSMXi2Ll99Tf6BjcgRhbLCWuYoLNIYSYw9lWsInf987jo38WEBjkx1l9+9InqhujY8e0Gj9+XahSe4CPxoK/EmcRq7NX8si8T1i6dBPsKdQeSK8f1+aZ5C51QftAZt3/KKcknIZBGLnkt2n88O6f2kNsFAR0jgAgPiaCcruDvIJiCjcf0o4PMhPVIYrQ0EC2LdpW2Zk4ahjmNxdeZQVV3QlGReuQgi3aCMPtedYa45umEfs35HxxfpoMB0qafh/MihZtk1pc+dyZDdqzV1yDAealvntvMxweJSSo1AeeUXm3yb0xGg1s+GN95YRwbdcLtRDfO5HzRw/i3z37eHr8lYxPmNQ2V+TWhSIUFKEQbA6r2OZSXVz929NsX7ZTG6LZPTP85hosDV/CrRhjA3BlFGtWizfGtqYfV6JZPA5Va/yN9SN6d/V2tiUuCLXQo1s73jl3GqGWMD7a/CWzFq/i2x8XEBIVrO3n9b9CxcKt9EM57Nm43zNhZic3K1+zDvPsuITgm/89waSkM2tUcEbFiFEJpFNQNzr17IZRMVLqKiHfnkekLYbL33xFG+paDVqHE2Lhyksm8NSIu7m1z01M63sr5e4y8h25FWsO2gJHa3W3KlXuW/w07//4t6ZsvBOYvn7sMs/krUFw3dRTmZh4RsUIo1dULD9ICQmBIMDPaqG4tByjyYBVmEkKjGLy6eN47/f55O7MQkrJI5MuZFre+2Qu3ae59xyOymt5aUjESUNw+xhOFoP2bEkqwgYtYX7YM8s0a1lKcHh29jNWWRFbgddC9c4lqLJ5RieqrJwfa6r7xGtYZZZVjkIknu9cz0nru6avwg8wccG1pzK121iCzUG8tPIbfv3lXy46dyx3DbyUk164teZ7BxBiZsjpA5k28gwGRw0mwhqtrRgeVv+XbrOWfnWcqoPtBZsxCiPX/vQcO3en43arFK5I13ao3vgNAgyClAk9uGLkKB7/chZ2h4PzTx3BfYOvYGXWar7evIwFS9bjdLmQpS6tESii0lduEJWLMRyN6ADCrVqMLlTE74aGB+FntZCenlVZptDf6P1y2rnLfR40mwFzUjCOtEKI9tN8v0WelbwulYcfuYL7B9zbJLeFQ3Uw+oPLWffzWu27WQyVw/o4P/4z9WTeHPskLunEzxhQUZIvzBLZ6Gu1NVSpUuwsPCx5lipVnlrxDE/P/AH2F2mRJ1Jqv3WBQ/tdvJEpZS46ju3K8+dezbj4UxBCYW32Sm758XXWb9xFSEgAKZ2TGNQukfiAcD5evIj8/GJ+vOEpMkuzOOP+e7XJz2ibpoii/LSJ/EJH87t1oNJd42/Uvou/UZt/srtrds14n4OGWvFeS/doR+rUR3WLuyaaOlnse04BiWO78N5/bmdU7LgKQyS7PINCRyGz9/7Jx/8uZOOGXcTERmAyGRnYqxN9oxP4Ye0a2sVGMn34f2kf1KXWyLMTIuGaN1tgkDkUVbrJs+eQZ89l0H1XazsUOyv8qkqnYCJCgjh79ECeHHYPX+78ijKXnctSLsFisGFUjFqvKSV2Twpkp3SSVryP1OJUpn0zg91LdlZGQJS7tT+vb7M2qg/9LIaqw3Jvg/IOJZMDOe3kQeQXl7Ji1VbcRXYtf493NZ/ZoIXrGZXKCB9PB/H7e28yMmZMvbVJayLPns3IN69m919btRA7g6Jd16RAqYueU/ox76r38DcGIpH8uHcWN3z4NgvvfIPOwd0bfb22gt1djsVgrTUf1PLMJZz8/N04N2aBIhApIQwZ2J2lf64hskMkfn5W9q3cA2FWOnSKZ3DPTrw9/smKBF1FzgLW5azhx51z2ZOfR/+YBMpcdt76cS6laQXab1zsPHyiMd5fa3t2d92ugLoIMtXvv/YqRa8d0RBfvK/N0RyqxjsXcLQCM5pKgj/Dxvejc1QEihDsKyjApCj4mUxc1vNkip3FfLZxIfMWraNDcgyvnXsjI2PGVkk1Pe/AHP776aukr9jL/x6+jjM7TCLOL0GLOfBk1XWpToRQ6g0zPiGUfk04VQfZ5YfwMwawu3AHBY4Cpr7zPwqKSoiJCOWdy24mKSCJLsHdG6UcXaqL5ZmL+Wb7n+zOy2PVuh3kbDpYd4rYmqjNaggyEdglEkVRKNiVrS3acElt1aBBAQU6De/M/gNZOHbkVobQQWXsb7dQundJ5p9r3mtQ1kRftuVv5MLPHmXbH5u0h8ypVo4wnCrE+xMUF8IdZ09kzs5tLJ63FvYW4dcvhr/veoneYQOOi9KKLtXJgdJUMkozeGXV19zcbwpDokbU2lYKHLncMHc6y9Zu45xRA3lw8DRMiplhb16K0WTEXu7gUHYeJVnF/P3kK/QLH1ixWtbuLuevtD9QpZsLXn1WGy0kBNCpfRw7/9is3f8uIdo8gTeG3xOBEzQwni6dElj5xbKm+/PbBWrrLeryV3upbrk2BN8otvqOU0SlAVPTZxZPh1vfCtpjgUlhxH9G8H+THyDJv0NFu69eLlJKSam7BAFYFCvC4552qU4Olqbx7Ip3+b+3f4YiJzfcfz7Th90L0OR5shNW6Xvx1ty8Zd6TbE/N4N1z7sAojLy6+nOu7T2FwVHDm3TeAkcu9yx8hnCbjSV79rFs1vLafXBNfRgNQvOrWwyY4oNwbs8FKQkfmMilJw/nlVe+1VwJif7aQ5BeUuUBu+6+8xiR0JNIawS9w/sSYglv0GKkMlcpnZ46k9yl+2uW2xt+Vq3jihrRjvX3flllrqWtUuoqYW/RTs5+70FSdx4gMiGcO86czG19bqu1U9MKkbvJsR/iy+3fcl6ns0krSWXMPTdXZHM0dw1n/5O/sKtwOz1C+2BWLLyz6W0+WDyfV8+6kTNefZjSNRmVOV68ife8eFekeu+/1z/uG0GTGKC1hfpcEQahrfT15pdpiCvGG6HjPb4xLqX6wkLrQ/HIa1Yat+CyuSe3BYQOSeTtK25kYtIZVYIkGordXc5dCx/n/bd/rmIwGnqEs3X6FyT4t2uyeG02tfKRUuIqJs+ezdc7vufPXVt4fNRl9DtlEGaDBVWqvD2u5xEtIgo2h/H8qAfYU7QTs+E3lPMV/v19lWYx+TZsgRbjLKl9NGDw+M59l5ebFQg0k9wvmX2bUnHuyvNY+wbyC4p55bNftYfAZoADpdrDF2apXKKvSmY8O4sZ6rfafuFWhk7sz1sTp9EusFPFkNGLKlVKnIVYjX5IVKIjQ8m1pmtzBnuLqsrriTKo4o6SksyDuWzIXc+QqOGtJpVsU/Ez+hPvn0iXLomkrtpLeZSTkxLqNhCE0OZ5zvzsLtZv3MWoh4cSbYth6IR+LF2ygTMvHsOUlMH4Gf3pFz4YIQQO1cGjX35N0coDjP/lv5VKtHqoo9etUeLymaOS4PRpa96XGaU1pymo7hpRpRYGWn1f74RmdcvcM7qomNB0SW0SufoIwddV6StXQzuV2rAoWjqD7Qdr/jzMUvsCqKZQkzzBZiZcPJrPJv2v0aNoX/638nnef/67qr+HSWH86H6UOIubfN76OO6UvpSSA6X7sbvtbMrdSJeQLlzb4ypu7e1fJS+MN/rnSAkyh9AnXOtQYwNWckrnFJ795mfs6zMrLaIATwRDXY05wOSJdCitHMIKLXxv34b9WkIqVWKNDaL8UBHu9CJtGbvJAHbP6CLWT2vwYZ5C5vl2LWQVtIctq4ylnyym/+xVmOMCGT+qH+E2GwNjO3J1t2swKkZUJL/v/4W7vvmQ/fO2aw/33iKI8dOsQYdbWwFc6KjM/Ck9D36RA3YXcsrNNzHx8rFc33ci/SL6E26NbnM1ETJK03hnw4cIofDA0Kns35fBjp1pDH/oBjY8+xkdg7rWau0rQmFM984YjQYySg/x2eafeHvSHbhOc9EhqDNWgx+FznxcqoNQcwQ7CjZjs5gpgqpWs0FUKn1JVeUgq/3XLlypaGuyvsOtRHeN5dCa1Erfv6TmiJS6on68nZFXIZZUU/hmhaA+sRTuz9XajBBaWwk21270mBRthS9UrtCtCbuqVcuqyQ3VJYQbzjuFt5/+RnvvOwptjJUfbK7Mf1PtuKTxKfzfpXeSGJB4ROnepZTM2byt6m9qMUCUjTfG301iQHtAczGWu8uaNRz6uHDvqFKt8N9HWKMpcRXhbww8LLXA0SS9ZB+rs1ZxSsJpTPz6JpYu2aAlU/LGaTcki58v3ogJg9Am6YLM2sNpVLRIGrMCkTasIX7ERobidLmxWc3sWL4LgIB2oRSn5XtcCk7tgfIWcfEuDPE8tKJ7KNnPzcXPGMDq7GWMuPUaTYk71cq6pN4kUorAr080pVuzKjIIRg9vx8gB3di0O42tv27QZG4fRGJCFEaDwszLH9LcGJ4FdK0ZVaq8vfFt3v37L3b8tYXIQYmsvuczAk3BvLTmVaY/8gF0DGLhI68xMHJYlWOllDilkxJnIfMP/MNFzz/D8NF96BARxqb9B7h19EQOFGfx3dpVrFq4CQyC7v06sXn+Zu1e2t1VLctmdkmYe0fisDu1QjoNobbrezt672vfzsl7XIRN6wy87k7voqna5gwUAUkBsK+oYd85MQDSiiv3TQzg4/vu4sW/f2b9rFUN+nq1yRHQL6b2XDzhVroO78Kr59xAoaOI+amreGDQbfgbA9lfvJtOwd0afKmnVz7DEw99oL0RcOtDU5nW7zrCrFFV3EVNKSR1XPn0fW+Ad7KkwJELCEyKGT+jfwtIqvnnvt/zLVPan0d2+SFmbvuGR9//Uhtml9Vj5ftSfXJX8bEmfYfLntCypHEp3HLqqUT5h9EzrAcD7r5Cy8roDbV0Sa2D8MZDB1u0h6XcXXnOcCsfT7+PCYmnYTP4kfz4JAo2H8JTuqxq4qvq8nmX0nszJHo/aheodTKFTggx89Q9V3FH32mNv7HHGCklX+z8jKunvwC55XQ8uRtrb/uWn/d9z82fvFsxx5EwtjNPnnsxU9qfh9lgocRZxKzd33LfF5+Rl56nKbx8R+Vv5lWOblmzT7suBdtcoYyxfpoSbkzAgbe9mX3y8tTUMQmquqO87+P8NaOhIRO/ASbtvjXk63rzBAlB1LBkHjr3XL7bupp/PpxfGR5b11xD9bmFxnSw3k6tzAVWA50HdeSC4YP5e+cO/pr6foMV9Oa8dQy4aiqYFO69cyq397uJkGaaC2vTPn2X6sShao3UarBR7i7FYrChoFQMr1vDpKHFYOW8DlNRpZt4/2Tu6Hsb4hrBIy9/UvPkbm2hbNUf8JoeeJuxoiPZv2gnP7ePZfu2/Vw0dgj9hnfX8pybtfDKigygTlVTwPmOyofXG/GTU87NH87g51uT6BycQlhwAAUlaVWX83sfIm/8tTfbot1d85LyvUWViaYirBg8xbFbe1SPU3Xw4/YVpAzpxE3jJnBq0imUu0v5ffcKclNzKnziafN38nbCHM5qfw6lrmLuWvgkH338u9bhVsfrmqlrArO2j5pD4YdZNHdOibPmdB7Vqckt4uvbr0kkSeV6Eu/+DlWz3OvCV9mWurQEbL6jAd/PzYrWJku1QidhQ5Po1bMDWZn53Pr461U7M1c962aqb2/MbQ61VLqgQi04nC5WHUzjQHpWo6zyDkFdCOwRTd8+nXlo0H3HLEnhESl9IcReoAhwAy4p5UAhRBjwFdAO2AtcIKXME9rT/iowCSgFrpBSrq5XQMVUsdpTKyHXdD/a0UabM9BkVYSBmIBwUgZ3YsfedNSccm1xl+/D1FjrwptZ07cTsbtZ8MNSBp7Sh5c//5VLzhrDGtM6bd8YP63hFzurRll4r+kT7VG08gBj7r9Fe+j2FWnrD7x+3xg/zb20Pb8yB4zFoMlh9iw48j5gqtTSPQeYuOzSCTw45HruXfAqiYFto8C62WBh5sQXEYiK0MxSVzFmg4Gxk4dQWFBMVHgIwRYLF3cfz7VzHmD2/FWU7sppmEKFpkWw+K4Cbww2g+bay60hhXFN5/JO3npfQ6VCr46vm8e7X3XPQWPat5RaJa50nxXNvsZ4p2CkW2ruKQm5mzOYv/6AZ5RZTT5fWQSHL/6qS676nkvvArUyN2SU8tLD1xLrF8Pwr68hrWQv7QI71ft1vYv85t/5Gp2Cux7TrLTNYemPlVJm+7y/D5grpXxGCHGf5/29wESgs+dvCPC253+9tHbrsCYUoTAx6TQWdd/M3rRDTLzgJNZs3s2+xTs1l4u3EEJD3Wu1PXgA2eWs/GMtADPfmQ1WI2Gdo8hdna5ZJSVOUMG/VxS9enZk6cwlVZeXe/HmKjEpWlEHFa3DyLNrbiqB1ikAlDoxdAtj1u0P0iGoA3a3nb/2z6dzaHtSQlMIt0QQbA7j99RfmP3PSgbFteNQWTpWo1+zDWGPFtUfQJfq4olhd+FvCsShOjhUegCHaufWOa+x8NOFjU8p7HX5NCYxWolLUzRRtsoFeg0hzFqZvrnql6p5/5oidWqjJiu6PneUv7Gq7N5drQatjdqsFIe7Dy8kIkBuyau6raFuKk/IMyWuytDWuqjPIEsM0J4Vz4LIxIAEJNpI6LOt3/DQoPvrFcmlOrEYrHQL7d2w79CMHJFP32PpD/RV+kKIbcAYKeVBIUQs8I+UMkUI8a7n9RfV96vt/M0Vp9+SqFJld+E2ps17jSHxSfy1fRvLPltyWIMGGm/910a4laS+yezflKpF1JS6tGyb/eJ4+LxzySkr4IVvf8W+IavqA2ozaGkDDpRWKqZwq2bZl7qgY5AWGx1g4twLx1DidLJxy16y8wp54uILubL7FRQ7C4i0xqIIhYzSNHo//R+KNhzSJrIDzFx91UReOml6k+KaWwpVqiw8+DcL0pYT6RfKtLf+T0ujkd/IxXi+/m9oWuqBEHNlta26/N/hVk1x2gyV1c5qkqU+agvdbAoWA8bOobi25VamdPCuJvda5uFWlGg/1M25ldf3hpk2xdXl82wFDIileGdO4xdR+hJi1ow2jwvK1CuS9Kdns+jgAtZmbubWvjfhV0/WViklKupRLd5zNH36EvhTCCGBd6WUM4BoH0WeAUR7XscDqT7Hpnm2VVH6QojrgOsAEpMSj1C8lkcRCp2Cu/Hw8EsZ/9xdJMdHHf7weP3rqtTcKDWVS/MteuElyOOC8V2UA1DsZP+eg5o7ySgIGhjP4xdewOntJ+JvDCDUEsGV3S/m3gUvM+v13yrPV+bWKitZlMrcMTnllefdVQhhFjr0TmbW5/M0v6afkU6ju3Bup7PwNwbg73G/qVJlfc46rpw4msJxdorsdvZn5ZJVWsq+op1tKl2DIhTi/OMoc9m5+6NPYXdh/QcZhKbYvFleoVpHXy0CpqFVmwqdFdFR5JZra0KCzVq7ySjVLPhQC0qMn+ZSrGkkYfakD7a76x9pNGc+HCnx97NS4FX4QeZK/713rijEjJrr0+bqmwup95qVL4u3ZWvV5qpTXwfoO0/h8/yJ7qGsfuh9Ak3BBJoDGRbXv6L91yqOZ17LwNGv1lYbR6r0R0op04UQUcAcIcRW3w+llNLTITQYT8cxAzRL/wjlazX0DR9AZFgwu/7ZVvMO3obtm51RVvu8umVZ6Kw5ntrhhoMlmpUnITE+knWZe3n+5xuQEh44+xzyygvpGhFN2JBEcvOLNOVR5tKGwd6HzjdXt5+xwje6e/72ymIkZS52LtzOd+N/YkzCCCKtUewt2osq3UxInMyExMmAVn7RKIzsKtpOpDWatkbn4O48PfxxLkg5ndHP3I5zQ1bNO4ZbK2u81ubjl2gdglvLltogxZrgz02XT2bBlp1s3LoHIQQTp47mwm4j6BTckWu/fYFNv67X9i1woFZ339gMYDZw0gXDeXTkf4j1i2NT7ibOu/uBqnIKIMrGyMmDyMstZNPP6xqmdA2CPlP6YzabSE/PoqikjLiYcLYt2wmgpf1eup+C5WmV1/EGAHi/vyphZwM61KbiCTk+rJO1ehRwbSHVNf0+IWYW3P9KRYhmgb2AUlcpDtVx2CjWmwZeEQZEnf6yY8MRKX0pZbrnf6YQ4ntgMHBICBHr497J9OyeDvia7gmebScEijBo9VkbWpXnSLo7SWU+Hrdk0w9r2cRa7TN/I8WTSpjSaTL3L3iTO86cxPmdz+ZASTprszYy7Zm3NStUoXKZf7hFO2duuWfdgQAHEGQiuGs0XTol8M26Fbz3z9/EJUQytfsQToofXUUk7+KSHqF9j+CLtTx9wwdx85STeXnTlzUrA2+H6XWLWA0QYObxaZfy6YJF7Fy5u+E5bkD7DRMD+P2RZxkdO56iIQXc9Pfj/L1sA3cPnkqifxLf7PyeTf9srnSTeFft+uTI6Xlabz44/166h/YBKVmWuZgFaasOV3RdQlj52Aw6BqXwn9/uZlND22GElU7x0cx654+K9QbbxMEKCz43OL8ypQSe++Lr269pstUXm0EbfULjRh81zadUp545maBB8RTuyKo0upIC+Pz+++kfMcRzSpUYv2h6hPWpovAdbjtl7lIsigVFGFpFCVE4AqUvhPAHFCllkef1BGA68BNwOfCM5/+PnkN+Am4WQnyJNoFbUJc//3jDoZaTcaiRNTjN3upDdexjUjQfo9cHX3104MWjAMwdQ5na5Twu+elRlnyxhBWDkrj6nivYVbCTmauWEJAcSrG1EPxNWo3VVWmauynUo/gdKsT6cfKlJ3FRj+FMSJzAmZ/fQWCQlefOvooBkYManN+nteOW7sO+h5SSFWnptSseCQSZGHzGQDIyctn/z3bwN3Jp1wu5ode1fLjlY+596K2GV2NSBF/f9yAjYsYghCDIHMKzo6axousKuof0xGL0Y0TcYIacvoZlP62otNp9f3t/I09NuoxeYf0BWJgxjwlP3V2jVd25QzztAjvhVl2UOBrYMQngUBmz3vit8rresGKBprBTSzS3oTc9ePXOxjfaxvO9q9xj7/4NNZS9z0JD+od6RjKKUhn5pvQIY9mDb9MzrJ/PpQSPLv6YsnIH7026lzBrBAKBIhT8jQGtRtl7ORJLPxr43jNhYQQ+l1L+LoRYAXwthLga2Adc4Nl/Nlq45k60kM0rj+DabY4DJamQXi2fRn2+xIYoBt99ajuXJxwOID46nBBzGFu27gW7m0OZeUyeeSuKIlj54ypGXTSC3jHRRPmHMGvVKm0xElTEnwf1j2P1fR8R65eIIhTcqou3z5pG15Cex3QF9NHEqTp4d+MM3vvnb0JCAwkM9KNjaCgmRSHQYmPRX1UjjXud05+bTzqVP3evI6uklDdPmUZyYEfs7nKGGC5l919b6fjQeSx76G1u6Hk97V9L4NHZX7Dlj431jvy6ntaTcfGnVEkhEuefxFk+tYgHRg5j7iWD+X3UL9z4yZtk/ruvqsIscfHw759x8jUTUYTCkKjhjBzdl0WZ/x7mgtqxYBvJhyZhtVrIWbq/cblyfOu/hlq093Z3ZXK4hoxyveerq1Otj+odRm1rYuo7VgBx/oSHBJHvl0PXcd358pLHSAnpWbG7S3Wy8OA85s5fDVvy6LtqK7edO4FHBt+PUZhaZeRhm1uR21bZmLuGQVdeXOkLh9oboLc4S1PKPNaG52EKG5bE+ns/p8fTUzX/qsXAiIuHc16PgdiMFj5es4h/v1hSGVJa7eE57YZT+PaM144LS7423KqL/m9cwPbZG+vfWRF8+NrDTO30H6Byom5u+h9YDBZSglOY+uNDLJm5mMhBifx80//oFNwVkzCxLHMJV378POnzd9b8OxsE7750H5elVNpH5a5SDpalI6UkyhaDvzGwimIpc5XS56ULSP17e9VzhVuZ/dwLjIk7BSEETtXBHQse5dclazm4QEvdodWHVZqWk9+rLE0KdApm7Oh+zPtmsbY+oLaJ6mhbzYvZ6qM2Y6k5It+82Ax0GNWFWVc9SYegFEqchQSagg+z2jfkruazLT9wUuJAPlw/h7fGP0CoJbJKJ90StOkVuccLSQHtIdIKaSX17tthfFd2b9qvpcZtLjwPQ+7KNBLuOR125Fd8lJdbxPM//8SBVfur+pq9BkG0jT6junPBwMGc1WESUqpwHCt9g2IkMSma7WysLA1YHUXQ48w+3HXymZzV7pyKzUIIpJTYDFaMiolIWyy/XfA2vwz+gWf++o6ht1xJz5O688aUWxgWPZKnzz/I5Sue1Fx0LhUCTYR1i6GwqBRXVgnfb1tJrH80B4ozKHaW8cQ3syjYmKFdLDmQKROGcUn3scT5x+JUXWzK3ULq/oxKf74QmiIucjDpvrtZ+NI79I8Ygkkxc9fAa7i5bxkjS26qzDXTkHUDNoNmudc2Et2Sx7wtf9dfOauwiaGTtSn26hlERQ3bvFgMhA9IIGf9gco275uwzmLg6ysfp2tILwDMlogaL5non0yUXwgnxY6tUv6yNaMr/WOEiooSbEFNK6kcbvpObPmw+59tmk+0OS0XL05VW1nrxe5m849ra97XpICACy4ez9vjp2OrJ/74eMGtukhL9cQfOGp2SQy5ZBh/TH0HgzBQ5CzQ5mx9UlVPXzyTnlFRDI4ajlkxc06HCzjzmil8NeYLbv7gPQ6WZLBUXUzn4E4QaIbMMrqd3puHJ17AhITTOFCayukz7uVQdj7XfvQqh5bvO1zJbsnj+y2z+V75rbJNVY+nj/fTrGm3hOwyHl/8Cd+c0RerwUZOeTaTXruf4o2ZlYq5IZE63jbrXZ1dPQKnPhdN9fM0F9VlF6Kqy8k7ck0J4fX/Xsv4xDEMfPpqDAZFK1i09gA4JR0ndOedi26nW0j9C6dSS/bx0LMfYLzfwG19bmve73OU0JX+MSLEHMZVZ4zl/e3fa4o3wHR4VkUvDrWyyHVTqH7OpnQeZoWup/YkNi6CV8Y80KrTXzQ36aX72bZ6l4+1TNX7pwhuGz5Zm9OQbm11pdlW8bEQgqv7jqVfRN8q5zUqJi7ufClrT99BcmASfcMHIZF89NDdXPHEcxzIyKF7aDesBhuhlnASk6JY/OHCyhN4UyfEeGriOn2iWbxpATyhtpaekdg3Z3uK6oiKPEjPnXQDVoMma5/wgVw4figzln9b/02pqQ35Km1vLiavTEdisDRH+4XD0y5EWLnvlgu4JOUcOgV3Y1nmIv6+6yW6hfSi2FVIwv2nI3cVcvMppzIselSDcujsLtzTOip4NYK2leS8jfPcyIeZcM04zWXgrdnb3NT0gHjfGxphpQeYmDp0MPcPvZDQWoa2xytxfon0G9VDU7JwmMJPHNOZsfHjMSlmLAYrkbbYw0ZA53e8iA5BKYAW0jdr91c8sfxpXljzEpM7jKZP+ECE0CI8JiVNZvL5owgLDmDWzl8ochViNdi4sNdgzccu0BbiJfhrMuXWsOiq3F3ZQZkV7KV2cKnEn9RJO9YtwajgbwpAldqxBmFgcGxX7XhR7a86spbtXkqr1YduTNO2GCpDXKsfK9AWcTUHdhcZxYWEWSNIL9lHx6DOdA/tjdlgwak6GDOqH0TbmPbo68xN+73W06hSxa262JK3nmvff50u47txfc//No+MxwB9IvcYs7doJ93uvvDwSlTNiXciuKbl877JvmqaYPN2GiaFq+84m1dPerJJxdXbOksy5pNZlsWH6//mzy8W0H5QBwIC/RjRtSNPDb8fm8GfImcBufZsdhRsZ1vublLCOrDkwBoMQsGluvh9w2ZePuMGhkaNosRVxK/7fiavvJDre95w2PU25q7hlVUzeW3sYxiFCYnEodqZvuwFvvp7KVl7shgyvi/LFqyHg6VEDkokJDiAHat3a+6b2n7LTsHaYrHscm1bUgBTzxvL3YMuo9hZwqebf+b95747PJ231x/e0NxAXvcJNM6YsdWyMMogtA7PZ+4g5fRebFuxs2mTv14UQcrEnmzbncbp4wczfcS1FflvXKqTsZ9excovlhE8OIH9j/5WUQPCN0Ps+txVfLTxO/7vl39wrM/ivNsn8+lpLzddpqOAPpHbikgK6MBj117IY9M/bPhCrcagiMpEXjU9fNVL5VXHZxFLelGRliOk+aVs9QyPOQmA0XEn8XL0Wzww8G4MwoBBMbItfyOvr/mcj775S1NA3noJNYQKzu2zmGHRowkwBXFhp0tqvV7PsH68O74X5Wo5dncZZoMVP2MADw2+g0eHGMixZ5Ho3579Z+1mQ856RseNpcRZRMdrJ2vWv0k5PIW3RDMufBX3/mK+fOlnvjT/WnuKA1//fEOpHmdf04izpo6pzF2ZqVWgTWg7PCGevh2BgG2+0VSNdfn4zDNsW7ebbx5+lIlJZ1SJQlOEAYdDu4fFJWW4pBMzFlyqi4OlqRQ6C7C77Zz91kNkLdmnfZ8OQVzcfVwjBGl5dKV/jFGEwl397mDltan88uPiqtV/mgNveuOa8r344rX4a1oJqQi6TurFy2Ontfk6t0fKr/t+5YUXvmZGt38wGBRCgwLYvWqP5lOvTnWFZjZQ5ChrcB0Bg2JEqHCgNI1AUxAxfgkEm0OByhXNVoOV9OJDWBQLpaK4MuLGV7F7VwKXuQ/Py+T9vCFrQI60XZqUyspxlmqhoL6RPUJokUveFeC+1zUrWuGf7LLDffQNxfv9PYsdh47ry9i48YeFHbuki44J0Zz15FWc1m4sFoONnPJM7pj/DH8uWUtCXCTpB7MJDQpg4E0TGJfclcntTyXOr23lCNOVfgtgUIx8PulFCk7O48317/PM619rdW6LGlg1qCa8OsVr5VfPdV6d6grKILS/hADeuvUGLup8ccWE34lIsbOQmds/5/bX34NiJ4UrtIwheY05SYiZHhHtcaj2Bi9c8zMGVFn8U50Yv4QK99CM1S/XmjHS0CEE975CbTTpprItNNRC9rp4GprszOtS9K0X4a3Y5k3eV33BlNcFKaUWwZRTfvj1HGrdNXMbgveUDhXCrSz9dhnXhDzE5xNfqdIZG4SBt8Y/Qog5DJfqYm32Cp7891PO6TqI9PwCRrXvQOiwQCYmn0ynoG5tNpJN9+m3MKpUefDfx3nl+a8rw9+aglfJ11YMuvrDXr2QhyLoPaU/H5//QEVs8olKsbOQfi9dRNo/O5olnXDkgAR69OxA14gIru990WFKfVXWUh5d/DFR/v68MPpegkwh9S7dL3YWEnv/abg2Zte8g79RU6TeOghQ+V2ONBTYt4355PepEufuiSKqMXTTV+ELoeVygoanpmgIviNZ32v5GbV7E2Rm01Mz6RDUpeIQKSVu6cKomHC47fy49zsue+Ullj/+LpHWKKJscY2uVdtSHFc1co9H8u05xN4zkbiYcAb27sxPn/9Ts/ugNmp6iA2isrpVAwgenMDuR346oUIza+O51S/w6IMzmn+NBECXEP599E3aB3Yi0BSMKt10/t8ZZCzcrf2OCQF06JbIFWNG0zOiC4OjhhBoCia9dD8rM1cyKnYUNqM//7f5Ix585WNNqfsq29oKs3jLZjbGTx9urb8dejOx+kb31CZDsKcWQJHDM1FrgCAzljA/7FuyNYXc2BoF9eGVSQgtismTvz8sJZqRA7vz+aSXa11dvqNgMy+t+pg3xjzV5oIZ9IncVk6IJZzX/ns1z//yM59OfIErlHv4/tXZDT9B9efYG3kR6AnVc7gx94qkT8+OxEeEUu5ysT89i83zN2sdw6EykhKisBr8ajr7Ccfe/Kzal/nDkXUG2/MZdtVlEGVjwulDubzXGDL2ZVaeN7WY3albeOTPLZ4Mp1aC24dTsDNLi8KJ99f2zSyrtIx9R3a+cfLe1aiKJxIm3Epc+2gOrE1tWCGRhmQC9Y0O884jVFf4JkVri3l2TbGbFK2EY5EDMkqxp5doBXoO1VDh60hQhBYdFGSudJ3mammvnU4Xg+Pa1bm6vH1gZ14Y/XCbU/j1oVv6rYQyVym3z3+MdsHhXNhlCj3unAqpPgnaagvJsxmrWvMea/GtO29iXMIYssoyWZS+jE6h7apEK7hUJ+ml+1GlysiXbiD3YB7pr/5OmCXyqH/X1kxOeSa9n72Y3H/3H5sLNtTV4ulwLL2jsJc7NAu83F27YjYp+PeKoiS/VFPw5S6+e/lZxsWfwtsb3uH+B94+OutE6sM74qg+X+ANzzySginV8TdWdECh/ePJW3dAu2eeWtMpE3qw7MaZx02iQF/qsvTbhoPqBMBm9OO1MdP5ad06Vmat5NuHHoX2gZU7eIfvvkiqKnyLgZseuICdr3zPFSlXkRzQkYGRw7i97+2cnnx2lWGsUTGRHNCR9oGduXfKmZBazBfbvz6q37E1U+TI57f9P9Px0XOOncKHho8apPZn35XHwP4pDDi5N8TWMTJzqpSsztAUvt0NZW5mbpqLSTHz357XkTimc+MW69VEbQu56sKlaj58m8d6NimaQWNqZlUk0BS8QyuanrczS3vtHQ3ZDFw/9uRaFb5LdVYsYjve0JV+K6LcXYqUkv++9zY9w3uycPob0Dm4cgfvZJkv3kpABkHUwESeHv4w8f7JjYosGBLTHwJMfLBoPg61mX2qbYByVyn9XrqYc264C/u6zPoPaEmKnWzdvp9fLnqV3+5/WvNT10VOecXq71n/N4eZOz7BbLCw5o4vefnZ2yElBAJMhA5NJGBAbM3nqK1zEKLxri6Jpnx9yyQKNOPFd3LY2wlUv3RDm7Wkat6dbJ/IILPC1GtO49oe19V4qCpVhFDazKRtYzk+v1UbJdgcxoIrP+LzG+/h0u8ep3f4AOY8+LyWp8eL74Phdfl4oifyi0pwy8Yv+BoYMZSzLx/Prr0HcJ5gSr/cXcbEr28ifcGu5nUtHEWK12bwd/pcRseO54rrT2+4Iix2ct1jL7Ipby12dxnX97yBzBfmsOmD7/ln2mt8eN00Ld2xN/WDF9/88r5Gx5G6h7ynshggzKq5XQw+oaK+ri/vtZtyyWrHJIzoyFvjnqh1AtfuLkc5jlWj7tNvhbilmyHvXsQzp1/F6NixfLXrC66b/pJmrfiGoEGVBj30shHMmfpek3J5l7pKWHTwH05OmHjcWjg18e+hBYy75rqGpxtoLmpaFNcI/PrFsG/6LwDEPTCx9pq9NVw3ZVIvbj15Ild1uwbQcsJf+OFjLL3lE9zSxcHSdA6UHGR+2jLMiokOIQkYFIVIWySL0lfyzLMza8+5b1Iqa982pOC7xeCpFyw1V1SV+H3PPtVDQY/k3iX489NjTzE6dhyz9//MGclTWjz3/dFAD9lsgxwo2c9b6z/gsu7n0ymoG7+n/sK5Dz9ce94RATc/eCHPj3zi2Arahil05NP3hYsqi4i0FTwd/pNPX88dfacx/+BfTHrhIeTmBi4dsxi46a5zifIPIau0gDe++QN2FPCfO87kieG3EWmNqTVixaU6ueyPu/n+63+0tli90I+fsTItRXVsNaQSV3xCKQsdtcfqV69oVVue/NqOlRJrn2j+vutF+oQP5O2NbzNzxRIWXfkpEomC0mYXW9WEPpHbBonzT+K+gXdwqDQTRShMTDyDzx59oKqrpxompXE/pypVtuSt54c93/LAkkf538pnSSvZi0ttYG3UNoyUkieWv9i2FL5AU6pmA0j4dPFiSt0ljIk7hU9vvqPhE7N2N29+/CuPz/yWN977SQtjVCWfvfAj7a+ZyGPLn8Kl1mzJGxUTn572Iu/ceyvtx6dU1n3wUuqqPSNnTSmIpYQiJ+a4QGKGtNNi+Ws61tc49fXX14XFgKVPFO3HpzDxxgnsefwH+kUMRiD4decGbhs9CUUoGIThuFL49XH8jWuOIwJMQYyKHQtoOdrPbncuvU/9gfWzVtW4f05Z7XleVKlid5dT6MwjwBiEvymQMlcJB0oP0iu8FykhXZiT+g8Xfv0QNj8LT4+9mv4Rg1pdUefmIq1kL2/8388tLUbjCDRVRqQA237dwKkB1zP/so8YHDVYU8ANia0HSCvRCvp4F/F5ybXz+vdzuGfA7QQqwTUeahAGpnQ4h+eVn6DURfTI9hzKyoNt+ZU71aaTg0yVdXnbBTL21EEoQnBG5z6c3m4ine4+t+Y1BD65c7QJZJ+5LbeEcCtJfZPZvz8D8uwk9k7ivJEDuHfgbfgbg6oodiEEMyc9ecIuRNSVfhvCoBgZntKB9aw6vIyfhE9+/If/jcyuMdb+xTUv89jMb1APlnDzjWfz/Mgn8DcFMj7+VFyqC6NipFtob27q6WLBwb+59ssXcLtVnjv3KsbFn3Lc5eGxGqxamoKDzbwg6GhSrYg5ElbN20DhRflEWqNJGtKB/X9va9xkp1tWDfvtFMTvd/yPQFPNCt9LkDmEn695nke6vcWdgy7m6q+fZ/O2tbUf4PXHe+cCFMHHd9/JeR2mIhAIIVClygVnjebr136t2ZJ3ebb5hi+7Jda+0fx15/P0CR/A3PQ/8DcFMDRqRJ0Gy4lWI8IX3b3Txgi3BWgK32Y4fBi8PZ9zvrkbd7WhuVN18P7cf1A35YLZwL0Db67yue9ElkExau6C/zzIk2f/B7vbjsNtZ0nGfJ5d9RwPLHmUHQWb23wMc4Q1hhEjjoMcQ0VO8h15WI1+rLjtU0ZcMarp5zIIfrn/KQZFDW/Q7u0DO/PpaS/TK7QfRUWezrM2L4lXh7slfv1i+N/TN3Bm8tkootKXrgiFd09+insf+U9lyKZvHL/v6l/v/wgr/9z9MgMih1LmLuWzTXNZkLYUg6jfnm3N85lHE13ptzHuG3A3v7z1CsaEoBoXtCz7fjm3L3iY1OI9pJXsZfb+n3hqxbPsX7gDgD4ju9VrxZW5S7EoFiYmnc6U9ueTY8+ixFXC4zNn8fInv3DBZ48wY9O7ONx2NuWtpcR5FAvCHCWEEPSLj2tpMY6cMhevrP4Qh9tOkDmEj05/GCLrGJV5J0Grr/cwKVx/73kMjhxSayhjbSjCQLv2nhj/+nzjnYJYdM8b3N739io1hb1YDTZu7nMtoQPitQ3euhC+0VVmxRNWKhBRNpIC2gGwIWctfy/bwOkdTqnXR1/gyGNtzgrKXW1opNdM6O6dNoZRMTI+/lTSnx7Cupw1XPXJC6Qt2V3p6il18f4zs3g/cja4VS2BlU/c842jJtS77NwgDBwqO0SENQqLwUqMLZ5IazTbn/gKBYVIWwwAAgVjAyyq1kpyUHRLi3DkSJjx3CzcqsrrY/5HnF8Sr9x3A7c/8KpWjKS6MSupOilqEJAcyHf3Pcr4hFMxN6F+ghCCs7r2ZSHza7f0BbQ/uSu//fdlkgM61nm+CGsM544ZzPtLUyHEfHjKcW8HIEFuzeeiXx7g5ylvMDR6FHsent2g7/Dljq+4/dHXWfT2/zEgcmjDvuhxgm7pt1GCzCGMih3Lxrtm8c9773He7ZO1tA2KgEgbUV2iqy5wMWgJ2HqEdUOVKi7VWWuEhsVgZXTseMKtUYCWIiLAFESCfzvi/JMwKWZMihmjYiQlpCf+psAaz9OaUaXKV2uWNfwAP6PmVmuNQR5uyYe//EOZuxRFKFzR9QpSTu5+eGptXwyCUVefxB/vv8XeF3/ktMTTm6TwvYyIG1yZV8f3mh5XZK8p/Vlyy0f1KnzQfptdeXnQOZiXHrqRkIHxlfn4vUVXTEpFOdD5Hy/g5nkP4VQdDfoOLtXJ2kN7AChqg6PUI6Xtmmk6gKagh0SNZPCpI3hjXD7rc9aSHJhMrF8CP+39nv888b/KycowK5d/9gxGk4G8vCKMRgNvXHIjExInNXpI39bJtWeyctmWhu3sHSmZPNEjrdAXrO4s4PPtn3NZymWYFQsRUSFs893Bd6FTUgCjxvfnhymv4Gf0b5brdw3pSdTgJDIX763MZS8Bo2DWy89wSsJpDa7CpgiFj057BHmaJNoWz+bsVN5fc6BqVlGHT/SOQ+WTl34E4O1xz9S7uNAt3fy6aDVjLxnFqJixTfzGbRfd0j9OEEIQbA5lVOxYkgI6YFLMnNP+AmY/+Rx+/WK0+Of9xez6YzPbftlA5uK9HJi/i3NeeOKE9Guuy1kHB0satrM3SZcnh02rxO7mlodeZsT7l5HnyGZSSrXqWxYD/v1jmPnGE2S8Noffz3u32RQ+aL74dy6/RbPsfe+RxcDgqCGNLrsZZYsj2qb59U9rP1RL21wXquSTz+dQ6Myv99wWg5X/u2oaYVYrWws2UuIsalL6kraKbukfxwghGBs/gdQnRpBZdpDPtn7D3B3bWfr3WkjTFF6nDnHYmvHhbysMjhyCqVMYzk3ZVZWUWamal95Lcyh7z+QjdveRV6+qiVIXG79fw8Ccy3jpoqshxExwlyiunzyWie3H0id8wGGht6pUmy3txqjYkyApAHYUVG4sc5FvzyXC2vT5kzHx4wnuGk1EaBBGk0ErkG4UEO2n1Zj2pn7ILudQ6QFCzGH1nrN9YHuGxncitzyPBemfclnXy/A/QeL2dUv/BMDP6E+7wE48NOh+/rrofV6/40ZChyZCgIn4xChEAxzVbtXFX2m/k11+iHxH7jGQ+uhiUEx06RivVYfypXqR8ebEJZv/3H5GjD2rxpwfXLCL5+Z9z4YZ37L7kZ94bMjDDIkaWaHwi52FuFQXUkr2F+9uthXYfsYAJp86pOrK4MTAion/pmJVrHRsH8eUYf2Ye83bvP7CXVV/N6cn149JwWqsfz2JKlUibdEMjR1Az7BeDIruxxfbvyCjNO2I5Gwr6Er/BMMgDFzZ7So+uvoOuo3rTmxgYJ3hbdnlGXy6/SPGfHolj/z5Kb/u/ZUxM67ltvkPYHeX41AdSCmxu8spdhbicNspdZW0+jh+P6M//1z9PlMuHls1fNE3q2NjJm2r72urwR3hnYj0vU5T8V6v3I1rb0HVzwyCgwdzCDWHYVTMFDhyWXhwHq+ue5XzfrmZLk9NId+RjRCCdoGdmm3VtSIUZpzyKCRXTuwnJ0UTYAo6ovMKoRAdEcIV3acSbo3i6m7XcN8N50OB3bMDEGxmytWnEGtLqPd8xc5CAoxB9Azry63zniLYHIxbuilzH2EB9jaC7t45ATEIA3H+ceTlFfHlrwt5eOj2igLRDtWBURhRhIJLdXHyezexbc4mcKgM+c9wHv32Kw4t2sO2OZuZvXQdhcWlXHzKcFILC1i0fDOPXXA+CYGxTE46q4W/Zf0EmIL4v1OeJru4hIUfLqjqwmmsUg40QbFPTvia8szUREMyUdaE9xBVHp56wS3JWLibTkXnEhLoT8bODC1Dq2eUYegRftRSEISaIzj3jJHMenU2WAzcftok3KoLg6HpgQKqVAm2WEjwTwY0t+X9A+9m3KvDmfD0PaSkJDHrsqdpH9i5XldVqauEtJJ9dA3phUmYmdixP59v+56TEgbjZ/SrNY3J8US9lr4Q4gMhRKYQYqPPtjAhxBwhxA7P/1DPdiGEeE0IsVMIsV4I0d/nmMs9++8QQlx+dL6OTkPpEdqXjfd8w10XTWbEi9fz2/6feW/zuzyweDq3/vMAuwu3897mGWxbvK0ix/nO3emUlnusK7ubtH92ULginXee/oZf3/iTguVpTJv+Bv/7+1tKXG0jFM5m9OOHKa/g37cRPueadEJhAyZ5qyfL84QcHi3K1x7SCq4fLK3iVjrz5CHYDEdnHkcIwfX9zgY/I1NvmsS5Hc/m8j/uweG2N/mc5e5S5q/czO+pv1ZsMytmBkUN5ZIzT+LPq1+nY1BKvQq/2FnInqIddA3pVZFo7aS40Zzf+XSibFHcNPd/2NV6CsEfB9SbWlkIMRooBj6RUvb0bHsOyJVSPiOEuA8IlVLeK4SYBNwCTAKGAK9KKYcIIcKAlcBANBtlFTBASllnLtgTObXysaLcXcb2gs28s/ZrFm3YzpIbP2b2/l+48q3XUA+VahZiXfiuA/DWOQ21YI4O4OlLL+b6nte3+nBQKSXPrHqO6dM/rD21rxdvSoBYP9jbyI7tSCZvPemBaz0+2NywYuddQkh77peKNRhHA7eq5W8aGj2SPUU7GHD1RXz+4uNMaX9+k85X7i7j/sVPMnfNZlbf8nWFO0pKiVu6G5wPv9RVgtVgq+gcVKkye/9PvLf2T+YtWoczvZDtM34iMaB9k+RsTRxRamUp5QKg+szdWcDHntcfA2f7bP9EaiwFQoQQscCpwBwpZa5H0c8BTmv0N9FpdqwGG73DBvDamKf449rX8DMGcGby2XRuF1+/wvfFLbWJSglklePYmM1d/3uH3/b/3OrD4YQQ3Nl/GjNfmQ4davA/V68i5XATEB6gdXTBZi1ipa7UB16OxKiXsqKg92FYDA2bIBbwzNWXHFWFD1r+prHxE7AZ/bTJYwFfbl7Y5PNZDTaeHfkoyckxvLjmZYqdhYD2uzVU4Ze7SlmbvRLp8yPY3eX0j+jPjAkP4swr5fSLx2A2NH2BWluhqRO50VLKg57XGYB3bBwPpPrsl+bZVtv2wxBCXCeEWCmEWJmVld1E8XQai1ExEuuXiCIUrEY/Lhg2qGEHVl8e7128pErItXP+HQ9wxg83cfeih7lt/gPMS/+Tsla4LsCsmDmnwwXsfH4WKaf3qqrorQYIs1S6Z/yMtEuK4c0X7mb3h7+R+eY8dr3zMzGjOhw9ASVaWGJNBJm1wiX1ILqFckW3S5tXrnpIDGjH1y8+ybunPNao41SpklGaht1djipVzIqZF8fezGOvzGTYm5eRU97wWsZu1cX9S55i/L238t6mGZS6inGpLoQQxPglEG6JZsY9t3HfkEuIsh4H+Zjq4YgncqWUUgjRbI5JKeUMYAZo7p3mOq9O42hIGGet+Ja7UyXzvlrEvBIXxPszI/072o/pwpzrXyfeMzHXmoj3T2bxfz/isb7P8dXfS7lg7BCu7HkeEdZIChz5zE9fxKi44XQK6lbFygw0BfPOZTdzYeEzREeEYrWY2P7Hpuatu1v9VCZFSw+RXVb/KMJi4PNb7ia4ATHszYlJMXNGu3OadOxDi1/hh3nLuey0UTw/ajrxfkkQYWXnnC1cFP8gf5w3o8qka01rDg6VpXPX/Bf49oM/ocTFZysXc0Hn87Ao1ooQVkUoXNrlikp3URvOJ9UQmvrtDgkhYqWUBz3uG2+3mw4k+uyX4NmWDoyptv2fJl5b5xgQaG7ARF9dPmrfZf/eEYDHB75nzlYGFl7J7od/wVZDpsWWxt8UyHMjpvP0cGeVlaTRtni6BPeo9bhTEiaR8dQ4jIqZzLID3NnpRX54/bfa71H1++et/epN+1AfyQGEhAeRv6ye+HKD4Oa7z+OM5LPrP2crQREKr4+djtnwOG+/+zOhVn/u6n87UyeO5MstP5OVmYdEVhgnbtVFmbu0IjxUSkmBI5ehL12tTWYDwYMT+GHqCzXWmwAqcvof7zTVvfMT4I3AuRz40Wf7ZZ4onqFAgccN9AcwQQgR6on0meDZptMK2Zi7hse+/Lr+HavrJd94d+/LEpf2V23gkL8xg71FO49EzKOKEKLRqQO8rjGjYiTaFsc1fSbVfUD11NjeDJi1Kfzqsf+7i3A46nHrBJi47p5zeXr4w7XWvW2tSCQvjH6EsecN5+lnP+PsH27nvsFXkjC2M7v2HiTfkVOxr0Ex4m8MRJUqDtXBFX/eSeKDZ2gK37NYrOBAHg533ZPdZsVMuau0Vbogm4uGhGx+AfwLpAgh0oQQVwPPAKcIIXYAJ3veA8wGdgM7gfeAGwGklLnAE8AKz990zzadVkahI5/T37qPktUZ9e9c3QNUPc5d+PxV12MlLs5+/0FKXcVHIm6rxaAYmbtvSd1uFyE0Re67grW2/f2NJAztAJE2lB5hFfH9pWtq/52CBsWz5J0PeWn0E43uwFoDfkZ/zIqZD059iNEXjWTBB/PpN/1qLj5pCNedOZalh/6tUgjF7i7jrB9v4s4Fj/L1B3/i2pRdtY0eKOXlNe/hVB3Y3eVklh2g3F2GS3Wys3ArLtWJW3Xx1c4vKXM3MC9TG6Terl9KeVEtH42vYV8J3FTLeT4APmiUdDrHlFJXMb2fu5BDS/bWvWNFtsl6TljX5yaFkJAASl3F2Az+x92CGFWqrD10qO6dIq1woPTwWP2a3D5OlbQFOyHGjw6JMez0htPW4mKLO6kjy6Z9SIT1yFIgtDSKMGBUTEwffRljfliK3JzHcy9/zemXjmV4zMgqLp4iZwF/fbsYcsorR52+RdSTAji/y0QcqoM/9s9mQ/YO2gfHkVWax4i4QXQK6opbuil2lmEUJo+P33Xc1YnW0zDoVPDb/l85tD6t8QuGBFqxiwhrzWUca9Dn5m7hZGfn0/3Jqby/ZQZz0mZT5Mg/bkrYlbqKWbN2++EVqrwEeRRJTfe6+qZwK8T4gVlh7lOvsPrWrxl11pDKc1e7RuDAOJZN+6jNK3zQXGah5nC6hfTk9Qdu0QyOAgd/L15HgSOvSghmsDmMDv3bVyp63/toUujZrxMDIgZT7CwgKTCRm3pfQ6/wHlzd/XKGRmtlJl2qk5PihxNoCsYpnSzNXMzdix7m7Y1vUeTIp8xVekQLzVoDutLXAbSJr9f//a1h6QNqignPd2gWls0IoZaqnwmhdQgRVvA3QmIAUsKB3ZolfN+nMznzumlE33cqN867j3JXaZtX/kZhJMDPVnPu/QBT7Z1BTYRbNas+yEzH4E6kl+xn4ZxVlbH7vh1HUgC/3fbMEWW1bG0YFCNZ5Ye4uMvFxA7TFk6VrjvE1K8eZmv+horwTZNi5usrH6/sUH0QnYOZfdkrGBQjM7d9zagnb+PUD28hsywThOBgaSolziKMwki4NQIhBCXOQgJNAfyweDV3/O8dFmYsQBEK2eWH+HjrB/xzYA7l7rIa26oqVezu8orXrak917sityXRV+QeWxYcnMvkFx/GtbGB6yMUocWwu2VluUbQFiopQK5ds/ztKlgUAjpHULzRE+hlUrTYcpuxau6YYDOjzx+OlJL/DjiZYHMwcf6xpIT0bPUre6uzPHMJJz1yy+Erd70+fIsBSuuPryfeHzLLwGZgyJmDKCgoZuvsDdr9F1SuIm4XyIqn36dHaN/jzl0GWhqFuelzmHrXI1qb8TPSfkQnpk2czGlJEwg2h2JWzJwy879s3LKX8kNFUOKEQiedJ/Zg9S3fAJL+r1+Aw+nCoCjMvf5NYvwScEs3dnc5dk/Sta35m/E3+dM9pA+b8tZy1dfP0SU5ljfGPcgpH9zElt83ABDQK5pRQ3tx75CpVVJXq1LFLV2YFDNSSkrdJUipYjP6H5N2XNeKXF3p6wDasPb6ufcz841fqypwXwSasvZWRnL5ZI0UaEnHytyVoYdQWdjaICrDN736SAjNr32orOo1oGrIp5+Rc68+hfdPebrGYtqtmXnpfzLpnru0UZAvvgXKXXXPj/j3j6Fke07VztFmgDCrllfH8xtcdOeZ/N/Jzx+XCh88FjOS9zbNYNojr2sdZrSNtHfnEGwORREGFKHglm6KnAW4VRe59mxu+esVru8/gbPanYtE8u6md9iem86a/elM7t6LaX1vxaiYKHEV82fqb4yOPYlSVwk59mzaB3bEYrDx6NL/sSkri+Urt1C05uDhEVYWA37dI7nujLHcM+AWQi0Rh8lf4Mgj35FLon/7ZqthUBu60teplzx7NnE3nVJRXOUwDKJSMdWWA8bgyRhZX5PyTj4KMHQPx70pp54DtH17nNWXF868lq4h3Yjxqz+FbmtASsmr617l/gfebnJytf4XDmb33gPkr0yvPZwzMYDNL31F+8DORyBt28Cluvg99RfOf/FpKHeR8cZcgs2hte5ffdHWgoNzmZ+6jAtTzmLmllncNeA2Ak3BdV7TLd1IqXKwNI1f9/7OK7/PZt/cbYdPuEuw9Iliy6NfEG2LR0oVN1pN6lfWvs64xBEMihyGRB7VCWJd6evUi1N1MPTd/7D5x7U179DUFMDNhbejUATtx6ewftp3Dc670tLk2bOJv38ycnOd+QVrJX5MJyIiglk3a1WtHWq3M/uw8LoPT4jqT14lvq94F9vzt5FZmk1SYCL9IwbibwrEpTpRPC6UmizqQkc+xc5CYvwSKHTmE2AMqrMteV01/x5ayPDo0RgVE6WuYro/cy6HFu05/AABkcOSuXfK2ZyWfDIfbvqCfQUF3ND3bIZGj0IRCutzVxFjiyXKdnTSPhxRwjWdEwOTYmbe1TPodFr3lhalZnzyx+/ZsJ9iV2GLitMYgs1hnHLSgCYfn74+lXHdu0CETUu74MWT8hpgy6qd5JZnHaGkbQOvIk8O6MjQqOF8v305Ex65k74vTeXyP+5g+PuXMvazK/lp73ccKkv3WOmVvWWQOaRipBhiDqtQ+Jpy1/b1rswtdZXg8vjmu4V0x+BJ0WA1+HHDxPGam82sVOZlshnApJD17z62ZKeSHNCB6UMf4aNTX2B4zEkVsncN7olLbcB8zlFAt/R1qrAxdw2Dbr28cRk2jzWxfhx89+8G1UJtLWzMXcOgay/RVic3BEVoUSj5DggyEdMnkbCwIAwGhQ1/rD/8PEEmtn7wE8kBHZtf+FaOS3VR6MzjYGk6efZ8Xl05i+270wkJDSQuPIRb+p9Du8D2qKgUOgpIDujAgdJUwiwRmBUzEnCo5RQ7i3GqDpIDO2ESJtzSRYEjj2CfjsFLavEerv7taQKsFn77Yj7k2aFTMF/eeR9Rtkju/m0Gq9btYNnDb9E7rOkdflOpy9JvG+NjnWNG99A+TDh/JH++/VdLi6LFpgNkVF0Sf9LpgwkyhRx7eY4AgzA0LrWyUWiKPcJK12Fd2Lp4Gxm2Q9rErUCz+H0jf0pcbMvbekIqfaNiJMwSWZFTZ+TpYzxhkip2VcvSWeQsYGvuVtoFtWN30Q7SitNpF+giwT8Jh2rn1bXvYlZMXNXjEkzCpKVtFqZa01DH+yfz23nvIBD80edXPt/8NynhUUxOOhOjYmL2xb0ZmnMZp7x8Fz/c/DjdQnsSZAqpiOhpSXRLX+cwtuStp/9/L9KqQTWEIykOUhtJAYTGhZK37kDl2gEBXSb2ZNH1HxBoDmnmCx5dip2FxD0wEeeGRrpgFKG5cXwjfEwKfc7qd5iPv8OEbiy75dMjrkl7vJNrz2JF5jLGx5+KQLAsczEAw6JHNznyya26QIgq4Zj59hze2/QR+wuzuSBlAkOiR2A+Rgpf9+nrNIouIT0YM3Vk3YXBfT+rnjisOVAleavSqywWixnZgcU3fNTmFD5o9XiHD609Q2etqFK7111CKrc5Vbonx2shmz7snr+dA6Wp6NRNmCWSUxNP1/zzQjA4agTDY046TOHn2rNYemghqcV7SC/Zh0t1VfH3+7K1YCN/p1fNIRliCefu/nfy2klPM9Sj8KWU2N3lLbpYS1f6OodhEAY+PO1haBdY+06+bdYtK2POm4u0kqorfxMDeP+KaW3Wis135LJh4+6mHexQYVt+5XsBnUNrSA8sJW619hXVdnc5f6T+0qpWh7YkwmOZ1xa5owgDM7fM5to//kenm8+ix4tTOPXb6/ho6weHKf73N3zDPT9+gEs9fHTsm7FVCIFZsbToWgpd6evUSIxfArf+Z3L9O3oXax1NFME3Dz3C2LhTju51jhKqVDln1l3k/rtf23Ckt0vCnB3bqkbyALgkBY6CWmWYvf9nprz8RJWUxDqVSCkpdRVXRPC8vvYd3n/uO+a/9w8cLGX/3G0s/GU5XUM7VykypEqVRVt2snXJ9gbd25ZePKcrfZ1auaTbWZWTqbXhlpolqsqjF8dvFHQK7njUVzEeLYqdhfw7b23lhsbephp0xL9/rK4xtfWughrixoGM0jQufu4Z5I4C9hY1ccRxHONUHby67lUSH57EkyueYVfhVp796ufD2nTXwZ3pFnq4m85e7oA8O1N/fJD1uauOldhNom0+RTrHhF6h/fn72deIHJ5cxZpPOb2XltMdalf0zennd0uKnW037/6yzCWQfgT52X1TUkR4/PgZpVr0ju8oK9rGoOjK8EApJcXOQn7b/zP9n70C9hRBhyDy7flNl+U4RJUqb65/i/uffI/SNRk88/rX9LrjYtw78w/bd+uaXXy14xvK3KU+x7spKCwBVbL4w4VcO+uFVl2BS1f6OrUihGBY9Gi2PvADn7z2GKFDE0ERbFu4rX6ru6ZMnE3FZiTSVnPoXFtg3v5lTauVK0B0D4UET+lKVVZdP5Fdrp3XIFB6hDHz0QfpHFS5uG530Xai75vAOTffTcFyraRix3ZxjIwdcwTf5vhiU95aPt72Ifd//EVlbqPccs1oqSkHVVoJ0+59hR7Pnssfqb9Q4iomoyydzG2VxWzWb9hFgaP11ojS4/R16sXP6M/5HS9i4oOTOfWzG1n9zYqGZ+L0JmerDW9yNt/EbdV2N3cIIcLSdpW+U/XpABsT3mpSkIfKDk/W5kuMH+8/cAdntT/7sEnuDzd9gbrJR/mYFcb3797iceKtBbd0c/Gn09m+bk/VkZhbHp4ZteqBZCzczdnL72b4RcPJyc6v2hlvz2fWru+5pvu1R032I0G39HUaTIApiN//8yZ08iSnshkgzFL3QXX5+T1L1rEolZE/1sPTzl582kj8TXVEErVypvW7Fjp77lljDH6HWrfC7xzMrOnTubjzpTVGNfWP7l7V7++WnNnppEYIcHyzPHMx25ftbLrrze5myUcL2fbLhsM++mTlQspbaZ1dXenrNAo/gz+xMeHamzK3tvy8LmqLVDAI7TOX1PLte8MIqxdxsRk4L2XckQndwsT5J/HstZceeUir7/Gdg1n35EdMSjqz1miQXuE9q3SiStdQhkaPODIZ2jBSSkpcxSzJmM/TK59h3DN3192pHgErvllO12emsODg3Fbn39fdOzqNQggFm9XHPVCf5VqbpW9UKvzRFatNa3B9jL3sJE6KPawcc5vjqm5X8Eivr7Gvy6x9p/pcP773ssxFuKWGWH0PbunmkcXvVulETx7dHz+Df8OFPk5wqg5+2DOL1xf/xtoNu3BuzWneOaeaL8qhRXs4de9dbHjxczoFdzu612sEuqWv0yikVLE7GpiewUtNhqjdXTlRVpGnv9ox3UJ5ccyNbSaFcl0EmIKYeePdWrWs2miM6ye9hL/S5tT68b6iXfzw8dzKDSaFOwef2+Ix4sea7QWbOHnmtVx2x3RWfL5US4NxtBW+L2klXP/7C+TbW8/aiLb/NOkcU4pchaTvPdS4g6orM28FLtAsfW8foggC+8dy1zmnE+Mfzui4UbQL7HSEErceRseNgWgb7G+G8FMJyw5s5sJabk+YNUKrrVvg0Da0C2RAxOAjv24b4455b7D883+bPzdUI1j82WJGpF/F6tu+wmKw1n/AUUZX+jqNosxVooW0NQWzoj18TrWyrqvQ/gL6x/LBtbcxOm4MwW0oZXJjMAgD+DdDtSSDAKNCv5jaq2QFGIOIiY8gY7dWd2DEyN7YjCeWa8fuLmf5yi0tqvABcKrs/mcb1/d8kHfGP9Xiil9X+jqNwqW6oProuKFhiGFWKLBXWvZox/U6pz9zrnjruFX2XmwGfwb06cyqLcubfhKzwvirxvLEyCvpGda31t2MipFPr76PGwNepU9KO54eeWubXdHcVAzCyIjBPfh95YGWFgUcKl+++gsAb49/sqKAekugK32dRpFWklo1jzs03JLKqCGEzSB46czrj3uFD9pit9FdOrKKRij96h1qpI1PJz5RY+Ht6oyMGcOaW0aiCMMJ58sHcEknfWPi+L2lS316cUu+/GUhb4yrPSneseDE6vp1jph5qUsOf4AE9Ycj1va51Jaxnyhc2u1crSJWfXjTK5iUqvfOYmhUQW2DYjwhFT6A1WAj3BrcOhS+lwI7xc6ak+IdK3Slr9NgXKqTT/5ZfPgHkrofLFHH5x2D6BRUu2/6eCMlpCdPPXCNtjCtLrx59L3J7Dwd68SJQ/E3tt2FaseaqV3Og06tKB13ZhlLMpa0qAi60tdpMC7VSdrBaukX/I2aQjLX0ZRq0vcCiPVj+ePvEuef1IxStm4UoXBb71vod0b/uneUVL1vEib8dzxfTHrhhPPNHwkR1hiuPvfklrl4TaNbs4GUkC7HXhYf9Naj02By7Fm4D1YLNyz3ibU3K5XpBryEWWqM008Y05kPH7yLHqF9j4aorRqDYuSpUy+vO2bfv+p0m7l3JB+dNr3FIz/aIuOS+zdvgZ+GUtPoNjmQ5MCWrWNcr9IXQnwghMgUQmz02faYECJdCLHW8zfJ57P7hRA7hRDbhBCn+mw/zbNtpxDivub/KjpHG6NiPLwhu2XVMMxdhVV9/GWuqqmBPRb+J1fcy9ntzz1hrdbRseMY9Z+Rte9QUjlZHjAgluUPvNugyVudwzk1aRLmng24d2YFAkyQEsKY68Zy3/TLeeTJq+kyqSd0CKq/WFAD+pWBA1JaNHIHGha98xHwBvBJte0vSylf8N0ghOgOTAV6AHHAX0II71jmTeAUIA1YIYT4SUq5+Qhk1znGmBVL3dYpVPqfQftvUEBRIdSi5TlJ8Oepmy9jcORwDMfBStumoggDyaEhLKx7Jzqf2p0/rn2NWL/EYyXacYdFsRIaFMAhailKbzHQ76z+PDfpWpIDkgmzROBnDKiYAL+nv4tSVzEX/HIv/7w3TzNiuoXCzoLK1b3eDqGmjLKeCKzAgXF8du5jVYqntwT1PnVSygVCiHYNPN9ZwJdSSjuwRwixE/AuA9wppdwNIIT40rOvrvTbEOtz1kJuPQnWwOOPluBvxNQ+RMt14klsldK3A9d0v/KEVvighW9O6jiYz4J/00ZDQWZiu8djNhux252MHNCNs7sMZXLymS1uGbZ1VmQt4ZBPvnsSAxg8uhdRoUHkFpcyJCmB6UMfqjUqyqAYCTSH8M0Zz9Nn24UEBvpxwdBBPPH0J5rS9wYqVE8j4ptexGLgmxsfIjmg+Vw7UsomRWYdyZN3sxDiMmAlcKeUMg+IB5b67JPm2QaQWm37kCO4ts4xRpUqzyz9pnF5Sxwqgwd25b/Xn8y6zG2oUnJH/xsIMoccNTnbEmcmT2HZ2x0oc5XRMagzIeZwhBBIKTGcoLH1R4O+4QN5/u5r+XL1UnonxfPI0FuItsU3+v4GmIL44b9PEe+fyJKMxfSd3Je1362q2br33WRS6HBSF4bHjD6yL1KN1JI9qFJtdKqSpjpU3wY6An2Bg8CLTTzPYQghrhNCrBRCrMzKamChDp2jzv7i3fzzrU+oWUOeF6fK4pmLibJF8uSwx3h6+ONEWGOOmoxtDYNipHfYAIZEjSTCGo1RMWIQBowncGz90cBm9OPm3jez8PJPeWvcs8T4JTT5/vYK648iDCQGJPDFBdPpM8UTheWdw/I9rXcOS8BrF97Y7MVrQi3hvLDyfYqdhUhZR8h0NZqk9KWUh6SUbimlCrxHpQsnHfB1PiZ4ttW2vaZzz5BSDpRSDoyM1CeuWgMu1cW0eS9Xde00tI05VF5d9UOjGqWOztGguTrSEHMYfcIH0i6wE3ePO1tT7t4AB2+KcIPPtigb/SMG1HK2phNoCubxYXfwzMqXyLFnIqXU0qTUQ5PcO0KIWCnlQc/bKYA3sucn4HMhxEtoE7mdgeVot6GzEKI9mrKfClzclGvrHHtc0snchWurbjQrlUnTfPH6MhUBQSa6DO/CBxMe1y1XneOS05PP4sHHd/PV4mXs3JaKf6g//fp2YdGspXQf050nJ19Kh6D2Ry3NSKA5hE//WsLMv5fSo0d7Luo+jEu6XFbnMfUqfSHEF8AYIEIIkQY8CowRQvRFe7z3Av8FkFJuEkJ8jTZB6wJuklJbYy+EuBn4AzAAH0gpNzXpW+occ8yKhc4d4tnsLT4hgCCzNjlbzYDvcVZfbh07kXZBSXQJTiHcGqXXZNU5brEYrDw06H7uH+im0JEHgL8piP2n7SbBPxmLYj1qBo+Ukq92fk7mpgMgYXDfLkxud3q9x4nWPOweMLC/XLxsUUuLccKTb88h7v5JyLRiKPSkyPQteK4Irc5tmZubH7qQ50c+0XLC6ui0ICWuYozCiFmx4JZu8uxZRNpim/06LtWJW7pxqg4umX0fcxesIe1/vxLiGVHYjP6rpJQDazr2xI6b02kQmeUZJMVFsS+nXCu/51SrRix0DGL+w6+yNW8bpyWdWvuJdHSOc6wGG2f8cBNut0pWZh7TTp7MpV2uaNZrSCn5aOtHvLNgDo+eNpUh8Uk8fN+lFQq/Pk7M5ZA6jWJDzkaeP+9KrYh5TSGbmWXsKdzD+IRxRNnijr2AOjqtBIMwcMegs1nw/VK2LN9xVPLs7C7azi0vv82mFTtICkzi/gH3MjByWIOP1y19nTqRUtI+qB1LDqyA/JoXZpmSghkdN5ooa/MPY3V02hqjY8fy1N1X0jEkib7hzRe1U+ws5LNtM3no8y8whdj48pZ76R02oNFzBrrS16kTiUQRCisO7j78Q0VAgJHzJgwjxtb02GcdneMJs8HCHX2nNdv53NLNn6mzuXzGixRtySSkZwwbH/qcSGvMMV+Rq3MCUOYuZUH6v8xdtqEy7tggIM4fS5gfy+5/h/ZBXXSFr6NzlFiXs5JzXnwCdheCWeG5Sy4jxi+hyefTlb5OrahSZemhxXyyZBE5OzKhXSBklmkZIFOL8YsPbVQVJx0dncaTEtKD3+99mgVpyxkS24fxCacd0fn0iVydWlGEQvfQ7iTFR2lJwdJLqtTHzVuaSs97LmZf0c4WlFJH5/jG3xjAsOhRPDTofiYkTj7iLJ260tepFbd089Hmz1m7YRcEm8FmBP9Kyz54cAL/PvUOHYO6tqCUOjrHP2aDpdlcqLrS16kVBYWeEZ2JigrRarqGW6FYW5yl9Ajj/66+jb7hg07YQig6Om0R/WnVqRUhBL0j+pCSGKstysoo1T4wKyy47xUmJ5/VsgLq6Og0Gl3p69RJqDmci7uPY+DonpqlbzFAlI2NOXr9Gx2dtogevaNTJ4GmYHqH9yEl7h8sJ5uJDQ4io7CINxf8ybkdzyPAFNTSIuro6DQC3dLXqRMhBNG2eG4fcAnn9hhAp7AIAqwWNvy7lZPev4aFB+dR6ipBlY2oqKWjo9Ni6Ja+Tr0oQqFnWD+6hvTkUNkBlkYspVtEBOd2nkixs5giZz5mxaJP6OrotAF0pa/TYIyKiXj/ZM7tkMyU9pplryt6HZ22ha70dZqErux1dNom+pOro6OjcwKhK30dHR2dEwhd6evo6OicQOhKX0dHR+cEQlf6Ojo6OicQutLX0dHROYHQlb6Ojo7OCYSu9HV0dHROIHSlr6Ojo3MCoSt9HR0dnRMIXenr6OjonEDoSl9HR0fnBEJX+jo6OjonELrS19HR0TmBqFfpCyEShRDzhBCbhRCbhBC3ebaHCSHmCCF2eP6HerYLIcRrQoidQoj1Qoj+Pue63LP/DiHE5Ufva+no6Ojo1ERDLH0XcKeUsjswFLhJCNEduA+YK6XsDMz1vAeYCHT2/F0HvA1aJwE8CgwBBgOPejsKHR0dHZ1jQ71KX0p5UEq52vO6CNgCxANnAR97dvsYONvz+izgE6mxFAgRQsQCpwJzpJS5Uso8YA5wWnN+GR0dHR2dummUT18I0Q7oBywDoqWUBz0fZQDRntfxQKrPYWmebbVtr36N64QQK4UQK7Oyshsjno6Ojo5OPTRY6QshAoBZwO1SykLfz6SUEpDNIZCUcoaUcqCUcmBkZERznFJHR0dHx0ODlL4QwoSm8GdKKb/zbD7kcdvg+Z/p2Z4OJPocnuDZVtt2HR0dHZ1jREOidwTwf8AWKeVLPh/9BHgjcC4HfvTZfpknimcoUOBxA/0BTBBChHomcCd4tuno6OjoHCOMDdhnBHApsEEIsdaz7QHgGeBrIcTVwD7gAs9ns4FJwE6gFLgSQEqZK4R4Aljh2W+6lDK3Ob6Ejo6Ojk7DqFfpSykXAaKWj8fXsL8EbqrlXB8AHzRGQB0dHR2d5kNfkaujo6NzAqErfR0dHZ0TCF3p6+jo6JxA6EpfR0dH5wRCV/o6Ojo6JxC60tfR0dE5gdCVvo6Ojs4JhNDC6lsnQogiYFtLy1ELEUBrzQiny9Z4WqtcoMvWVFqrbMdCrmQpZWRNHzRkRW5Lsk1KObClhagJIcRKXbbG01pla61ygS5bU2mtsrW0XLp7R0dHR+cEQlf6Ojo6OicQrV3pz2hpAepAl61ptFbZWqtcoMvWVFqrbC0qV6ueyNXR0dHRaV5au6Wvo6Ojo9OM6EpfR0dH5wSi1Sp9IcRpQohtQoidQoj7WkiGvUKIDUKItUKIlZ5tYUKIOUKIHZ7/oZ7tQgjxmkfe9UKI/s0sywdCiEwhxEafbY2WRQhxuWf/HUKIy2u6VjPI9ZgQIt1z39YKISb5fHa/R65tQohTfbY3++8thEgUQswTQmwWQmwSQtzm2d6i960OuVr8vgkhrEKI5UKIdR7ZHvdsby+EWOa5zldCCLNnu8Xzfqfn83b1yXwUZPtICLHH57719Ww/Zs+B55wGIcQaIcQvnvctfs9qRErZ6v4AA7AL6ACYgXVA9xaQYy8QUW3bc8B9ntf3Ac96Xk8CfkMrODMUWNbMsowG+gMbmyoLEAbs9vwP9bwOPQpyPQbcVcO+3T2/pQVo7/mNDUfr9wZigf6e14HAdo8MLXrf6pCrxe+b57sHeF6bgGWee/E1MNWz/R3gBs/rG4F3PK+nAl/VJfNRku0j4Lwa9j9mz4HnvHcAnwO/eN63+D2r6a+1WvqDgZ1Syt1SSgfwJXBWC8vk5SzgY8/rj4GzfbZ/IjWWAiHCUzi+OZBSLgCql5dsrCynAnOklLlSyjxgDnDaUZCrNs4CvpRS2qWUe9BKag7mKP3eUsqDUsrVntdFwBYgnha+b3XIVRvH7L55vnux563J8yeBccC3nu3V75n3Xn4LjBdCiDpkPhqy1cYxew6EEAnAZOB9z3tBK7hnNdFalX48kOrzPo26H4qjhQT+FEKsEkJc59kWLbVC7wAZQLTndUvI3FhZjqWMN3uG1B943SctKZdnCN0PzTpsNfetmlzQCu6bx02xFshEU4i7gHwppauG61TI4Pm8AAg/VrJJKb337SnPfXtZCGGpLls1GY6GbK8A9wCq5304reSeVae1Kv3WwkgpZX9gInCTEGK074dSG5O1ipjX1iQL8DbQEegLHARebElhhBABwCzgdilloe9nLXnfapCrVdw3KaVbStkXSECzNLu2hBw1UV02IURP4H40GQehuWzuPZYyCSFOBzKllKuO5XWbSmtV+ulAos/7BM+2Y4qUMt3zPxP4Hu0BOOR123j+Z3p2bwmZGyvLMZFRSnnI83CqwHtUDlGPuVxCCBOaYp0ppfzOs7nF71tNcrWm++aRJx+YBwxDc414c3X5XqdCBs/nwUDOMZTtNI+7TEop7cCHHPv7NgI4UwixF83FNg54lVZ2zypo7kmC5vhDSwS3G20ywztB1eMYy+APBPq8XoLm93ueqpOAz3leT6bqpNHyoyBTO6pOmDZKFjQraA/a5FWo53XYUZAr1uf1NDQ/JUAPqk5U7UabjDwqv7fn+38CvFJte4vetzrkavH7BkQCIZ7XNmAhcDrwDVUnJW/0vL6JqpOSX9cl81GSLdbnvr4CPNMSz4Hn3GOonMht8XtWo4zNfcJmE0ybed+O5k98sAWu38HzA6wDNnllQPO9zQV2AH95G4unYb3pkXcDMLCZ5fkCbcjvRPP1Xd0UWYCr0CaIdgJXHiW5PvVcdz3wE1WV2YMeubYBE4/m7w2MRHPdrAfWev4mtfR9q0OuFr9vQG9gjUeGjcAjPs/Dcs/3/waweLZbPe93ej7vUJ/MR0G2vz33bSPwGZURPsfsOfA57xgqlX6L37Oa/vQ0DDo6OjonEK3Vp6+jo6OjcxTQlb6Ojo7OCYSu9HV0dHROIHSlr6Ojo3MCoSt9HR0dnRMIXenr6OjonEDoSl9HR0fnBOL/AfHgeMpfrCPDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(default_pasture_ha_2000)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Generate yield for pasture\n",
"As the generic yield in not ptovided for pasture, we could produce the average yield by country using data from FAO. The data for the default pasture yield can be obtained from [FAOSTAT](http://www.fao.org/faostat/en/#data/QL)\n",
"\n",
" \"\"\"\n",
" \"http://fenixservices.fao.org/faostat/api/v1/en/data/QL?area=5000%3E&area_cs=FAO&element=2413&item=919&item_cs=FAO&year=2000&show_codes=true&show_unit=true&show_flags=true&null_values=false&output_type=csv\"\n",
" \"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = \"http://fenixservices.fao.org/faostat/api/v1/en/data/QL?area=5000%3E&area_cs=FAO&element=2413&item=919&item_cs=FAO&year=2000&show_codes=true&show_unit=true&show_flags=true&null_values=false&output_type=csv\""
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"default_pasture_ha_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m.tif\""
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Domain Code</th>\n",
" <th>Domain</th>\n",
" <th>Area Code (FAO)</th>\n",
" <th>Area</th>\n",
" <th>Element Code</th>\n",
" <th>Element</th>\n",
" <th>Item Code (FAO)</th>\n",
" <th>Item</th>\n",
" <th>Year Code</th>\n",
" <th>Year</th>\n",
" <th>Unit</th>\n",
" <th>Value</th>\n",
" <th>Flag</th>\n",
" <th>Flag Description</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>295</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>3</td>\n",
" <td>Albania</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>219</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>4</td>\n",
" <td>Algeria</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>240</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>7</td>\n",
" <td>Angola</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>285</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>8</td>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>224</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Domain Code Domain Area Code (FAO) Area \\\n",
"0 QL Livestock Primary 2 Afghanistan \n",
"1 QL Livestock Primary 3 Albania \n",
"2 QL Livestock Primary 4 Algeria \n",
"3 QL Livestock Primary 7 Angola \n",
"4 QL Livestock Primary 8 Antigua and Barbuda \n",
"\n",
" Element Code Element Item Code (FAO) Item Year Code \\\n",
"0 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"1 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"2 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"3 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"4 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"\n",
" Year Unit Value Flag Flag Description \n",
"0 2000 hg/An 295 Fc Calculated data \n",
"1 2000 hg/An 219 Fc Calculated data \n",
"2 2000 hg/An 240 Fc Calculated data \n",
"3 2000 hg/An 285 Fc Calculated data \n",
"4 2000 hg/An 224 Fc Calculated data "
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pasture_yield_2000 = pd.read_csv('../../datasets/raw/crop_data/default_pasture/FAOSTAT_data_6-22-2021_yield.csv')\n",
"pasture_yield_2000.head()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Afghanistan with timeout=180\n",
"Geocoded \"Afghanistan\" to (66.2385139, 33.7680065)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Afghanistan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Afghanistan\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Albania with timeout=180\n",
"Geocoded \"Albania\" to (19.9999619, 41.000028)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Albania')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Albania\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Algeria with timeout=180\n",
"Geocoded \"Algeria\" to (2.9999825, 28.0000272)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Algeria')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Algeria\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Angola with timeout=180\n",
"Geocoded \"Angola\" to (17.5691241, -11.8775768)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Angola')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Angola\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Antigua+and+Barbuda with timeout=180\n",
"Geocoded \"Antigua and Barbuda\" to (-61.9554608, 17.2234721)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Antigua and Barbuda')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Antigua+and+Barbuda\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Argentina with timeout=180\n",
"Geocoded \"Argentina\" to (-64.9672817, -34.9964963)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Argentina')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Argentina\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Armenia with timeout=180\n",
"Geocoded \"Armenia\" to (44.6736646, 40.7696272)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Armenia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Armenia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Australia with timeout=180\n",
"Geocoded \"Australia\" to (134.755, -24.7761086)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Australia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Australia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Austria with timeout=180\n",
"Geocoded \"Austria\" to (13.2, 47.2)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Austria')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Austria\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Azerbaijan with timeout=180\n",
"Geocoded \"Azerbaijan\" to (47.7872508, 40.3936294)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Azerbaijan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Azerbaijan\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Bahamas with timeout=180\n",
"Geocoded \"Bahamas\" to (-78.0000547, 24.7736546)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Bahamas')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Bahamas\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Bahrain with timeout=180\n",
"Geocoded \"Bahrain\" to (50.5344606, 26.1551249)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Bahrain')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Bahrain\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Bangladesh with timeout=180\n",
"Geocoded \"Bangladesh\" to (90.2932426, 24.4768783)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Bangladesh')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Bangladesh\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Barbados with timeout=180\n",
"Geocoded \"Barbados\" to (-59.5250305, 13.1500331)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Barbados')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Barbados\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Belarus with timeout=180\n",
"Geocoded \"Belarus\" to (27.6971358, 53.4250605)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Belarus')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Belarus\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Belgium with timeout=180\n",
"Geocoded \"Belgium\" to (4.6667145, 50.6402809)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Belgium')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Belgium\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Belize with timeout=180\n",
"Geocoded \"Belize\" to (-88.7600927, 16.8259793)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Belize')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Belize\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Benin with timeout=180\n",
"Geocoded \"Benin\" to (2.31006719638123, 9.2231105)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Benin')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Benin\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Bhutan with timeout=180\n",
"Geocoded \"Bhutan\" to (90.5119273, 27.549511)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Bhutan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Bhutan\n",
"Pausing 1 seconds before making HTTP GET request\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Bolivia+%28Plurinational+State+of%29 with timeout=180\n",
"Geocoded \"Bolivia (Plurinational State of)\" to (-64.9912286, -17.0568696)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Bolivia (Plurinational State of)')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Bolivia+%28Plurinational+State+of%29\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Bosnia+and+Herzegovina with timeout=180\n",
"Geocoded \"Bosnia and Herzegovina\" to (17.5961467, 44.3053476)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Bosnia and Herzegovina')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Bosnia+and+Herzegovina\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Botswana with timeout=180\n",
"Geocoded \"Botswana\" to (24.5928742, -23.1681782)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Botswana')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Botswana\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Brazil with timeout=180\n",
"Geocoded \"Brazil\" to (-53.2, -10.3333333)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Brazil')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Brazil\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Brunei+Darussalam with timeout=180\n",
"Geocoded \"Brunei Darussalam\" to (114.5653908, 4.4137155)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Brunei Darussalam')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Brunei+Darussalam\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Bulgaria with timeout=180\n",
"Geocoded \"Bulgaria\" to (25.4856617, 42.6073975)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Bulgaria')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Bulgaria\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Burkina+Faso with timeout=180\n",
"Geocoded \"Burkina Faso\" to (-1.6880314, 12.0753083)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Burkina Faso')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Burkina+Faso\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Burundi with timeout=180\n",
"Geocoded \"Burundi\" to (29.8870575, -3.3634357)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Burundi')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Burundi\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Cabo+Verde with timeout=180\n",
"Geocoded \"Cabo Verde\" to (-24.0083947, 16.0000552)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Cabo Verde')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Cabo+Verde\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Cambodia with timeout=180\n",
"Geocoded \"Cambodia\" to (104.869423, 13.5066394)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Cambodia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Cambodia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Cameroon with timeout=180\n",
"Geocoded \"Cameroon\" to (13.1535811, 4.6125522)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Cameroon')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Cameroon\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Canada with timeout=180\n",
"Geocoded \"Canada\" to (-107.991707, 61.0666922)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Canada')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Canada\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Central+African+Republic with timeout=180\n",
"Geocoded \"Central African Republic\" to (19.9981227, 7.0323598)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Central African Republic')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Central+African+Republic\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Chad with timeout=180\n",
"Geocoded \"Chad\" to (19.0156172, 15.6134137)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Chad')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Chad\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Chile with timeout=180\n",
"Geocoded \"Chile\" to (-71.3187697, -31.7613365)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Chile')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Chile\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=China%2C+Hong+Kong+SAR with timeout=180\n",
"Geocoded \"China, Hong Kong SAR\" to (114.1849161, 22.350627)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'China, Hong Kong SAR')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=China%2C+Hong+Kong+SAR\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=China%2C+Macao+SAR with timeout=180\n",
"Geocoded \"China, Macao SAR\" to (114.165602, 22.2791547)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'China, Macao SAR')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=China%2C+Macao+SAR\n",
"OSM returned a Point as the geometry\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=China%2C+mainland with timeout=180\n",
"Geocoded \"China, mainland\" to (24.9365563, 60.1672949)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'China, mainland')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=China%2C+mainland\n",
"OSM returned a Point as the geometry\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=China%2C+Taiwan+Province+of with timeout=180\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nominatim could not geocode query \"China, Taiwan Province of\"\n",
"China, Taiwan Province of\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Colombia with timeout=180\n",
"Geocoded \"Colombia\" to (-73.783892, 2.8894434)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Colombia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Colombia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Comoros with timeout=180\n",
"Geocoded \"Comoros\" to (44.2832964, -12.2045176)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Comoros')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Comoros\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Congo with timeout=180\n",
"Geocoded \"Congo\" to (15.6419155, -0.7264327)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Congo')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Congo\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Cook+Islands with timeout=180\n",
"Geocoded \"Cook Islands\" to (-157.9753368892878, -19.919672900000002)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Cook Islands')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Cook+Islands\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Costa+Rica with timeout=180\n",
"Geocoded \"Costa Rica\" to (-84.0739102, 10.2735633)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Costa Rica')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Costa+Rica\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=C%C3%B4te+d%27Ivoire with timeout=180\n",
"Geocoded \"Côte d'Ivoire\" to (-5.5679458, 7.9897371)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', \"Côte d'Ivoire\")])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=C%C3%B4te+d%27Ivoire\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Croatia with timeout=180\n",
"Geocoded \"Croatia\" to (17.0118954, 45.5643442)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Croatia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Croatia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Cuba with timeout=180\n",
"Geocoded \"Cuba\" to (-80.8328748, 23.0131338)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Cuba')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Cuba\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Cyprus with timeout=180\n",
"Geocoded \"Cyprus\" to (33.1451285, 34.9823018)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Cyprus')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Cyprus\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Czechia with timeout=180\n",
"Geocoded \"Czechia\" to (15.4749544, 49.8167003)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Czechia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Czechia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Democratic+People%27s+Republic+of+Korea with timeout=180\n",
"Geocoded \"Democratic People's Republic of Korea\" to (127.0870417, 40.3736611)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', \"Democratic People's Republic of Korea\")])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Democratic+People%27s+Republic+of+Korea\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Democratic+Republic+of+the+Congo with timeout=180\n",
"Geocoded \"Democratic Republic of the Congo\" to (23.8222636, -2.9814344)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Democratic Republic of the Congo')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Democratic+Republic+of+the+Congo\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Denmark with timeout=180\n",
"Geocoded \"Denmark\" to (10.3333283, 55.670249)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Denmark')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Denmark\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Djibouti with timeout=180\n",
"Geocoded \"Djibouti\" to (42.757784519943655, 11.85677545)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Djibouti')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Djibouti\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Dominica with timeout=180\n",
"Geocoded \"Dominica\" to (-70.3028026, 19.0974031)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Dominica')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Dominica\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Dominican+Republic with timeout=180\n",
"Geocoded \"Dominican Republic\" to (-70.3028026, 19.0974031)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Dominican Republic')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Dominican+Republic\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Ecuador with timeout=180\n",
"Geocoded \"Ecuador\" to (-79.3666965, -1.3397668)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Ecuador')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Ecuador\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Egypt with timeout=180\n",
"Geocoded \"Egypt\" to (29.2675469, 26.2540493)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Egypt')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Egypt\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=El+Salvador with timeout=180\n",
"Geocoded \"El Salvador\" to (-88.9140683, 13.8000382)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'El Salvador')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=El+Salvador\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Equatorial+Guinea with timeout=180\n",
"Geocoded \"Equatorial Guinea\" to (10.5170357, 1.613172)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Equatorial Guinea')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Equatorial+Guinea\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Eritrea with timeout=180\n",
"Geocoded \"Eritrea\" to (37.9999668, 15.9500319)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Eritrea')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Eritrea\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Estonia with timeout=180\n",
"Geocoded \"Estonia\" to (25.3319078, 58.7523778)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Estonia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Estonia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Eswatini with timeout=180\n",
"Geocoded \"Eswatini\" to (31.3991317, -26.5624806)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Eswatini')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Eswatini\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Ethiopia with timeout=180\n",
"Geocoded \"Ethiopia\" to (38.6521203, 10.2116702)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Ethiopia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Ethiopia\n",
"OSM returned a Point as the geometry\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Faroe+Islands with timeout=180\n",
"Geocoded \"Faroe Islands\" to (-7.0322972, 62.0448724)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Faroe Islands')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Faroe+Islands\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Fiji with timeout=180\n",
"Geocoded \"Fiji\" to (179.0122737, -18.1239696)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Fiji')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Fiji\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Finland with timeout=180\n",
"Geocoded \"Finland\" to (25.9209164, 63.2467777)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Finland')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Finland\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=France with timeout=180\n",
"Geocoded \"France\" to (1.8883335, 46.603354)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'France')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=France\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=French+Guyana with timeout=180\n",
"Geocoded \"French Guyana\" to (-52.999998, 4.0039882)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'French Guyana')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=French+Guyana\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=French+Polynesia with timeout=180\n",
"Geocoded \"French Polynesia\" to (-146.0490931059517, -16.03442485)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'French Polynesia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=French+Polynesia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Gabon with timeout=180\n",
"Geocoded \"Gabon\" to (11.6899699, -0.8999695)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Gabon')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Gabon\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Gambia with timeout=180\n",
"Geocoded \"Gambia\" to (-16.118732162564726, 13.44294275)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Gambia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Gambia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Georgia with timeout=180\n",
"Geocoded \"Georgia\" to (-83.1137366, 32.3293809)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Georgia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Georgia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Germany with timeout=180\n",
"Geocoded \"Germany\" to (10.4234469, 51.0834196)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Germany')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Germany\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Ghana with timeout=180\n",
"Geocoded \"Ghana\" to (-1.0800271, 8.0300284)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Ghana')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Ghana\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Greece with timeout=180\n",
"Geocoded \"Greece\" to (21.9877132, 38.9953683)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Greece')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Greece\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Grenada with timeout=180\n",
"Geocoded \"Grenada\" to (-61.6904045, 12.1360374)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Grenada')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Grenada\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Guadeloupe with timeout=180\n",
"Geocoded \"Guadeloupe\" to (-61.68712602138846, 16.230510250000002)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Guadeloupe')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Guadeloupe\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Guatemala with timeout=180\n",
"Geocoded \"Guatemala\" to (-89.8988087, 15.6356088)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Guatemala')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Guatemala\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Guinea with timeout=180\n",
"Geocoded \"Guinea\" to (-10.7083587, 10.7226226)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Guinea')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Guinea\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Guinea-Bissau with timeout=180\n",
"Geocoded \"Guinea-Bissau\" to (-15.535471371611191, 11.6688305)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Guinea-Bissau')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Guinea-Bissau\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Guyana with timeout=180\n",
"Geocoded \"Guyana\" to (-58.6416891, 4.8417097)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Guyana')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Guyana\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Haiti with timeout=180\n",
"Geocoded \"Haiti\" to (-72.3570972, 19.1399952)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Haiti')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Haiti\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Honduras with timeout=180\n",
"Geocoded \"Honduras\" to (-86.0755145, 15.2572432)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Honduras')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Honduras\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Hungary with timeout=180\n",
"Geocoded \"Hungary\" to (19.5060937, 47.1817585)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Hungary')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Hungary\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Iceland with timeout=180\n",
"Geocoded \"Iceland\" to (-18.1059013, 64.9841821)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Iceland')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Iceland\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=India with timeout=180\n",
"Geocoded \"India\" to (78.6677428, 22.3511148)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'India')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=India\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Indonesia with timeout=180\n",
"Geocoded \"Indonesia\" to (117.8902853, -2.4833826)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Indonesia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Indonesia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Iran+%28Islamic+Republic+of%29 with timeout=180\n",
"Geocoded \"Iran (Islamic Republic of)\" to (67.03296598400989, 24.81861825)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Iran (Islamic Republic of)')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Iran+%28Islamic+Republic+of%29\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Iraq with timeout=180\n",
"Geocoded \"Iraq\" to (44.1749775, 33.0955793)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Iraq')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Iraq\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Ireland with timeout=180\n",
"Geocoded \"Ireland\" to (-7.9794599, 52.865196)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Ireland')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Ireland\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Israel with timeout=180\n",
"Geocoded \"Israel\" to (34.8667654, 31.5313113)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Israel')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Israel\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Italy with timeout=180\n",
"Geocoded \"Italy\" to (12.674297, 42.6384261)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Italy')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Italy\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Jamaica with timeout=180\n",
"Geocoded \"Jamaica\" to (-77.3947693, 18.1850507)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Jamaica')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Jamaica\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Japan with timeout=180\n",
"Geocoded \"Japan\" to (139.2394179, 36.5748441)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Japan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Japan\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Jordan with timeout=180\n",
"Geocoded \"Jordan\" to (36.941628, 31.1667049)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Jordan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Jordan\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Kazakhstan with timeout=180\n",
"Geocoded \"Kazakhstan\" to (65.2093197, 47.2286086)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Kazakhstan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Kazakhstan\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Kenya with timeout=180\n",
"Geocoded \"Kenya\" to (38.4313975, 1.4419683)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Kenya')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Kenya\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Kuwait with timeout=180\n",
"Geocoded \"Kuwait\" to (47.4979476, 29.2733964)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Kuwait')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Kuwait\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Kyrgyzstan with timeout=180\n",
"Geocoded \"Kyrgyzstan\" to (74.724091, 41.5089324)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Kyrgyzstan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Kyrgyzstan\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Lao+People%27s+Democratic+Republic with timeout=180\n",
"Geocoded \"Lao People's Democratic Republic\" to (103.378253, 20.0171109)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', \"Lao People's Democratic Republic\")])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Lao+People%27s+Democratic+Republic\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Latvia with timeout=180\n",
"Geocoded \"Latvia\" to (24.7537645, 56.8406494)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Latvia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Latvia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Lebanon with timeout=180\n",
"Geocoded \"Lebanon\" to (35.843409, 33.8750629)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Lebanon')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Lebanon\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Lesotho with timeout=180\n",
"Geocoded \"Lesotho\" to (28.3350193, -29.6039267)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Lesotho')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Lesotho\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Liberia with timeout=180\n",
"Geocoded \"Liberia\" to (-9.3658524, 5.7499721)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Liberia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Liberia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Libya with timeout=180\n",
"Geocoded \"Libya\" to (18.1236723, 26.8234472)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Libya')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Libya\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Lithuania with timeout=180\n",
"Geocoded \"Lithuania\" to (23.7499997, 55.3500003)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Lithuania')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Lithuania\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Luxembourg with timeout=180\n",
"Geocoded \"Luxembourg\" to (6.1296751, 49.8158683)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Luxembourg')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Luxembourg\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Madagascar with timeout=180\n",
"Geocoded \"Madagascar\" to (46.4416422, -18.9249604)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Madagascar')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Madagascar\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Malawi with timeout=180\n",
"Geocoded \"Malawi\" to (33.9301963, -13.2687204)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Malawi')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Malawi\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Malaysia with timeout=180\n",
"Geocoded \"Malaysia\" to (102.2656823, 4.5693754)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Malaysia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Malaysia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Mali with timeout=180\n",
"Geocoded \"Mali\" to (-2.2900239, 16.3700359)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Mali')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Mali\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Malta with timeout=180\n",
"Geocoded \"Malta\" to (14.4476911, 35.8885993)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Malta')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Malta\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Martinique with timeout=180\n",
"Geocoded \"Martinique\" to (-61.01582685063731, 14.6367927)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Martinique')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Martinique\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Mauritania with timeout=180\n",
"Geocoded \"Mauritania\" to (-9.2399263, 20.2540382)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Mauritania')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Mauritania\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Mauritius with timeout=180\n",
"Geocoded \"Mauritius\" to (57.5703566, -20.2759451)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Mauritius')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Mauritius\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Mexico with timeout=180\n",
"Geocoded \"Mexico\" to (-100.0000375, 22.5000485)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Mexico')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Mexico\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Micronesia+%28Federated+States+of%29 with timeout=180\n",
"Geocoded \"Micronesia (Federated States of)\" to (-77.0405464, 38.9073892)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Micronesia (Federated States of)')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Micronesia+%28Federated+States+of%29\n",
"OSM returned a Point as the geometry\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Mongolia with timeout=180\n",
"Geocoded \"Mongolia\" to (103.8499736, 46.8250388)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Mongolia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Mongolia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Morocco with timeout=180\n",
"Geocoded \"Morocco\" to (-7.3362482, 31.1728205)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Morocco')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Morocco\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Mozambique with timeout=180\n",
"Geocoded \"Mozambique\" to (34.9144977, -19.302233)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Mozambique')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Mozambique\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Myanmar with timeout=180\n",
"Geocoded \"Myanmar\" to (95.9999652, 17.1750495)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Myanmar')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Myanmar\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Namibia with timeout=180\n",
"Geocoded \"Namibia\" to (17.3231107, -23.2335499)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Namibia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Namibia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Nepal with timeout=180\n",
"Geocoded \"Nepal\" to (84.0917139, 28.1083929)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Nepal')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Nepal\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Netherlands with timeout=180\n",
"Geocoded \"Netherlands\" to (5.7480821, 52.5001698)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Netherlands')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Netherlands\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=New+Caledonia with timeout=180\n",
"Geocoded \"New Caledonia\" to (164.55660583077983, -20.454288599999998)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'New Caledonia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=New+Caledonia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=New+Zealand with timeout=180\n",
"Geocoded \"New Zealand\" to (172.8344077, -41.5000831)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'New Zealand')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=New+Zealand\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Nicaragua with timeout=180\n",
"Geocoded \"Nicaragua\" to (-85.2936911, 12.6090157)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Nicaragua')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Nicaragua\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Niger with timeout=180\n",
"Geocoded \"Niger\" to (9.3238432, 17.7356214)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Niger')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Niger\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Nigeria with timeout=180\n",
"Geocoded \"Nigeria\" to (7.9999721, 9.6000359)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Nigeria')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Nigeria\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Niue with timeout=180\n",
"Geocoded \"Niue\" to (-169.861341, -19.0536414)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Niue')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Niue\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=North+Macedonia with timeout=180\n",
"Geocoded \"North Macedonia\" to (21.7168387, 41.6171214)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'North Macedonia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=North+Macedonia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Norway with timeout=180\n",
"Geocoded \"Norway\" to (9.0999715, 60.5000209)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Norway')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Norway\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Oman with timeout=180\n",
"Geocoded \"Oman\" to (57.0036901, 21.0000287)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Oman')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Oman\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Pakistan with timeout=180\n",
"Geocoded \"Pakistan\" to (71.247499, 30.3308401)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Pakistan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Pakistan\n",
"Pausing 1 seconds before making HTTP GET request\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Palestine with timeout=180\n",
"Geocoded \"Palestine\" to (35.27386547291496, 31.94696655)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Palestine')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Palestine\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Panama with timeout=180\n",
"Geocoded \"Panama\" to (-81.1308434, 8.559559)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Panama')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Panama\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Papua+New+Guinea with timeout=180\n",
"Geocoded \"Papua New Guinea\" to (144.2489081, -5.6816069)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Papua New Guinea')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Papua+New+Guinea\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Paraguay with timeout=180\n",
"Geocoded \"Paraguay\" to (-58.1693445, -23.3165935)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Paraguay')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Paraguay\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Peru with timeout=180\n",
"Geocoded \"Peru\" to (-75.0458515, -6.8699697)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Peru')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Peru\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Philippines with timeout=180\n",
"Geocoded \"Philippines\" to (122.7312101, 12.7503486)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Philippines')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Philippines\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Poland with timeout=180\n",
"Geocoded \"Poland\" to (19.134422, 52.215933)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Poland')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Poland\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Portugal with timeout=180\n",
"Geocoded \"Portugal\" to (-7.8896263, 40.0332629)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Portugal')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Portugal\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Puerto+Rico with timeout=180\n",
"Geocoded \"Puerto Rico\" to (-66.41328179513847, 18.2214149)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Puerto Rico')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Puerto+Rico\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Qatar with timeout=180\n",
"Geocoded \"Qatar\" to (51.2295295, 25.3336984)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Qatar')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Qatar\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Republic+of+Korea with timeout=180\n",
"Geocoded \"Republic of Korea\" to (127.6961188, 36.638392)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Republic of Korea')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Republic+of+Korea\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Republic+of+Moldova with timeout=180\n",
"Geocoded \"Republic of Moldova\" to (28.5670941, 47.2879608)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Republic of Moldova')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Republic+of+Moldova\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=R%C3%A9union with timeout=180\n",
"Geocoded \"Réunion\" to (55.536480112992315, -21.130737949999997)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Réunion')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=R%C3%A9union\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Romania with timeout=180\n",
"Geocoded \"Romania\" to (24.6859225, 45.9852129)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Romania')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Romania\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Russian+Federation with timeout=180\n",
"Geocoded \"Russian Federation\" to (97.7453061, 64.6863136)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Russian Federation')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Russian+Federation\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Rwanda with timeout=180\n",
"Geocoded \"Rwanda\" to (30.0644358, -1.9646631)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Rwanda')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Rwanda\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Saint+Kitts+and+Nevis with timeout=180\n",
"Geocoded \"Saint Kitts and Nevis\" to (-62.6725973, 17.250512)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Saint Kitts and Nevis')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Saint+Kitts+and+Nevis\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Saint+Lucia with timeout=180\n",
"Geocoded \"Saint Lucia\" to (-60.975036, 13.8250489)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Saint Lucia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Saint+Lucia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Saint+Vincent+and+the+Grenadines with timeout=180\n",
"Geocoded \"Saint Vincent and the Grenadines\" to (-61.2765569, 12.90447)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Saint Vincent and the Grenadines')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Saint+Vincent+and+the+Grenadines\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Samoa with timeout=180\n",
"Geocoded \"Samoa\" to (-172.1200508, -13.7693895)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Samoa')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Samoa\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Sao+Tome+and+Principe with timeout=180\n",
"Geocoded \"Sao Tome and Principe\" to (6.9648718, 0.8875498)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Sao Tome and Principe')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Sao+Tome+and+Principe\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Saudi+Arabia with timeout=180\n",
"Geocoded \"Saudi Arabia\" to (42.3528328, 25.6242618)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Saudi Arabia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Saudi+Arabia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Senegal with timeout=180\n",
"Geocoded \"Senegal\" to (-14.765340959100413, 14.46517725)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Senegal')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Senegal\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Serbia+and+Montenegro with timeout=180\n",
"Geocoded \"Serbia and Montenegro\" to (4.3701129, 50.8367275)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Serbia and Montenegro')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Serbia+and+Montenegro\n",
"OSM returned a Point as the geometry\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Seychelles with timeout=180\n",
"Geocoded \"Seychelles\" to (55.4540146, -4.6574977)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Seychelles')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Seychelles\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Sierra+Leone with timeout=180\n",
"Geocoded \"Sierra Leone\" to (-11.8400269, 8.6400349)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Sierra Leone')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Sierra+Leone\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Singapore with timeout=180\n",
"Geocoded \"Singapore\" to (103.8194992, 1.357107)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Singapore')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Singapore\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Slovakia with timeout=180\n",
"Geocoded \"Slovakia\" to (19.4528646, 48.7411522)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Slovakia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Slovakia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Slovenia with timeout=180\n",
"Geocoded \"Slovenia\" to (14.4808369, 45.8133113)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Slovenia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Slovenia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Solomon+Islands with timeout=180\n",
"Geocoded \"Solomon Islands\" to (159.1070693851845, -8.7053941)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Solomon Islands')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Solomon+Islands\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Somalia with timeout=180\n",
"Geocoded \"Somalia\" to (49.083416, 8.3676771)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Somalia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Somalia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=South+Africa with timeout=180\n",
"Geocoded \"South Africa\" to (24.991639, -28.8166236)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'South Africa')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=South+Africa\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Spain with timeout=180\n",
"Geocoded \"Spain\" to (-4.8379791, 39.3260685)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Spain')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Spain\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Sri+Lanka with timeout=180\n",
"Geocoded \"Sri Lanka\" to (80.7137847, 7.5554942)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Sri Lanka')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Sri+Lanka\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Sudan+%28former%29 with timeout=180\n",
"Geocoded \"Sudan (former)\" to (34.36023964636068, 11.79739395)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Sudan (former)')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Sudan+%28former%29\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Suriname with timeout=180\n",
"Geocoded \"Suriname\" to (-56.0771187, 4.1413025)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Suriname')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Suriname\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Sweden with timeout=180\n",
"Geocoded \"Sweden\" to (14.5208584, 59.6749712)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Sweden')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Sweden\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Switzerland with timeout=180\n",
"Geocoded \"Switzerland\" to (8.2319736, 46.7985624)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Switzerland')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Switzerland\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Syrian+Arab+Republic with timeout=180\n",
"Geocoded \"Syrian Arab Republic\" to (39.0494106, 34.6401861)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Syrian Arab Republic')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Syrian+Arab+Republic\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Tajikistan with timeout=180\n",
"Geocoded \"Tajikistan\" to (70.8156541, 38.6281733)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Tajikistan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Tajikistan\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Thailand with timeout=180\n",
"Geocoded \"Thailand\" to (100.83273, 14.8971921)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Thailand')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Thailand\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Timor-Leste with timeout=180\n",
"Geocoded \"Timor-Leste\" to (125.8375756, -8.5151979)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Timor-Leste')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Timor-Leste\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Togo with timeout=180\n",
"Geocoded \"Togo\" to (1.0199765, 8.7800265)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Togo')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Togo\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Tonga with timeout=180\n",
"Geocoded \"Tonga\" to (-175.2026424, -19.9160819)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Tonga')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Tonga\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Trinidad+and+Tobago with timeout=180\n",
"Geocoded \"Trinidad and Tobago\" to (-60.9821067, 10.8677845)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Trinidad and Tobago')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Trinidad+and+Tobago\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Tunisia with timeout=180\n",
"Geocoded \"Tunisia\" to (9.400138, 33.8439408)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Tunisia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Tunisia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Turkey with timeout=180\n",
"Geocoded \"Turkey\" to (34.9249653, 38.9597594)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Turkey')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Turkey\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Turkmenistan with timeout=180\n",
"Geocoded \"Turkmenistan\" to (59.3924609, 39.3763807)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Turkmenistan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Turkmenistan\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Uganda with timeout=180\n",
"Geocoded \"Uganda\" to (32.2166578, 1.5333554)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Uganda')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Uganda\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Ukraine with timeout=180\n",
"Geocoded \"Ukraine\" to (31.2718321, 49.4871968)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Ukraine')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Ukraine\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=United+Arab+Emirates with timeout=180\n",
"Geocoded \"United Arab Emirates\" to (53.9994829, 24.0002488)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'United Arab Emirates')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=United+Arab+Emirates\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=United+Kingdom+of+Great+Britain+and+Northern+Ireland with timeout=180\n",
"Geocoded \"United Kingdom of Great Britain and Northern Ireland\" to (-3.2765753, 54.7023545)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'United Kingdom of Great Britain and Northern Ireland')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=United+Kingdom+of+Great+Britain+and+Northern+Ireland\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=United+Republic+of+Tanzania with timeout=180\n",
"Geocoded \"United Republic of Tanzania\" to (35.7878438, -6.5247123)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'United Republic of Tanzania')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=United+Republic+of+Tanzania\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=United+States+of+America with timeout=180\n",
"Geocoded \"United States of America\" to (-100.4458825, 39.7837304)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'United States of America')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=United+States+of+America\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Uruguay with timeout=180\n",
"Geocoded \"Uruguay\" to (-56.0201525, -32.8755548)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Uruguay')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Uruguay\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Uzbekistan with timeout=180\n",
"Geocoded \"Uzbekistan\" to (63.9528098, 41.32373)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Uzbekistan')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Uzbekistan\n",
"Pausing 1 seconds before making HTTP GET request\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Vanuatu with timeout=180\n",
"Geocoded \"Vanuatu\" to (168.1069154, -16.5255069)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Vanuatu')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Vanuatu\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Venezuela+%28Bolivarian+Republic+of%29 with timeout=180\n",
"Geocoded \"Venezuela (Bolivarian Republic of)\" to (-66.1109318, 8.0018709)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Venezuela (Bolivarian Republic of)')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Venezuela+%28Bolivarian+Republic+of%29\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Viet+Nam with timeout=180\n",
"Geocoded \"Viet Nam\" to (108.4265113, 13.2904027)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Viet Nam')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Viet+Nam\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Yemen with timeout=180\n",
"Geocoded \"Yemen\" to (47.8915271, 16.3471243)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Yemen')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Yemen\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Zambia with timeout=180\n",
"Geocoded \"Zambia\" to (27.5589884, -14.5189121)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Zambia')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Zambia\n",
"Pausing 1 seconds before making HTTP GET request\n",
"Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=Zimbabwe with timeout=180\n",
"Geocoded \"Zimbabwe\" to (29.35365015971339, -19.01688)\n",
"Params:OrderedDict([('format', 'json'), ('limit', 1), ('dedupe', 0), ('polygon_geojson', 1), ('q', 'Zimbabwe')])\n",
"Preparing url: https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&polygon_geojson=1&q=Zimbabwe\n"
]
}
],
"source": [
"retrieved_geoms = []\n",
"for i, row in pasture_yield_2000.iterrows():\n",
" try:\n",
" geo_request = GeolocateAddress(query=row['Area'])\n",
" gdf = gpd.GeoDataFrame.from_features(geo_request.polygon_json, crs='epsg:4326')\n",
" geom = gdf['geometry'][0]\n",
" except:\n",
" print(row['Area'])\n",
" geom = None\n",
" retrieved_geoms.append(geom)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"pasture_yield_2000['geometry']=retrieved_geoms\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Domain Code</th>\n",
" <th>Domain</th>\n",
" <th>Area Code (FAO)</th>\n",
" <th>Area</th>\n",
" <th>Element Code</th>\n",
" <th>Element</th>\n",
" <th>Item Code (FAO)</th>\n",
" <th>Item</th>\n",
" <th>Year Code</th>\n",
" <th>Year</th>\n",
" <th>Unit</th>\n",
" <th>Value</th>\n",
" <th>Flag</th>\n",
" <th>Flag Description</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>295</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((60.5176034 34.0617567, 60.533076 34....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>3</td>\n",
" <td>Albania</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>219</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((19.0009866 40.5021742, 19.0009956 40...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>4</td>\n",
" <td>Algeria</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>240</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((-8.668908 28.240942, -8.668842 28.21...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>7</td>\n",
" <td>Angola</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>285</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>(POLYGON ((11.4609793 -16.5543239, 11.4627429 ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>8</td>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>224</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>(POLYGON ((-62.5536517 16.8993134, -62.552848 ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Domain Code Domain Area Code (FAO) Area \\\n",
"0 QL Livestock Primary 2 Afghanistan \n",
"1 QL Livestock Primary 3 Albania \n",
"2 QL Livestock Primary 4 Algeria \n",
"3 QL Livestock Primary 7 Angola \n",
"4 QL Livestock Primary 8 Antigua and Barbuda \n",
"\n",
" Element Code Element Item Code (FAO) Item Year Code \\\n",
"0 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"1 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"2 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"3 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"4 5420 Yield 919 Hides, cattle, fresh 2000 \n",
"\n",
" Year Unit Value Flag Flag Description \\\n",
"0 2000 hg/An 295 Fc Calculated data \n",
"1 2000 hg/An 219 Fc Calculated data \n",
"2 2000 hg/An 240 Fc Calculated data \n",
"3 2000 hg/An 285 Fc Calculated data \n",
"4 2000 hg/An 224 Fc Calculated data \n",
"\n",
" geometry \n",
"0 POLYGON ((60.5176034 34.0617567, 60.533076 34.... \n",
"1 POLYGON ((19.0009866 40.5021742, 19.0009956 40... \n",
"2 POLYGON ((-8.668908 28.240942, -8.668842 28.21... \n",
"3 (POLYGON ((11.4609793 -16.5543239, 11.4627429 ... \n",
"4 (POLYGON ((-62.5536517 16.8993134, -62.552848 ... "
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pasture_yield_2000.head()"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"pasture_yield_2000 = pasture_yield_2000[pasture_yield_2000['geometry']!=None]"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"pasture_yield_2000 = pasture_yield_2000.set_geometry('geometry')"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"pasture_yield_2000 = pasture_yield_2000[pasture_yield_2000['geometry'].apply(lambda x : x.type!='Point' )]"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-97-9f5cd11be15e>:1: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
" pasture_yield_2000.to_file(\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.shp\", driver='ESRI Shapefile')\n"
]
}
],
"source": [
"pasture_yield_2000.to_file(\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.shp\", driver='ESRI Shapefile')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Domain Cod</th>\n",
" <th>Domain</th>\n",
" <th>Area Code</th>\n",
" <th>Area</th>\n",
" <th>Element Co</th>\n",
" <th>Element</th>\n",
" <th>Item Code</th>\n",
" <th>Item</th>\n",
" <th>Year Code</th>\n",
" <th>Year</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>295</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((60.51760 34.06176, 60.51934 34.06627...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>3</td>\n",
" <td>Albania</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>219</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((19.00099 40.50217, 19.00099 40.50396...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>4</td>\n",
" <td>Algeria</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>240</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((-8.66891 28.24094, -8.66891 28.30392...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>7</td>\n",
" <td>Angola</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>285</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>MULTIPOLYGON (((11.46098 -16.55432, 11.46166 -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>8</td>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>224</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>MULTIPOLYGON (((-62.55365 16.89931, -62.51644 ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Domain Cod Domain Area Code Area Element Co \\\n",
"0 QL Livestock Primary 2 Afghanistan 5420 \n",
"1 QL Livestock Primary 3 Albania 5420 \n",
"2 QL Livestock Primary 4 Algeria 5420 \n",
"3 QL Livestock Primary 7 Angola 5420 \n",
"4 QL Livestock Primary 8 Antigua and Barbuda 5420 \n",
"\n",
" Element Item Code Item Year Code Year Unit Value \\\n",
"0 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 295 \n",
"1 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 219 \n",
"2 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 240 \n",
"3 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 285 \n",
"4 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 224 \n",
"\n",
" Flag Flag Descr geometry \n",
"0 Fc Calculated data POLYGON ((60.51760 34.06176, 60.51934 34.06627... \n",
"1 Fc Calculated data POLYGON ((19.00099 40.50217, 19.00099 40.50396... \n",
"2 Fc Calculated data POLYGON ((-8.66891 28.24094, -8.66891 28.30392... \n",
"3 Fc Calculated data MULTIPOLYGON (((11.46098 -16.55432, 11.46166 -... \n",
"4 Fc Calculated data MULTIPOLYGON (((-62.55365 16.89931, -62.51644 ... "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#open the yield of pasture globally\n",
"default_pasture_yield_2000 = gpd.read_file(\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.shp\")\n",
"default_pasture_yield_2000.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The units of measure of this yield are hectograms per animal (hg/an) and we need to get the info in tonnes per hectare. For that we use the livestock info from FAOSTAT to get the information of number of livestock units (annimals) per hectare (link [here](http://www.fao.org/faostat/en/#data/EK)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Domain Code</th>\n",
" <th>Domain</th>\n",
" <th>Area Code (FAO)</th>\n",
" <th>Area</th>\n",
" <th>Element Code</th>\n",
" <th>Element</th>\n",
" <th>Item Code</th>\n",
" <th>Item</th>\n",
" <th>Year Code</th>\n",
" <th>Year</th>\n",
" <th>Unit</th>\n",
" <th>Value</th>\n",
" <th>Flag</th>\n",
" <th>Flag Description</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>EK</td>\n",
" <td>Livestock Patterns</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>7213</td>\n",
" <td>Livestock units per agricultural land area</td>\n",
" <td>866</td>\n",
" <td>Cattle</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>LSU/ha</td>\n",
" <td>0.05</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>EK</td>\n",
" <td>Livestock Patterns</td>\n",
" <td>3</td>\n",
" <td>Albania</td>\n",
" <td>7213</td>\n",
" <td>Livestock units per agricultural land area</td>\n",
" <td>866</td>\n",
" <td>Cattle</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>LSU/ha</td>\n",
" <td>0.38</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>EK</td>\n",
" <td>Livestock Patterns</td>\n",
" <td>4</td>\n",
" <td>Algeria</td>\n",
" <td>7213</td>\n",
" <td>Livestock units per agricultural land area</td>\n",
" <td>866</td>\n",
" <td>Cattle</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>LSU/ha</td>\n",
" <td>0.03</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EK</td>\n",
" <td>Livestock Patterns</td>\n",
" <td>5</td>\n",
" <td>American Samoa</td>\n",
" <td>7213</td>\n",
" <td>Livestock units per agricultural land area</td>\n",
" <td>866</td>\n",
" <td>Cattle</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>LSU/ha</td>\n",
" <td>0.01</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>EK</td>\n",
" <td>Livestock Patterns</td>\n",
" <td>7</td>\n",
" <td>Angola</td>\n",
" <td>7213</td>\n",
" <td>Livestock units per agricultural land area</td>\n",
" <td>866</td>\n",
" <td>Cattle</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>LSU/ha</td>\n",
" <td>0.04</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Domain Code Domain Area Code (FAO) Area \\\n",
"0 EK Livestock Patterns 2 Afghanistan \n",
"1 EK Livestock Patterns 3 Albania \n",
"2 EK Livestock Patterns 4 Algeria \n",
"3 EK Livestock Patterns 5 American Samoa \n",
"4 EK Livestock Patterns 7 Angola \n",
"\n",
" Element Code Element Item Code \\\n",
"0 7213 Livestock units per agricultural land area 866 \n",
"1 7213 Livestock units per agricultural land area 866 \n",
"2 7213 Livestock units per agricultural land area 866 \n",
"3 7213 Livestock units per agricultural land area 866 \n",
"4 7213 Livestock units per agricultural land area 866 \n",
"\n",
" Item Year Code Year Unit Value Flag Flag Description \n",
"0 Cattle 2000 2000 LSU/ha 0.05 Fc Calculated data \n",
"1 Cattle 2000 2000 LSU/ha 0.38 Fc Calculated data \n",
"2 Cattle 2000 2000 LSU/ha 0.03 Fc Calculated data \n",
"3 Cattle 2000 2000 LSU/ha 0.01 Fc Calculated data \n",
"4 Cattle 2000 2000 LSU/ha 0.04 Fc Calculated data "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pasture_per_hectare_2000 = pd.read_csv('../../datasets/raw/crop_data/default_pasture/FAOSTAT_data_6-23-2021_livestock_per_ha.csv')\n",
"pasture_per_hectare_2000.head() "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Domain Cod</th>\n",
" <th>Domain</th>\n",
" <th>Area Code</th>\n",
" <th>Area</th>\n",
" <th>Element Co</th>\n",
" <th>Element</th>\n",
" <th>Item Code</th>\n",
" <th>Item</th>\n",
" <th>Year Code</th>\n",
" <th>Year</th>\n",
" <th>Unit</th>\n",
" <th>Value</th>\n",
" <th>Flag</th>\n",
" <th>Flag Descr</th>\n",
" <th>geometry</th>\n",
" <th>animal_ha</th>\n",
" <th>yield_t_ha</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>295</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((60.51760 34.06176, 60.51934 34.06627...</td>\n",
" <td>0.05</td>\n",
" <td>0.001475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>3</td>\n",
" <td>Albania</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>219</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((19.00099 40.50217, 19.00099 40.50396...</td>\n",
" <td>0.38</td>\n",
" <td>0.008322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>4</td>\n",
" <td>Algeria</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>240</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((-8.66891 28.24094, -8.66891 28.30392...</td>\n",
" <td>0.03</td>\n",
" <td>0.000720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>7</td>\n",
" <td>Angola</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>285</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>MULTIPOLYGON (((11.46098 -16.55432, 11.46166 -...</td>\n",
" <td>0.04</td>\n",
" <td>0.001140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>8</td>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>224</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>MULTIPOLYGON (((-62.55365 16.89931, -62.51644 ...</td>\n",
" <td>0.88</td>\n",
" <td>0.019712</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Domain Cod Domain Area Code Area Element Co \\\n",
"0 QL Livestock Primary 2 Afghanistan 5420 \n",
"1 QL Livestock Primary 3 Albania 5420 \n",
"2 QL Livestock Primary 4 Algeria 5420 \n",
"3 QL Livestock Primary 7 Angola 5420 \n",
"4 QL Livestock Primary 8 Antigua and Barbuda 5420 \n",
"\n",
" Element Item Code Item Year Code Year Unit Value \\\n",
"0 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 295 \n",
"1 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 219 \n",
"2 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 240 \n",
"3 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 285 \n",
"4 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 224 \n",
"\n",
" Flag Flag Descr geometry \\\n",
"0 Fc Calculated data POLYGON ((60.51760 34.06176, 60.51934 34.06627... \n",
"1 Fc Calculated data POLYGON ((19.00099 40.50217, 19.00099 40.50396... \n",
"2 Fc Calculated data POLYGON ((-8.66891 28.24094, -8.66891 28.30392... \n",
"3 Fc Calculated data MULTIPOLYGON (((11.46098 -16.55432, 11.46166 -... \n",
"4 Fc Calculated data MULTIPOLYGON (((-62.55365 16.89931, -62.51644 ... \n",
"\n",
" animal_ha yield_t_ha \n",
"0 0.05 0.001475 \n",
"1 0.38 0.008322 \n",
"2 0.03 0.000720 \n",
"3 0.04 0.001140 \n",
"4 0.88 0.019712 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"animal_ha_list = []\n",
"yield_t_ha_list = []\n",
"for i, row in default_pasture_yield_2000.iterrows():\n",
" location = row['Area']\n",
" yield_hg_an = row['Value']\n",
" try:\n",
" animal_ha = pasture_per_hectare_2000[pasture_per_hectare_2000['Area']==location].iloc[0]['Value']\n",
" \n",
" except:\n",
" animal_ha = 0\n",
" # 0.0001 converts hg to tonnes\n",
" yield_t_ha = float(yield_hg_an) * 0.0001 * float(animal_ha)\n",
" animal_ha_list.append(animal_ha)\n",
" yield_t_ha_list.append(yield_t_ha)\n",
" \n",
"##append to main geodataframe\n",
"default_pasture_yield_2000['animal_ha'] = animal_ha_list\n",
"default_pasture_yield_2000['yield_t_ha'] = yield_t_ha_list\n",
"default_pasture_yield_2000.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#save file\n",
"default_pasture_yield_2000.to_file(\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.shp\", driver='ESRI Shapefile')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"#convert file to raster with same extent and resolution that the pasture harvset area\n",
"!gdal_rasterize -l Pasture2000_5m_yield -a yield_t_ha -tr 0.083333 0.083333 -a_nodata 0.0 -te -180.0 -90.0 180.0 90.0 -ot Float32 -of GTiff \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.shp\" \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.tif\""
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"#get the generated yield just for those areas whete the harvest area is greater than 0\n",
"# 1. produce a harvest area pasture with just 1 values\n",
"default_pasture_ha_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_ext_v2.tif\"\n",
"!gdal_calc.py -A $default_pasture_ha_2000 --outfile=\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_normalised_val_v2.tif\" --calc=\"(A>0)\" --NoDataValue -3.402823e+38 "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#multiply pasture yield with the normalised raster\n",
"!gdal_calc.py -A \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_normalised_val_v2.tif\" -B \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.tif\" --outfile=\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v4.tif\" --calc=\"A*B\" --NoDataValue -3.402823e+38 "
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.402823e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"default_pasture_y_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v4.tif\"\n",
"a = rasterio.open(default_pasture_y_2000)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Explore cotton data\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"cotton_2000_ha_4326 = '../../datasets/raw/crop_data/cotton/cotton_HarvestedAreaFraction.tif'\n",
"cotton_2000_y_4326 = '../../datasets/raw/crop_data/cotton/cotton_YieldPerHectare.tif'"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'blockxsize': 432, 'blockysize': 224, 'tiled': True, 'compress': 'packbits', 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(cotton_2000_ha_4326)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'blockxsize': 432, 'blockysize': 224, 'tiled': True, 'compress': 'packbits', 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(cotton_2000_y_4326)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Explore rubber data"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"rubber_2000_ha_4326 = '../../datasets/raw/crop_data/rubber/rubber_HarvestedAreaFraction.tif'\n",
"rubber_2000_y_4326 = '../../datasets/raw/crop_data/rubber/rubber_YieldPerHectare.tif'"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'blockxsize': 432, 'blockysize': 224, 'tiled': True, 'compress': 'packbits', 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(rubber_2000_ha_4326)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'blockxsize': 432, 'blockysize': 224, 'tiled': True, 'compress': 'packbits', 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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FtyaOsquri4Nj47x49hjvTJ2nEQQUUyn++M2X+MsT38awcb+5KdXkRw3+v+N/wg9Hn2dbfgTbcpiuT3B07hCRiS+hmHPjcfdj1THGq1OLhmA2k7xjOZfMjNlczxa7taw5nj/rpnlv33tx23gEnCb9DUhE+NDgR3hn5h2m6mPs630fv/nhX+LgxAT9uSKd2TQXSmXOl8tMVqtMVCqICCMdW9tu8ih1czpZOsp3T7zGVHUWQUjZGeaDMi+NvcRo5TRz/iy22EhyHKCY6ljyalcFL4tr2WSc+ByA5vIwighN2Ho+Ywwp2+OTI59kKLttyZjahSb9DSrr5Hmg/wOU/TIiwm3Fu/knH3iMtydG6cvmEBHma3V8/91RDO36dVXdfDJOll+652+TcdOUgzkEyNhZdnbspOyXcMRtDUnuz/Szo2MEz3bJOCmKqQIdqTxd6Q66011k3cyiMs7Cnn5nKs8tHVu5r/9u/tYtn6I71df2I910yOYGlrLT7Oy4tXX/7u593NP/HN88/Bbw7tC0IIh7NM2JqZZijMGPGlTDCmk7owd91brakt3OcO4W9hbvpOKXsMSmK9XL7k5hqjYRXxUrCpGkRFNwC4wUtlHwCuzu3MvZ+VPUwzqCUHAL2GKTdtKUGiVqQZ3+bB8jhVu4pbCLtJ3RDtECmvRvIpZY7CpuJ4p+QjrlUavHoxiayf8vDr/EI9s/Sd7tuOR3nzv/A/7Dm39JqV7nV/b/HT44+NNrHb5SLc3eds7Jk3PenTOnWdJJViIwAZaxsMRmW2E7W7Pb8ew0ds6iHJQYq16gmCrynp77SNtZJmrnscSiO9Wvif4ydKvcZDJOFsexsUWwbbs1VjmXTnGhVObJt7+6qJYJ8TVFnz31HGPz8+zp7ube3vvXKXqlrqzgFulJ9S76DJf8OV6feJ2nD/0ZJX8WSywKXpFOr4uUnWJ7fidZJ08Q+ZydP8NkbWJZCX8jD1dfTZr0bzI/M/yz/Ov/7lfoz+WAdw9c+VGEZQnPHHubPz32dabrE0zXJzg08ybfO/cdTs3Nsq2jg/sGbsOzLp34SqmNwBKLnFsgY2dabW9OvsV3T7zCZLXMs2efJYwCakGFyETk3UJrLqpyMMep8mkGsluu+jyNqEHJnyW8wpm9m5WenHWTqgYVRitneOrA07x6fpRaw2drsRMR4czMLLmURz0M8f2AYjbDZ+/4MPf0vIcdhT1tdT1QdXOaaUwxUb2AiMXB6YM8e/JFqkHAPf0jNCKfqeost/fs5OdHPk01nKfo9SAIkQmX9fkeq57j9YnX+MDAhyhswpk2r3Rylv7136QyTpadHbfyfz7wq5yrnOZH53/ED069wVS1Slc2w5ZCgYMTExhjyLous/X4YJkmfHUzCKMA1/boTvXiWi5vThzi0NQYL5w70lpntn6AR7b/HFk7x0xjkk6ve1mf78hEzAfxTJ4h7Xcyo2aAm5xjuWzP72Tbrh38rZFHuVAdpcvrJu928PbMT/idV/6A/UO7cCybseoFtuZuWXJec6U2kk6vi06vC8dy8aMGjTC4pAY/U6vxzJnv8Okdf5f5oMxE7TwDmeGrPrbBMF4dZ6RjhA63uEqvYOPSpL9JiAhZJ8+Owp5W293d+/iND/by+uTr1MM6uztv1YSvVlUQBYxVz5Gy0/Sk+6/7cRzLbd12LY+sm150clZPJkPO9fBsr3UyVxAF9KT6F/3uUmyx2dN5K+cr56gEZdJ2Blucthm/r0l/k9uS205/ZguRCTXhq1XlRw2ePvIHHJ0+zXv69vKJ7Z8k42Rv+HGzTp6Pbf8p8t4r2GLzk/Hj3NW7g+50kblGib8e/WsGsv10pboIIv+qSR/As9MUk/XHGjP0Z+ILFrUDTfptIJ5/RN/qy6kFFeb8GVzLI2NnsSwHRxwd532NHHEZzPaTd3PsLd7KfDC3IklfELpTPbx/cD+7O/bywOA7HJh6Gz8KCKIA10m/+14to7dujMEVl2wy37+IMF47T1eql/SCUUOblX6qVVurhzXOVeKx3ecr5xitnqUSlKkEZYBFs5ZGJqIWVqkE8zSipaf3vV7GGGph9aYeO96cMyoyEcO57XSnrr+8s+hxESITsavjVnJugfd038/9ffvwI5+cm2VrfphiqshAZnBZw5EbUR3Hcsk7BU6Wj7Z6+Bef37JZafdPtTXX8ujwOpiuB6TsNGW/RC2oYls2b06+hWs5DOeH2dmxB0GYbUxT8ucoel1syW5fsTpwI6ozVh1lILPlpp4iI+vkscXmrak32N//AbwVuBShwXBu/ixbc7e02nZ27KUvM8iZ8knSdpqt+ZFl9dKDKMCPGnhWChGbLq+bifoYnW4RQWv6AIjIk8AngTFjzF1JWzfwR8AIcAL4jDFmWuK/gN8BHgEqwN83xrya/M5jwD9JHvY3jTFPrexLUeraNU/ZzzsdpJ0s0/UJTpSOUa1X+fbxlzlXKuHa8RnQI8Ui//3eR0gnO4eICJuVmdXUtTw8y6PkzyYJ6eZNQPf03sPR2aM4sjJ9ykZY46/PvkBPuoe9xbuA+GBsh1ukP1Oj6PUs63hVEPlUwwp5p6O1fQtuJ2kniyCcLh9nd8dtm35Y83LKO18FHr6o7YvAs8aYPcCzyX2ATwB7kp/Hga9AayfxJeB9wAPAl0Sk60aDV2olWGKRTmrPBbfIjo7dZN0snm0ny2He95moVKiFdQITEJoQk5R+miWgelhb9kVs5oMyo5XTlPxZjDFcqJ7lVPkU78wc5NT8sZv6Yji35Hfx/oEPYomVjIkv31DZyrIc7uu/k2dO/WDRGbSWWPSlh65hgIIsSvjGGKphhXPzp3l14iVOlU9Rjy692Ppmc9Wkb4z5K2DqouZHgWZP/SngUwvav2ZizwNFERkCPg48Y4yZMsZMA89w6Y5EqXUV1+wrBFGDrJOlO52hmE4TRoaC53F77xBFrxNJ/kUYjDFUgjLzyXGAeX+OWljFjxpLJu5m+9G5Q5wqnwRjOFc5xWjlHFtyW7DE4nT5NOO10Zu2vm+J1Zr0L4h8TpePMx9cfgbYq/Esj48Of5xbu3bwysSL1MJqa9lyvxFFJqK+4PfitpC0nWWksJswChkpjLTF9Siu90DugDFmNLl9HhhIbg8DpxesdyZpu1z7JUTkcRF5WUReHh+fuM7wlLp2llh4VgpbHIZzt/Dzux6iL5ulP5djS6HAQLYbANdyybo5wihonR+RttLY4uAbn3m/hB818KMGoQkZr462kv3x0mFK/gwpK0Wn18l0Y4rzlVFKjRLzfpnB7BD9mT5yTmGdt8bKcCyXrblbyNjXPoqnEdZbO07Hcvjw0IOMV8c5MP3GNR1IN8YwVR/jeOkIs40pQhMSmQgDpO0MruVxd889DN7kx1OW64aLV8YYIyIr1iUxxjwBPAHx3Dsr9bhKLYdnp3Asl1pYpRrW+Mzeh9nTuRfP8giJECBlZ0hZ6VYv0xILkhJRNZhPJsDzmQ9LGAwHpt/i3t776XS76E33Uw0qNJIdQqdXpDNVxLZshnPbydjZJCmFhCZcsbr4elnY679W040JputT9KR66U0PkrJS7OzYQT2qM1Y9x9bcyFUfI4gCqkGZ0co5IhMx58+SdQvxdXebl1JE6E3H/dbmyV+b2fV+oi6IyJAxZjQp34wl7WeBhdci25q0nQUevKj9B9f53EqtKksssk6ODw585JrG6nuWh+u6yeX6AibCChk7x709+yi48WR4nW4XoRPSlerFJOWhLq8Hx3KxktKCIxYQDyNs9nQF2fTJ6GK96UEE4VzlLJbYFFM99KR7OVc5S7Cgtt8sg4UmXHS93CDyOVU+TiWYx4980naawcxwa1bOixljMJhNP4rness73wAeS24/Bvz5gva/J7H3A7NJGejbwEMi0pUcwH0oaVNqw7qek7Oaidm2HAYyw+TdDgpesVUrti0Hx3JJ2Wlcif/37Lg01HzOpX4Mpm3GkbcYQ0+qn950H6fKJzg3f4qCW6TD7aQe1vAXlHhEpJXwIxMxVj3H0blD8fQMJp63JzABhisXDyyxCJNvWZvVcoZs/iFxL71XRM4Qj8L5MvB1EflF4CTwmWT1bxIP1zxCPGTzcwDGmCkR+afAS8l6v2GMufjgsNoAmnXodjgzcS1YYhFGARFRaycSmbCVfBb14M27vdZmj7O5LDJh65tAu7Ath8hE9KYHMBjGqhco+XMMZre0TpRzLa+1jUITcr5yBs/yOF8dbX1Levdi6SFzjWl6klLOwikbGmENz06DgZI/Sy4532AzumrSN8b8wmUWfWyJdQ3w+cs8zpPAk9cUnVoRzQtMX00Q+Txz5lvMNea4v28fg5kt5JOyhLp2834Jz04tStaNsB4nE2MQsRZt2+aEYgaz6P2K3z+7LaeFsMRCxKIr1ctkbYJaWON85Rw96V48K7WoBh9GAZP1iVbpp3kmL4Bt2aSsFDm3gDER5aCUJP14x+JZqfgblTEYEyFYS74Xm8HmejVqSZWgvKwx5Beq5zhbHmW2Xua/nf0hb88cIDSrf2UhY8w1jXG/GQRRQGRCgiguLQhC2Z9jujFBYOKe/1K1YxFpJZ+maBOXGpbDFZeUleaWwk4A6mGd2cZM6zyJpubUChB/pi6eQiNlp8nYuaT271LwiriWR8HtJOfGvxcRJeU2m4iISlBmvDrKdLIzaY78uZlp0m8DWSff6slcyanyydbtvmw3ezpvbdWaV1NoQsZr5wkif9Wfa61YYuFaHoaIuFhjGKueZ96fpxZWEKy4zHOZsfjNg7zx0MLN19u8FiJCYHwKbicFt0BkIsp+mQu1UaphpVXbF4Sh7NZFZZnmNaRTdoqt+RFEhPmgxPG5w5wpn4iTOHGnI4gCLCxciQ/GxyU1i1l/llPlE635mPyowUxjirI/d8UdwEbdOdzc48HUsiw3YdzaeRvn5kepBFV2d+4i6+TXpLRTDuaohTV84+MYd1MkOEssHMujFlaohVUyTo6uVDfT9Skk6Wstle5bdegk0UdRuOlHkyxH2s4QRAGDmS3MB/OEUchUbRI/bNCb7ifj5LDFxrNTFFNdVIMKfuRjMARRPK+SI3EpZ6o+SdpOE5qAlJXGs7x4B0tEaAIssZj3S4hYZOwcW7LbGK2coeTPknaynKucZqY+DUDGyVD0uuhK9eJa3qLP7sL3LTRh66S+9S6XatJXLWk7QyWocn/ffdzRdc8NP1588pJ1xSQembjM0Z8exLW8ZR9/uBk4loMdOdTCCk7kkrGzTJrxZCROXDZrlnLi7RCXF5rbRCwhIrppz8xdaY7lkHc7Gcnv5ET5GGEUUgkr5NxCfBGUJKFuz+0kTEbqhCZksjZGwe1sfa56Ur34UYNqWKXkz9Jl9cbLDFSDeVJOFsdyk1E8AWk7Q6fXSS2scWzuENWg2to5V4Nqa7SPZ3mknSwZO4djLb4oiy126/Ec3HVN/Jr0VUvazvDQtp+l072xaZGaB9eWM3GVJRaOOJwsH2NXx14c+8ZnZdxIMk42LjeIYImwJbcNA616dGAChGavULDEJowCjAiNqI4gm2YnuBJEhE6vm10dDqfKx5Md5OKOhYi0pkt2geEFs3NCPLldaEL604OU/NlWGccW+7IXSe9JxSOI4p1FhcnaOHON2VYZzmBoRA0ajQZVa57uVN+i0qjBUA3mcSwXR9x1PQlMk75qaY4tvxGRiXuq13ImqWO5jBR2bdga6I3y7FTrW09c54+/BRkMYdTAwsKzUxgTxROKiRBEfvwtiXj0inqXiFBwO9ndcRuNqM58MIctDhk72xrmeaUdZSqZesG1vEUnxV1O83M5H5TIOQVSdpqC28nxuXeY8+cuSd5+5DPTmCJjZ3HtFI2w1hoG3ZzmoRHWscRedDLZWtGkr1aUJdY116DbYb6T5rceK5lfvkF8QFBYMJbfhK1yjiAYE2GE1s5gs0/5e61SySib8dp5JmrjZOwMebdANaxiiUXR627NrbOwlu5YDsbYi74RNF3cA19YokzZmdbOxBabYqqbkl9a9HkXBJMcGE5ZKVxx8dxLv73G3zZWf2TcUvRTpFZMs0cUmgB3BS6ecTn1sHbTzzlvi41YHhYWtuUQmhA/9GHBVACOOMRjS+LatK1/rpdwLJfBzFZ60wOtYbIGQyWoMNOYwhWXTq+rdRJWXHtPEUSN1k60OWOqjYWB+NtVkrxDE+Im3wQWTt9QD2tUgwoDmUHm/Nn4/bIcCm589nU1mKcSzpOyM0tO/bzUDmet6KdIXZfmMDlL7NZJMBFRnMQuKu3EJZ9w0QWrm1MSO5Z7TQk8MhElf4ae1MDVV97AbLEXDy1Mevat+xInImBZw23bmYjgSlyuAVpj7psjcuID5WHrMomNKC63EMU7DYOhHlQuORFRkMvO1Z+y02zNxUNAi0E5Hu4pFhk7m0wJ4RI1ppjzpylQxBFnWaWntaBJX12zeG7yGuO1CwxktpC2M/HohCjAT0Y7NJX9OarBPAZDzu0g5+QpNWaYD8o8e+Z75L0c7+17L1ty25f13JZY9KQGbupe/lIWHrA1C9ouPgFJLZ+IxFc2EwCnlcD9qEGUTLsgCJWgjHXRaJvlPj7E58FczBabTq+bWlChEdUxlkHMlUeyrRVN+uq6WGKxPbdjUZ3ZsVz8qEE9qrUuUH1m/iS22PSk+7CIr6QUmICck+fRHZ8C3j0Raak/uqXaN1vCh/g1paw0JCcF+VEjPrFrE77W9dY8iAvx52u1zkexxV70rWOjfG416atrFk89fGnvJi7t2PG8JUkiH8xsISL+ZlBwO+ODkiak4HYuKvcsJTIRk7ULZJ38mp0otp6aO1Bb7HhYXzKev3msZCP0EjebtfpMbaTPrn6K1IpZeHCqEdZbYxpK/hzzfjm+rmzSVg9rV328yERM1MYZrZ6l0QbXLl2oOQdP8yxPPStXrRTt6asVFSd+h0ZUZ9afZqY+TZSUKU6Xj9OV6mamPk3dqVGPatjiYCHk3I54fP+icpHD9sJOKhcNi2s32sNXK0k/TWpV+FGD2cYMtbCKlfybD+ZxLY+0k6EaVJiqTfB7P/l9jpYOM1m7wGjl9CWPk7Gz5NwOrW0rtUK0p69WRd7tIG1nmG1MIUgyf0mRalBBEOb8OSpBlY5UnqOzx9hZ2NO6uMVCQeQnQzT71+FVKLX5aNJXq8IWG9uOh62FJkCwePbsd3ln+gQ/NfwAR2aP4Vkuc/Uyc/Uynd7zPLjlY5eUMkITUmrM0ZseXKdXotTmouUdtapSdpqskyfjZNlT3M1kdY4js8coeh14tkdPpou0k+LIzHEm62OcKh9b9Pv1sEox1b1pL12n1FrTnr5aMyfmTvLW+Dh5N81c4yh39e7g6MwZBnPdfHTbRzg+d4wDU4f4zO7e1u8ExsezNv/cPEqtFU36ak0YY/juyRc5cXyU+wYHma5W+ebR1wE4MD7OvF/ll+/5PPf1vhdbHEr+THwmozH0pDrXN3ilNhFN+mpNiAh//85PM5jrZG/3TsyA4fD0SaZrJd43dDe1ZPrZnB2fwSjE86anrPSGOrFFqZudJn21Zu7p2c/ezjt46tDXuLvndgazA9SCGvv63hufsp5c1BqgmOpZx0iV2rw06as15dlpfm7kYfrSg3hWCj9q8PzYcwxkBthbvGu9w1Nq09Okr9aUJRZbcyOt+56d4o6uu+jwbuwSjUqp5dGkr9Zd7xInZSmlVoeO01dKqTaiSV8ppdqIJn2llGojmvSVUqqNXDXpi8iTIjImIm8uaPt1ETkrIq8nP48sWParInJERA6JyMcXtD+ctB0RkS+u/EtRSil1Ncvp6X8VeHiJ9n9ljLk3+fkmgIjcAXwWuDP5nX8rIraI2MC/AT4B3AH8QrKuUkqpNXTVIZvGmL8SkZFlPt6jwNPGmDpwXESOAA8ky44YY44BiMjTyboHrj1kpZRS1+tGavpfEJE3kvJP88yaYWDh5Y/OJG2Xa1dKKbWGrjfpfwXYBdwLjAL/YqUCEpHHReRlEXl5fHxipR5WKaUU15n0jTEXjDGhMSYCfp93SzhngW0LVt2atF2ufanHfsIYs98Ys7+vr3epVZRSSl2n60r6IjK04O6ngebInm8AnxWRlIjsAPYALwIvAXtEZIeIeMQHe79x/WErpZS6Hlc9kCsifwg8CPSKyBngS8CDInIvYIATwC8BGGPeEpGvEx+gDYDPG2PC5HG+AHwbsIEnjTFvrfSLUUopdWVijFnvGC7r/v37zA9feG69w1BKqZtKxsm9YozZv9QyPSNXKaXaiCZ9pZRqI5r0lVKqjWjSV0qpNqJJXyml2ogmfaWUaiOa9JVSqo1o0ldKqTaiSV8ppdqIJn2llGojmvSVUqqNaNJXSqk2oklfKaXaiCZ9pZRqI5r0lVKqjWjSV0qpNqJJXyml2ogmfaWUaiOa9JVSqo1o0ldKqTaiSV8ppdqIJn2llGojmvSVUqqNaNJXSqk2oklfKaXaiCZ9pZRqI5r0lVKqjWjSV0qpNqJJXyml2ogmfaWUaiNXTfoisk1Evi8iB0TkLRH55aS9W0SeEZHDyf9dSbuIyO+KyBEReUNE9i14rMeS9Q+LyGOr97KUUkotZTk9/QD4X40xdwDvBz4vIncAXwSeNcbsAZ5N7gN8AtiT/DwOfAXinQTwJeB9wAPAl5o7CqWUUmvjqknfGDNqjHk1uV0C3gaGgUeBp5LVngI+ldx+FPiaiT0PFEVkCPg48IwxZsoYMw08Azy8ki9GKaXUlV1TTV9ERoD7gBeAAWPMaLLoPDCQ3B4GTi/4tTNJ2+XaL36Ox0XkZRF5eXx84lrCU0opdRXLTvoikgf+BPiHxpi5hcuMMQYwKxGQMeYJY8x+Y8z+vr7elXhIpZRSiWUlfRFxiRP+fzLG/GnSfCEp25D8P5a0nwW2Lfj1rUnb5dqVUkqtkeWM3hHg3wNvG2P+5YJF3wCaI3AeA/58QfvfS0bxvB+YTcpA3wYeEpGu5ADuQ0mbUkqpNeIsY50PAf8D8BMReT1p+z+ALwNfF5FfBE4Cn0mWfRN4BDgCVIDPARhjpkTknwIvJev9hjFmaiVehFJKqeW5atI3xjwHyGUWf2yJ9Q3w+cs81pPAk9cSoFJKqZWjZ+QqpVQb0aSvlFJtRJO+Ukq1EU36SinVRjTpK6VUG9Gkr5RSbUSTvlJKtRGJh9VvTCJSAg6tdxyX0Qts1BnhNLZrt1HjAo3tem3U2NYirluMMX1LLVjOGbnr6ZAxZv96B7EUEXlZY7t2GzW2jRoXaGzXa6PGtt5xaXlHKaXaiCZ9pZRqIxs96T+x3gFcgcZ2fTZqbBs1LtDYrtdGjW1d49rQB3KVUkqtrI3e01dKKbWCNOkrpVQb2bBJX0QeFpFDInJERL64TjGcEJGfiMjrIvJy0tYtIs+IyOHk/66kXUTkd5N43xCRfSscy5MiMiYiby5ou+ZYROSxZP3DIvLYUs+1AnH9uoicTbbb6yLyyIJlv5rEdUhEPr6gfcXfbxHZJiLfF5EDIvKWiPxy0r6u2+0Kca37dhORtIi8KCI/TmL7v5L2HSLyQvI8fyQiXtKeSu4fSZaPXC3mVYjtqyJyfMF2uzdpX7O/g+QxbRF5TUT+S3J/3bfZkowxG+4HsIGjwE7AA34M3LEOcZwAei9q+y3gi8ntLwL/d3L7EeC/El9w5v3ACyscy0eAfcCb1xsL0A0cS/7vSm53rUJcvw78b0use0fyXqaAHcl7bK/W+w0MAfuS2wXgnSSGdd1uV4hr3bdb8trzyW0XeCHZFl8HPpu0/x7wPye3/wHwe8ntzwJ/dKWYVym2rwJ/Z4n11+zvIHnc/wX4A+C/JPfXfZst9bNRe/oPAEeMMceMMQ3gaeDRdY6p6VHgqeT2U8CnFrR/zcSeB4qSXDh+JRhj/gq4+PKS1xrLx4FnjDFTxphp4Bng4VWI63IeBZ42xtSNMceJL6n5AKv0fhtjRo0xrya3S8DbwDDrvN2uENflrNl2S157ObnrJj8G+Cjwn5P2i7dZc1v+Z+BjIiJXiHk1YrucNfs7EJGtwM8B/09yX9gA22wpGzXpDwOnF9w/w5X/KFaLAb4jIq+IyONJ24CJL/QOcB4YSG6vR8zXGstaxviF5Cv1k83yyXrGlXyFvo+4d7hhtttFccEG2G5JmeJ1YIw4IR4FZowxwRLP04ohWT4L9KxVbMaY5nb7Z8l2+1cikro4totiWI3Yfhv434Eoud/DBtlmF9uoSX+j+LAxZh/wCeDzIvKRhQtN/J1sQ4x53UixAF8BdgH3AqPAv1jPYEQkD/wJ8A+NMXMLl63ndlsirg2x3YwxoTHmXmArcU/ztvWIYykXxyYidwG/Shzje4lLNv9oLWMSkU8CY8aYV9byea/XRk36Z4FtC+5vTdrWlDHmbPL/GPBnxH8AF5plm+T/sWT19Yj5WmNZkxiNMReSP84I+H3e/Yq65nGJiEucWP+TMeZPk+Z1325LxbWRtlsSzwzwfeADxKWR5lxdC5+nFUOyvBOYXMPYHk7KZcYYUwf+A2u/3T4E/LyInCAusX0U+B022DZrWemDBCvxQzwR3DHigxnNA1R3rnEMOaCw4PbfENf9/jmLDwL+VnL751h80OjFVYhphMUHTK8pFuJe0HHig1ddye3uVYhraMHtXyGuUwLcyeIDVceID0auyvudvP6vAb99Ufu6brcrxLXu2w3oA4rJ7Qzw18AngT9m8UHJf5Dc/jyLD0p+/Uoxr1JsQwu2628DX16Pv4PksR/k3QO5677NloxxpR9wxQKLj7y/Q1xP/Mfr8Pw7kzfgx8BbzRiIa2/PAoeB7zY/LMkH698k8f4E2L/C8fwh8Vd+n7jW94vXEwvwPxIfIDoCfG6V4vqPyfO+AXyDxcnsHydxHQI+sZrvN/Bh4tLNG8Dryc8j673drhDXum834D3Aa0kMbwK/tuDv4cXk9f8xkEra08n9I8nynVeLeRVi+16y3d4E/l/eHeGzZn8HCx73Qd5N+uu+zZb60WkYlFKqjWzUmr5SSqlVoElfKaXaiCZ9pZRqI5r0lVKqjWjSV0qpNqJJXyml2ogmfaWUaiP/P+bSyt+jp+QXAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(rubber_2000_y_4326)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='risk'></a>\n",
"## 3. Generate risk map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='waterRisk'></a>\n",
"### 3.1 Unsustainable water use risk\n",
"\n",
"Notes for the unsustainable water risk methodology can be found [here](https://docs.google.com/document/d/1h4BIWMrv6OGRAgNPlOfjOLex_FHb9h22RRTLp00QMzs/edit#). The formula for estimating the unsustianbale water consumption risk(m3/ year) would be:\n",
"\n",
" Unsustainable water consumption risk (m3 / year) = blue water footprint of one commodity (mm / yr) * harvest area portion in that location (unitless) * 1/1000(unit conversion) \n",
" \n",
"However, as the final impact metric would be dependent on the probability purchase area, for this first iteration we would be estimating alternative the unsustainable water consumption metric withouth this alternative approcah and therefore we will use the following formula to estimate the risk map:\n",
"\n",
" Unsustainable water consumption risk (m3 / year * Tonnes) = blue water footprint (mm / yr) * harvest area portion in that location * 10000/1000 * 1/ yield (Tonne/ha)\n",
" \n",
"Comments and limitations: \n",
"\n",
"- very high harvested crop area in one grid-cell and a very low blue water value can result in unrealistically low irrigation volume per hectare.\n",
"\n",
"### 3.1.1 Blue water footprint:\n",
"#### Generic blue water footprint:\n",
"\n",
"We can derive the unsustainable water use risk map for rubber from the blue water footprint for specific crops (as can be cotton in our case). However we will need to obtain the blue water footprint for any other crop no covered by the data retreived above (for example rubber). More info can be found [here](http://www.waterfootprint.org/Reports/Report50-NationalWaterFootprints-Vol1.pdf)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# download generic water footprint\n",
"url = 'https://data.4tu.nl/articles/dataset/The_green_blue_grey_and_total_water_footprint_related_to_production/12675440'\n",
"local_path = '../../datasets/raw/water_indicators/'\n",
"\n",
"#if not os.path.isdir(local_path):\n",
"# os.mkdir(local_path)\n",
"#print('Downloading dataset...')\n",
"#r = requests.get(url)\n",
"#z = zipfile.ZipFile(io.BytesIO(r.content))\n",
"#print(\"Done\")\n",
"#z.extractall(path=local_path) # extract to folder"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dblbnd.adf hdr.adf metadata.xml prj.adf sta.adf w001001.adf w001001x.adf\r\n"
]
}
],
"source": [
"!ls ../../datasets/raw/water_indicators/Report50-WF-of-prodn-RasterFiles/wf_totagr_mm"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"default_blwf_1996_2005 = '../../datasets/raw/water_indicators/Report50-WF-of-prodn-RasterFiles/wf_totagr_mm/hdr.adf'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-179.99166664999998, bottom=-55.91166665, right=180.00836215000007, top=83.08834447000001)\n",
"{'driver': 'AIG', 'dtype': 'float32', 'nodata': -3.4028234663852886e+38, 'width': 4320, 'height': 1668, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334, 0.0, -179.99166664999998,\n",
" 0.0, -0.08333334, 83.08834447000001), 'tiled': False}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = rasterio.open(default_blwf_1996_2005)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Blues')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Set the rasters with the global extent:\n",
"\n",
"Before doing the calculations, we should check:\n",
"\n",
"- all the rasters have the same extent\n",
"- all the rasters have the same resolution\n",
"- the no data in each raster"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 1668\n",
"Warning 1: Computed -srcwin -1 -83 4320 2160 falls partially outside raster extent. Going on however.\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"#change extent in blwf generic data\n",
"!gdal_translate -projwin -180.0 90.0 180.0 -90.0 -of GTiff '../../datasets/raw/water_indicators/Report50-WF-of-prodn-RasterFiles/wf_totagr_mm/hdr.adf' '../../datasets/raw/water_indicators/Report50-WF-of-prodn-RasterFiles/wf_totagr_mm/hdr_ext_v2.tif'"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 2160\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"#change extent in default aggricuture harvest area fraction \n",
"!gdal_translate -projwin -180.0 90.0 180.0 -90.0 -of GTiff \"../../datasets/raw/crop_data/default_crop/spam2010V2r0_global_H_REST_A.tif\" \"../../datasets/raw/crop_data/default_crop/spam2010V2r0_global_H_REST_A_ext_v2.tif\""
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 2160\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"#change extent in default crop yield\n",
"!gdal_translate -projwin -180.0 90.0 180.0 -90.0 -of GTiff \"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A.tif\" \"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A_ext_v2.tif\""
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 2160\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"#change extent in default pasture harvest area fraction\n",
"!gdal_translate -projwin -180.0 90.0 180.0 -90.0 -of GTiff \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m.tif\" \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_ext_v2.tif\" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Check global extent reprojection:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"default_blwf_1996_2005_ext = '../../datasets/raw/water_indicators/Report50-WF-of-prodn-RasterFiles/wf_totagr_mm/hdr_ext_v2.tif'\n",
"default_ag_ha_2010 = \"../../datasets/raw/crop_data/default_crop/spam2010V2r0_global_H_REST_A_ext_v2.tif\"\n",
"default_ag_yield_2010 = \"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A_ext_v2.tif\"\n",
"default_pasture_ha_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_ext_v2.tif\"\n",
"default_pasture_y_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v4.tif\""
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.07499998999998, bottom=-89.99500271000002, right=179.92502881000007, top=90.00501169)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.4028234663852886e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334, 0.0, -180.07499998999998,\n",
" 0.0, -0.08333334, 90.00501169), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(default_blwf_1996_2005_ext)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99928, right=179.99856, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -1.0, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.083333, 0.0, -180.0,\n",
" 0.0, -0.083333, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(default_ag_yield_2010)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99928, right=179.99856, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -1.0, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.083333, 0.0, -180.0,\n",
" 0.0, -0.083333, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(default_ag_ha_2010)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.402823e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(default_pasture_ha_2000)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.402823e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(default_pasture_y_2000)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1.2 Calculate the risk \n",
"\n",
"For the first version of the mock data, we will produce the risk map without producing the probbility purchase area, using the formula below:\n",
"\n",
" Unsustainable water consumption (m3 / year * Tonnes) = blue water footprint (mm / yr) * harvest area portion in that location * 10000/1000 * 1/yield (Tonne/ha)\n",
" \n",
"Remove 0 values from yiel for calculation as the division produces inf results."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: invalid value encountered in true_divide\n",
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 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]
}
],
"source": [
"#remove 0 from yield to avoid inf values in default crop\n",
"!gdal_calc.py -A \"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A_ext_v2.tif\" --outfile=\"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A_ext_v2_NoZero.tif\" --calc=\"A/(A!=0)\""
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99928, right=179.99856, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.083333, 0.0, -180.0,\n",
" 0.0, -0.083333, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(\"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A_ext_v2_NoZero.tif\")\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: invalid value encountered in true_divide\n",
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 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66.. 66.. 66.. 66.. 66.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#remove the zeros to the pasture yield to avoid inf results\n",
"!gdal_calc.py -A \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v4.tif\" --outfile=\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros.tif\" --calc=\"A/(A!=0)\" --NoDataValue -3.402823e+38 "
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.402823e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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IrGX2lMDyw9u9dGLsSboyLJt0Ld0X3Ub808cPRfxDni+laTTJkNfTRnDT96Lo90nfVJlTvypjD/lsHb2jqioiWr5n5eOtB9YDHLtqpbfjGvmUPRXNBV5OWub9gxihFK42oa1dVxpVj/3Yzl+x/96LS/cLsesnvV/TGWWzKgJjd5pG7zzkum1w/7e79K1Asnm+zKXlpXdGn5EJMVF3zpykyIcyT3qXFM13lF4PLX99aOP5pfv0bW/XfmrzoaAi28ZcYTQV/auAeQTOGuCrifSzXBTPCcCjrhvoOuBkEVniBnBPdmlGx1QpAEMKWIiFKz2h3NwvRX4KoQL4uxM+OOj5YyXEPNglpd07InI5s4HYg0VkC7MonPOBK0XkbOAHwJvc7tcCpwMLwA7grQCq+oiIfBC4xe33gfmgbhVC6wOOkSo+HCrz++jmsTwyo6gLbuq+SdP0bfPY/Sjp+bhD4thVK/XbN93QqkBPPYqnauRMKK2dtnPADHmPi3zoY4CxKTHn+1DyZZrQfbr/3otvVdVVWdtsGoaJUaVV7XvAtg5NxTu0QhjKE1VofhkL6cakz4nnuiYK0e8qVtnIJoQB25hj+0OxNRQ72hBD+W0SaTTkvYlC9NsyhszflFBanG1JF5ZYu+368nkdn4QgRFNjSJ9PQvSN8RBSbH+aUMZLqvonbVeovo2htd+UIZ5obZZNIzrGKgBGPiFWRrFiom9ESwix8bGSVXGG7svQ7YsFE/2K+Jxn3jBCJIa8HcKb0G0DS4ro4x5MRvTt4yCGMSPmyKjYqfo5yy41ZtQDuV1PgmWFxQidIoHPW46FPufMGco/XUSpRSv6WSFPXYZBjTGCYIzXZDSfThjiE/+03T4qgqyw4Ky5/WMtP6Ps3snKwD76AmMrEFWI4Zqyvi8bg91zQrI1JpHy1SWbNXle0X7zc2fZk/WbvGPVsa8KRf6o46coWvp5tWuVF498F7iQCrAvQmqxZE0BkV6P7R6E4tskWVNFx0gTu7N6B5LLvnyRd56s7VXSfRG86Jd9OSdJUS3d5pxVKxqjPWmBT1cC5v/p0HVjJO/YvoI9uhozaXusUXbvtJmpscp0q1MN3czqZvF1rLxjm+A3o6wrI5b8O3R4Zl9U7X4uejqo6qegW/qSUSclW9x1prLNOkZVYikgbajSqmoyEVvViiKESd66YMhvFNTZr6+AiJBo+yTRZD7+Ln06qj79PMaeKfvG9+N0smsmS/zrfmTdwmSNtgzdgMuLAqr7+6z0qkQj+nUEqcnAbxOm0iKqQ1E3TXrbWFv3MVAU4mgMS9cVUzSi7/NN2iG/YhQ6XQ6eJVv9Jvj+KYsWKaOoqyedbrSjjY/bPvFGI/p1qOuQpo9LYy0ETaKg5kKeJ+ZJoW8q+FXHaUK6L32Fw7aJCx97oye06+uih6BOPotS9Ns6y5dAhCo0vkiLf1HG6rrlPkb/+qJNH/GY6Vrss8as6pQDH3MgJcvmKKJ3+iCvsEytNVSEDxGZ9+l3VTmELHQhvfxWxlhe2uqD9DiVr7xdJb+0yU/RiX7TN/CyxD3ry0F55ynr8rDIkj1Ji/yU+/G7Fv6qx8/Ko5Zvm5OVp+s0bsreyi27p03uXRSi7yNTlmX2qgXS11u/RnVMlKphfgoDn42bLqIPgxZ95cneMrKPwd/YKctcfVV0Y/St0S+hN8q6mJCtKqOchmFMJKd8iG0KiLotnqm8cl+FKm8y1/XX1H3rcxqRtgxZhoNu6Q+NRUQYQ1FWYTado2Uq5I1xNImyqUNWxRLaWFbwom9vvT5N1z7wdfwuIhrGQJXBVntxrTv68GuTEM4qjUufOhi06GdNuNYnIVQ0IdhQlyaFK8brNIw8Qq64rU/fGJwpCX5XkWjGnoTspyaBI76up1T0ReRiEdkuIncl0t4vIltF5Db3d3pi23tFZEFE7hWRUxLpp7q0BRFZ58V6w4iQNoU3ZCEz4qBK984lwL8Al6XSP6aq/5hMEJGjgDOBo4HnAV8XkRe5zZ8ATgK2ALeIyFWqek/RifsM2Zw6Pl4Jb4Ld390p6hYwXxk+KBV9Vf2miBxR8XirgStU9QngfhFZAI5z2xZUdTOAiFzh9i0UfSMc8gYg2ww8mohVx3zVD23nqm9yjrr3tm0DrU2f/rkicofr/lni0pYCDyb22eLS8tKNiCn6MEoZJmLVMV/1Q9mUCKFS992dpqJ/IfBC4BhgG/CRhsfZAxFZKyIbRWTjww//2NdhjRLmA0VZA0ZVwgzrnmvqpP2c9n/evTDqUSTkdfzs6z50OZDf6dTKqvrQfFlELgKudqtbgeWJXZe5NArS08deD6wHOHbVSm1in9EtRSJfFKdsArYnY/RJaC81+hTsUN6Gz7Olin2NRF9EDlPVbW71jcA8sucq4HMi8lFmA7krgJsBAVaIyJHMxP5M4A+bnNvol6xMlBTyos8jGsbYaDJJY9Zvh6RU9EXkcuBE4GAR2QKcB5woIscACjwA/AmAqt4tIlcyG6DdCZyjqrvccc4FrgMWARer6t2+L8YIl1AyvGH4ok7LP6T8L6rh9qAcu2qlfvumG4Y2Y/IUZeyi6J2QMrphdE1ROfFZFqrMsb//3otvVdVVWduDnobBGJ6iSauKMME3jP6pUu6imIYhpumEDRN8Y5p0ne/LdHCU38i1TxKGhQ3WGkb3+P5kYlSib/RLXmazPnzDyCZrcLdNueiih8NE3zAip85c60PNsTQlQp+3KgrRt4xpGNnYWJeRpLOXs4xp0/Un54ywsaeF4WnjcxN9oxEm+GHQ5GMcRryM/nOJRpikBd+ExDC6p7cvZxlGkrozahqGERbW0jdqYa18o2jqbcsP3ZAMBS3y8WjeyDXCxaJHDKM7uvimgrX0jVyqziI45ZbelK/d8EPVPOQrf5noG4WE9OEII0yssosLE32jlHmh7mvq2JiY6nUb/ukrL5noG5XJEn8TPcNoR99lyETfqI0JvWHEi0XvGIZhTAgTfcMwjAlhom8YhjEhTPQNwzAmhIm+YRjGhDDRNwzDmBAm+oZhGBPCRN8wDGNCmOgbhmFMCBN9wzCMCWGibxiGMSFM9A3DMCaEib5hGMaEKBV9EVkuIt8QkXtE5G4ReadLP0hENojIJvd/iUsXEblARBZE5A4RWZk41hq3/yYRWdPdZRmGYRhZVGnp7wT+UlWPAk4AzhGRo4B1wPWqugK43q0DnAascH9rgQthVkkA5wHHA8cB580rCsMwDKMfSkVfVbep6nfc8i+A7wFLgdXApW63S4E3uOXVwGU640bgQBE5DDgF2KCqj6jqT4ENwKk+L8YwDMMoplafvogcAbwcuAk4VFW3uU0/Ag51y0uBBxM/2+LS8tLT51grIhtFZOPDD/+4jnmGYRhGCZVFX0SeCXwJeJeq/jy5TVUVUB8Gqep6VV2lqqsOOeRgH4c0DMMwHJVEX0T2YSb4n1XVL7vkh1y3De7/dpe+FVie+Pkyl5aXbhiGYfRElegdAT4NfE9VP5rYdBUwj8BZA3w1kX6Wi+I5AXjUdQNdB5wsIkvcAO7JLs0wDMPoiSofRn8F8BbgThG5zaX9NXA+cKWInA38AHiT23YtcDqwAOwA3gqgqo+IyAeBW9x+H1DVR3xchGEYhlGNUtFX1RsAydn82oz9FTgn51gXAxfXMdAwDMPwh72RaxiGMSFM9A3DMCaEib5hGMaEMNE3DMOYECb6hmEYE8JE3zAMY0KY6BuGYUwImYXVh4mI/AK4d2g7cjgYCHVGOLOtPqHaBWZbU0K1rQ+7nq+qh2RtqPJG7pDcq6qrhjYiCxHZaLbVJ1TbQrULzLamhGrb0HZZ945hGMaEMNE3DMOYEKGL/vqhDSjAbGtGqLaFaheYbU0J1bZB7Qp6INcwDMPwS+gtfcMwDMMjJvqGYRgTIljRF5FTReReEVkQkXUD2fCAiNwpIreJyEaXdpCIbBCRTe7/EpcuInKBs/cOEVnp2ZaLRWS7iNyVSKtti4iscftvEpE1WefyYNf7RWSr89ttInJ6Ytt7nV33isgpiXTv91tElovIN0TkHhG5W0Te6dIH9VuBXYP7TUT2E5GbReR2Z9vfuvQjReQmd57Pi8gzXPq+bn3BbT+izOYObLtERO5P+O0Yl95bOXDHXCQi3xWRq9364D7LRFWD+wMWAd8HXgA8A7gdOGoAOx4ADk6lfRhY55bXAX/vlk8H/oPZB2dOAG7ybMurgJXAXU1tAQ4CNrv/S9zykg7sej/w7ox9j3L3cl/gSHePF3V1v4HDgJVu+VnAfc6GQf1WYNfgfnPX/ky3vA9wk/PFlcCZLv2TwNvd8juAT7rlM4HPF9nckW2XAL+XsX9v5cAd9y+AzwFXu/XBfZb1F2pL/zhgQVU3q+qvgSuA1QPbNGc1cKlbvhR4QyL9Mp1xI3CguA/H+0BVvwmkPy9Z15ZTgA2q+oiq/hTYAJzagV15rAauUNUnVPV+Zp/UPI6O7reqblPV77jlXwDfA5YysN8K7MqjN7+5a/+lW93H/SnwGuCLLj3ts7kvvwi8VkSkwOYubMujt3IgIsuAM4B/detCAD7LIlTRXwo8mFjfQnGh6AoF/lNEbhWRtS7tUJ196B3gR8ChbnkIm+va0qeN57pH6ovn3SdD2uUeoV/OrHUYjN9SdkEAfnPdFLcB25kJ4veBn6nqzozzPGWD2/4o8Jy+bFPVud8+5Pz2MRHZN21byoYubPs48FfAk279OQTiszShin4ovFJVVwKnAeeIyKuSG3X2TBZEzGtItgAXAi8EjgG2AR8Z0hgReSbwJeBdqvrz5LYh/ZZhVxB+U9VdqnoMsIxZS/PFQ9iRRdo2EflN4L3MbPwtZl027+nTJhF5PbBdVW/t87xNCVX0twLLE+vLXFqvqOpW93878BVmBeChebeN+7/d7T6EzXVt6cVGVX3IFc4ngYt4+hG1d7tEZB9mwvpZVf2ySx7cb1l2heQ3Z8/PgG8Av82sa2Q+V1fyPE/Z4LY/G/hJj7ad6rrLVFWfAP6N/v32CuB3ReQBZl1srwH+icB89hS+Bwl8/DGbCG4zs8GM+QDV0T3bsBh4VmL5f5j1+/0Duw8Cftgtn8Hug0Y3d2DTEew+YFrLFmatoPuZDV4tccsHdWDXYYnlP2fWTwlwNLsPVG1mNhjZyf12138Z8PFU+qB+K7BrcL8BhwAHuuX9gW8Brwe+wO6Dku9wy+ew+6DklUU2d2TbYQm/fhw4f4hy4I59Ik8P5A7us0wbfR/Qm2Gzkff7mPUnvm+A87/A3YDbgbvnNjDre7se2AR8fZ5ZXMb6hLP3TmCVZ3suZ/bI/3/M+vrObmIL8MfMBogWgLd2ZNdn3HnvAK5idzF7n7PrXuC0Lu838EpmXTd3ALe5v9OH9luBXYP7DXgp8F1nw13A3yTKw83u+r8A7OvS93PrC277C8ps7sC2/3J+uwv4d56O8OmtHCSOeyJPi/7gPsv6s2kYDMMwJkSoffqGYRhGB5joG4ZhTAgTfcMwjAlhom8YhjEhTPQNwzAmhIm+YRjGhDDRNwzDmBD/DyKZyCSu/umxAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open(\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros.tif\")\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: invalid value encountered in true_divide\n",
"0.. 1.. 2.. 3.. 4.. 5.. 6.. 7.. 8.. 9.. 10.. 11.. 12.. 13.. 14.. 15.. 16.. 17.. 18.. 19.. 20.. 21.. 22.. 23.. 24.. 25.. 26.. 27.. 28.. 29.. 30.. 31.. 32.. 33.. 34.. 35.. 36.. 37.. 38.. 39.. 40.. 41.. 42.. 43.. 44.. 45.. 46.. 47.. 48.. 49.. 50.. 51.. 52.. 53.. 54.. 55.. 56.. 57.. 58.. 59.. 60.. 61.. 62.. 63.. 64.. 65.. 66.. 67.. 68.. 69.. 70.. 71.. 72.. 73.. 74.. 75.. 76.. 77.. 78.. 79.. 80.. 81.. 82.. 83.. 84.. 85.. 86.. 87.. 88.. 89.. 90.. 91.. 92.. 93.. 94.. 95.. 96.. 97.. 98.. 99.. 100 - Done\n"
]
}
],
"source": [
"# remove 0 in cotton to avoid inf results\n",
"!gdal_calc.py -A '../../datasets/raw/crop_data/cotton/cotton_YieldPerHectare.tif' --outfile='../../datasets/raw/crop_data/cotton/cotton_YieldPerHectare_NoZeris.tif' --calc=\"A/(A!=0)\" --NoDataValue -3.402823e+38 "
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.402823e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open('../../datasets/raw/crop_data/cotton/cotton_YieldPerHectare_NoZeris.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: invalid value encountered in true_divide\n",
"0.. 1.. 2.. 3.. 4.. 5.. 6.. 7.. 8.. 9.. 10.. 11.. 12.. 13.. 14.. 15.. 16.. 17.. 18.. 19.. 20.. 21.. 22.. 23.. 24.. 25.. 26.. 27.. 28.. 29.. 30.. 31.. 32.. 33.. 34.. 35.. 36.. 37.. 38.. 39.. 40.. 41.. 42.. 43.. 44.. 45.. 46.. 47.. 48.. 49.. 50.. 51.. 52.. 53.. 54.. 55.. 56.. 57.. 58.. 59.. 60.. 61.. 62.. 63.. 64.. 65.. 66.. 67.. 68.. 69.. 70.. 71.. 72.. 73.. 74.. 75.. 76.. 77.. 78.. 79.. 80.. 81.. 82.. 83.. 84.. 85.. 86.. 87.. 88.. 89.. 90.. 91.. 92.. 93.. 94.. 95.. 96.. 97.. 98.. 99.. 100 - Done\n"
]
}
],
"source": [
"# remove 0 in rubber to avoid inf results\n",
"!gdal_calc.py -A '../../datasets/raw/crop_data/rubber/rubber_YieldPerHectare.tif' --outfile='../../datasets/raw/crop_data/rubber/rubber_YieldPerHectare_NoZeros.tif' --calc=\"A/(A!=0)\" --NoDataValue -3.402823e+38 "
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.402823e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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wit6e5inDqGM6U2tL8qvA++jV+Bv0hmz+bJA1JXk7sLeqdgxyu3M1qqG/G1jdN7+qtQ1UVe1u13uBL9B7ATw5OWzTrve21YdR82xrGUiNVfVke3H+DPhHXvyIOvC6khxNL1g/VVWfb81D77fp6hqlfmv1/BD4GnAmvaGRyd/q6t/OCzW05b8IfH+Ata1rw2VVVT8B/oXB99ubgN9O8ii9Iba3AH/PiPXZC+b7IMF8XOj9ENzD9A5mTB6get2AazgeeEXf9Dfojfv9LfsfBPxom34b+x80umMBano1+x8wnVUt9PaCHqF38Gppmz5xAepa0Tf9R/TGKQFex/4Hqh6mdzByQZ7v9vivA66c0j7UfjtAXUPvN2AZcEKb/nngv4C3A59h/4OS727Tl7D/QcmbDlTzAtW2oq9frwQ+MozXQbvvs3nxQO7Q+2zaGuf7DuetsN6R9+/QG098/xC2/5r2BNwN3D9ZA72xt1uBncB/TP6xtD+sj7d67wXG5rmeG+h95P8/emN9F8+lFuAP6B0gmgAuWqC6/rVt9x5gG/uH2ftbXQ8B6xfy+QbOojd0cw9wV7ucN+x+O0BdQ+834NeAb7ca7gP+su/1cEd7/J8Bjm3tx7X5ibb8NQereQFq+2rrt/uAf+PFM3wG9jrou9+zeTH0h95n0138GQZJ6pBRHdOXJC0AQ1+SOsTQl6QOMfQlqUMMfUnqEENfkjrE0JekDvl/Cx09KIhnCDsAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore download raster - harvest area fraction\n",
"a = rasterio.open('../../datasets/raw/crop_data/rubber/rubber_YieldPerHectare_NoZeros.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Greens')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"default_blwf_1996_2005_ext = '../../datasets/raw/water_indicators/Report50-WF-of-prodn-RasterFiles/wf_totagr_mm/hdr_ext_v2.tif'\n",
"default_ag_ha_2010 = \"../../datasets/raw/crop_data/default_crop/spam2010V2r0_global_H_REST_A_ext_v2.tif\"\n",
"default_ag_yield_2010 = \"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A_ext_v2_NoZero.tif\" \n",
"default_pasture_ha_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_ext_v2.tif\"\n",
"default_pasture_y_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros.tif\"\n",
"cotton_y_2000 = '../../datasets/raw/crop_data/cotton/cotton_YieldPerHectare_NoZeris.tif'\n",
"cotton_ha_2000 = '../../datasets/raw/crop_data/cotton/cotton_HarvestedAreaFraction.tif'\n",
"rubber_y_2000 = '../../datasets/raw/crop_data/rubber/rubber_YieldPerHectare_NoZeros.tif'\n",
"rubber_ha_2000 = '../../datasets/raw/crop_data/rubber/rubber_HarvestedAreaFraction.tif'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Default unsustianbale water risk for aggricultural:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: overflow encountered in multiply\n",
"/opt/conda/lib/python3.8/site-packages/osgeo/utils/gdal_calc.py:367: RuntimeWarning: invalid value encountered in multiply\n",
" myResult = ((1 * (myNDVs == 0)) * myResult) + (myOutNDV * myNDVs)\n",
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 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66.. 66.. 66.. 66.. 66.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#CALCULATE DEFAULT AGRICULTURAL RISK\n",
"!gdal_calc.py -A $default_blwf_1996_2005_ext -B $default_ag_ha_2010 -C $default_ag_yield_2010 --outfile='../../datasets/processed/water_indicators/water_risk_4323_2010_v2.tif' --calc=\"A *B*(1/C)*(10000/1000)\""
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.07499998999998, bottom=-89.99500271000002, right=179.92502881000007, top=90.00501169)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334, 0.0, -180.07499998999998,\n",
" 0.0, -0.08333334, 90.00501169), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default aggricultural risk map\n",
"default_aggri_risk_2010 = \"../../datasets/processed/water_indicators/water_risk_aggriculture_4326_2010_v2.tif\"\n",
"a = rasterio.open(default_aggri_risk_2010)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Default unsustianbale water risk for pasture:\n"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: overflow encountered in multiply\n",
"/opt/conda/lib/python3.8/site-packages/osgeo/utils/gdal_calc.py:367: RuntimeWarning: invalid value encountered in multiply\n",
" myResult = ((1 * (myNDVs == 0)) * myResult) + (myOutNDV * myNDVs)\n",
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 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94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"## the results from gdal cal produce inf results\n",
"!gdal_calc.py -A $default_blwf_1996_2005_ext -B $default_pasture_ha_2000 -C $default_pasture_y_2000 --outfile='../../datasets/processed/water_indicators/water_risk_pasture_4326_2000_v9.tif' --calc=\"A*B*(1/C)*(10000/1000)\" --NoDataValue -3.402823e+38 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### NOTE:\n",
"\n",
"I'm having issue runing the gdal calculator for the pasture risk. TO that end I'll use the raster calculator from qgis to produce the pasture risk map."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.075, bottom=-89.995, right=179.92503, top=90.00501)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.4028234663852886e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334027777778, 0.0, -180.075,\n",
" 0.0, -0.08333333796296297, 90.00501), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default pasture risk map\n",
"default_pasture_risk_2000 = \"../../datasets/processed/water_indicators/water_risk_pasture_4326_2000_v10.tif\"\n",
"a = rasterio.open(default_pasture_risk_2000)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Water risk for cotton:"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: overflow encountered in multiply\n",
"/opt/conda/lib/python3.8/site-packages/osgeo/utils/gdal_calc.py:367: RuntimeWarning: invalid value encountered in multiply\n",
" myResult = ((1 * (myNDVs == 0)) * myResult) + (myOutNDV * myNDVs)\n",
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 48.. 48.. 48.. 48.. 48.. 48.. 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94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#calculate global water risk for cotton in 2000\n",
"!gdal_calc.py -A $default_blwf_1996_2005_ext -B $cotton_ha_2000 -C $cotton_y_2000 --outfile='../../datasets/processed/water_indicators/water_risk_cotton_4326_2000.tif' --calc=\"A *B*(1/C)*(10000/1000)\""
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.07499998999998, bottom=-89.99500271000002, right=179.92502881000007, top=90.00501169)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334, 0.0, -180.07499998999998,\n",
" 0.0, -0.08333334, 90.00501169), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default pasture risk map\n",
"a = rasterio.open('../../datasets/processed/water_indicators/water_risk_cotton_4326_2000.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Water risk for rubber:"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: overflow encountered in multiply\n",
"/opt/conda/lib/python3.8/site-packages/osgeo/utils/gdal_calc.py:367: RuntimeWarning: invalid value encountered in multiply\n",
" myResult = ((1 * (myNDVs == 0)) * myResult) + (myOutNDV * myNDVs)\n",
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 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94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#calculate global water risk for rubber in 2000\n",
"!gdal_calc.py -A $default_blwf_1996_2005_ext -B $rubber_ha_2000 -C $rubber_y_2000 --outfile='../../datasets/processed/water_indicators/water_risk_rubber_4326_2000.tif' --calc=\"A *B*(1/C)*(10000/1000)\""
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.07499998999998, bottom=-89.99500271000002, right=179.92502881000007, top=90.00501169)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333334, 0.0, -180.07499998999998,\n",
" 0.0, -0.08333334, 90.00501169), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default pasture risk map\n",
"a = rasterio.open('../../datasets/processed/water_indicators/water_risk_rubber_4326_2000.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='deforestationRisk'></a>\n",
"### 3.2 Deforestation risk\n",
"\n",
"Notes for the deforestation risk methodology can be found [here](https://docs.google.com/document/d/16XoklY3Ze2LQRMuiignE7fPURIB0a1llZHEzzRy2fWU/edit#heading=h.g8tlcpcbnmgq). The formula for estimating the deforestation risk(ha/ year) would be:\n",
"\n",
" Deforestation area due to commodity (ha/year) = Total deforestation area (ha) * Purchase area fraction (unitless)\n",
"\n",
" \n",
"However, as the final impact metric would be dependent on the probability purchase area, for this first iteration we would be estimating alternative the deforestation metric withouth this alternative approcah and therefore we will use the following formula to estimate the risk map:\n",
"\n",
" Deforestation area due to commodity (ha/year *tonnes) = Total deforestation area (ha) * 1/ yield (Tonne/ha) * 0.0001\n",
"\n",
"NOTE: For calculating this first mock data we will use the deforestation stimations from [our world in data](https://ourworldindata.org/deforestation)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Calculate total deforestation in ha"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Entity</th>\n",
" <th>Code</th>\n",
" <th>Year</th>\n",
" <th>Net forest conversion</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Algeria</td>\n",
" <td>DZA</td>\n",
" <td>1990</td>\n",
" <td>-8800</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Algeria</td>\n",
" <td>DZA</td>\n",
" <td>2000</td>\n",
" <td>33900</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>DZA</td>\n",
" <td>2010</td>\n",
" <td>7600</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Algeria</td>\n",
" <td>DZA</td>\n",
" <td>2015</td>\n",
" <td>-1400.0000000000005</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Argentina</td>\n",
" <td>ARG</td>\n",
" <td>1990</td>\n",
" <td>-182600</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Entity Code Year Net forest conversion geometry\n",
"0 Algeria DZA 1990 -8800 None\n",
"1 Algeria DZA 2000 33900 None\n",
"2 Algeria DZA 2010 7600 None\n",
"3 Algeria DZA 2015 -1400.0000000000005 None\n",
"4 Argentina ARG 1990 -182600 None"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"##open the csv \n",
"deforestation = gpd.read_file('../../datasets/raw/deforestation_indicators/annual-change-forest-area.csv')\n",
"deforestation.head()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Entity</th>\n",
" <th>Code</th>\n",
" <th>Year</th>\n",
" <th>Net forest conversion</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Algeria</td>\n",
" <td>DZA</td>\n",
" <td>2000</td>\n",
" <td>33900</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Argentina</td>\n",
" <td>ARG</td>\n",
" <td>2000</td>\n",
" <td>-316400</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Aruba</td>\n",
" <td>ABW</td>\n",
" <td>2000</td>\n",
" <td>0</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Australia</td>\n",
" <td>AUS</td>\n",
" <td>2000</td>\n",
" <td>-226799.99999999994</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Austria</td>\n",
" <td>AUT</td>\n",
" <td>2000</td>\n",
" <td>2510.0000000000005</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Entity Code Year Net forest conversion geometry\n",
"1 Algeria DZA 2000 33900 None\n",
"5 Argentina ARG 2000 -316400 None\n",
"9 Aruba ABW 2000 0 None\n",
"13 Australia AUS 2000 -226799.99999999994 None\n",
"16 Austria AUT 2000 2510.0000000000005 None"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#filter those whose year is 2000\n",
"deforestation = deforestation[deforestation['Year']=='2000']\n",
"deforestation.head()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Domain Cod</th>\n",
" <th>Domain</th>\n",
" <th>Area Code</th>\n",
" <th>Area</th>\n",
" <th>Element Co</th>\n",
" <th>Element</th>\n",
" <th>Item Code</th>\n",
" <th>Item</th>\n",
" <th>Year Code</th>\n",
" <th>Year</th>\n",
" <th>Unit</th>\n",
" <th>Value</th>\n",
" <th>Flag</th>\n",
" <th>Flag Descr</th>\n",
" <th>geometry</th>\n",
" <th>animal_ha</th>\n",
" <th>yield_t_ha</th>\n",
" <th>deforestation_ha</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>295</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((60.51760 34.06176, 60.51934 34.06627...</td>\n",
" <td>0.05</td>\n",
" <td>0.001475</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>3</td>\n",
" <td>Albania</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>219</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((19.00099 40.50217, 19.00099 40.50396...</td>\n",
" <td>0.38</td>\n",
" <td>0.008322</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>4</td>\n",
" <td>Algeria</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>240</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>POLYGON ((-8.66891 28.24094, -8.66891 28.30392...</td>\n",
" <td>0.03</td>\n",
" <td>0.000720</td>\n",
" <td>33900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>7</td>\n",
" <td>Angola</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>285</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>MULTIPOLYGON (((11.46098 -16.55432, 11.46166 -...</td>\n",
" <td>0.04</td>\n",
" <td>0.001140</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>QL</td>\n",
" <td>Livestock Primary</td>\n",
" <td>8</td>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>5420</td>\n",
" <td>Yield</td>\n",
" <td>919</td>\n",
" <td>Hides, cattle, fresh</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>hg/An</td>\n",
" <td>224</td>\n",
" <td>Fc</td>\n",
" <td>Calculated data</td>\n",
" <td>MULTIPOLYGON (((-62.55365 16.89931, -62.51644 ...</td>\n",
" <td>0.88</td>\n",
" <td>0.019712</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Domain Cod Domain Area Code Area Element Co \\\n",
"0 QL Livestock Primary 2 Afghanistan 5420 \n",
"1 QL Livestock Primary 3 Albania 5420 \n",
"2 QL Livestock Primary 4 Algeria 5420 \n",
"3 QL Livestock Primary 7 Angola 5420 \n",
"4 QL Livestock Primary 8 Antigua and Barbuda 5420 \n",
"\n",
" Element Item Code Item Year Code Year Unit Value \\\n",
"0 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 295 \n",
"1 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 219 \n",
"2 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 240 \n",
"3 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 285 \n",
"4 Yield 919 Hides, cattle, fresh 2000 2000 hg/An 224 \n",
"\n",
" Flag Flag Descr geometry \\\n",
"0 Fc Calculated data POLYGON ((60.51760 34.06176, 60.51934 34.06627... \n",
"1 Fc Calculated data POLYGON ((19.00099 40.50217, 19.00099 40.50396... \n",
"2 Fc Calculated data POLYGON ((-8.66891 28.24094, -8.66891 28.30392... \n",
"3 Fc Calculated data MULTIPOLYGON (((11.46098 -16.55432, 11.46166 -... \n",
"4 Fc Calculated data MULTIPOLYGON (((-62.55365 16.89931, -62.51644 ... \n",
"\n",
" animal_ha yield_t_ha deforestation_ha \n",
"0 0.05 0.001475 0 \n",
"1 0.38 0.008322 0 \n",
"2 0.03 0.000720 33900 \n",
"3 0.04 0.001140 0 \n",
"4 0.88 0.019712 0 "
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deforestation_ha_list = []\n",
"for i, row in default_pasture_yield_2000.iterrows():\n",
" location = row['Area']\n",
" try:\n",
" deforestation_ha = deforestation[deforestation['Entity']==location].iloc[0]['Net forest conversion'] \n",
" except:\n",
" deforestation_ha = 0\n",
" deforestation_ha_list.append(deforestation_ha)\n",
" \n",
"##append to main geodataframe\n",
"default_pasture_yield_2000['deforestation_ha'] = deforestation_ha_list\n",
"default_pasture_yield_2000.head()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-95-36214e1729bc>:3: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
" default_pasture_yield_2000.to_file(\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.shp\", driver='ESRI Shapefile')\n"
]
}
],
"source": [
"# save file \n",
"default_pasture_yield_2000.to_file(\"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.shp\", driver='ESRI Shapefile')"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"# rasterise to the same resolution\n",
"#convert file to raster with same extent and resolution that the pasture harvset area\n",
"!gdal_rasterize -l Pasture2000_5m_yield -a deforestat -tr 0.083333 0.083333 -a_nodata 0.0 -te -180.0 -90.0 180.0 90.0 -ot Float32 -of GTiff \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield.shp\" \"../../datasets/raw/deforestation_indicators/global_deforestation_2000_ha_4326.tif\""
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"#set projection to dataset\n",
"!gdal_edit.py -a_srs EPSG:4326 \"../../datasets/raw/deforestation_indicators/global_deforestation_2000_ha_4326.tif\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Assing that deforestation to the different commodities:\n",
"\n",
"We will assign the deforestation to each commodity based on the area fraction in each location:\n",
"\n",
" deforestation_commodity = total_deforestation * commodity_harvest_area_fraction\n",
" \n",
"#### cotton deforestation risk:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"default_blwf_1996_2005_ext = '../../datasets/raw/water_indicators/Report50-WF-of-prodn-RasterFiles/wf_totagr_mm/hdr_ext_v2.tif'\n",
"default_ag_ha_2010 = \"../../datasets/raw/crop_data/default_crop/spam2010V2r0_global_H_REST_A_ext_v2.tif\"\n",
"default_ag_yield_2010 = \"../../datasets/raw/crop_data/default_crop/spam2017V2r1_SSA_Y_REST_A_ext_v2_NoZero.tif\" \n",
"default_pasture_ha_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_ext_v2.tif\"\n",
"default_pasture_y_2000 = \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros.tif\"\n",
"cotton_y_2000 = '../../datasets/raw/crop_data/cotton/cotton_YieldPerHectare_NoZeris.tif'\n",
"cotton_ha_2000 = '../../datasets/raw/crop_data/cotton/cotton_HarvestedAreaFraction.tif'\n",
"rubber_y_2000 = '../../datasets/raw/crop_data/rubber/rubber_YieldPerHectare_NoZeros.tif'\n",
"rubber_ha_2000 = '../../datasets/raw/crop_data/rubber/rubber_HarvestedAreaFraction.tif'"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"total_deforestation = \"../../datasets/raw/deforestation_indicators/global_deforestation_2000_ha_4326.tif\""
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"deforestation_metric_ha_2018_3857_test_location.tif\r\n",
"deforestation_metric_ha_2018_3857_test_location.tif.aux.xml\r\n",
"deforestation_risk_ha_2018_new_extent_3857.tif\r\n",
"deforestation_risk_ha_2018_new_extent.tif\r\n",
"deforestation_risk_ha_2018_new_xxtent_bio.tif\r\n",
"deforestation_risk_ha_2018.tif\r\n",
"deforestation_risk_ha_2018.tif.aux.xml\r\n"
]
}
],
"source": [
"!ls ../../datasets/processed/deforestation_indicators"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#calculate global cotton deforestation risk in 2000\n",
"!gdal_calc.py -A $total_deforestation -B $cotton_ha_2000 -C $cotton_y_2000 --outfile='../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000.tif' --calc=\"A*B *(1/C)*0.0001\""
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99928, right=179.99856, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.083333, 0.0, -180.0,\n",
" 0.0, -0.083333, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default cotton risk map\n",
"a = rasterio.open('../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### rubber deforestation risk:"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#calculate global cotton deforestation risk in 2000\n",
"!gdal_calc.py -A $total_deforestation -B $rubber_ha_2000 -C $rubber_y_2000 --outfile='../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000.tif' --calc=\"A*B *(1/C)*0.0001\""
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99928, right=179.99856, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.083333, 0.0, -180.0,\n",
" 0.0, -0.083333, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default rubber risk map\n",
"a = rasterio.open('../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### pasture deforestation risk:\n",
"\n",
"For doing the operations with gdal we need to remove the no data from the pasture harvest area and yield datasets"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 2160\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdal_translate -a_nodata 0.0 -of GTiff \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_ext_v2.tif\" \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_ext_v2_0_nd.tif\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 2160\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdal_translate -a_nodata 0.0 -of GTiff \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros.tif\" \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros_0_nd.tif\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
}
],
"source": [
"!gdal_calc.py --calc \"A*(A>-3.40282e+38)\" --format GTiff --type Float32 --NoDataValue 0.0 -A \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros_0_nd.tif\" --A_band 1 --outfile \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros_NoData.tif\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<string>:1: RuntimeWarning: divide by zero encountered in true_divide\n",
"<string>:1: RuntimeWarning: invalid value encountered in multiply\n",
"/opt/conda/lib/python3.8/site-packages/osgeo/utils/gdal_calc.py:367: RuntimeWarning: invalid value encountered in multiply\n",
" myResult = ((1 * (myNDVs == 0)) * myResult) + (myOutNDV * myNDVs)\n",
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 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94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#calculate global cotton deforestation risk in 2000\n",
"!gdal_calc.py -A $total_deforestation -B \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_ext_v2_NoData.tif\" -C \"../../datasets/raw/crop_data/default_pasture/CroplandPastureArea2000_Geotiff/Pasture2000_5m_yield_v5_NoZeros_NoData.tif\" --outfile='../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_v2.tif' --calc=\"A*B*(1/C)*0.0001\""
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=180.0, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.4028234663852886e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.08333333333333333, 0.0, -180.0,\n",
" 0.0, -0.08333333333333333, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default pasture risk map\n",
"a = rasterio.open('../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_v2.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### NOTE: \n",
"\n",
"- check deforestation risk in qgis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='carbonRisk'></a>\n",
"### 3.3 Carbon emissions due to land use change risk\n",
"\n",
"Notes for the carbon risk methodology can be found [here](https://docs.google.com/document/d/19D1WKgLqVelLlG98aHHoYxhaj6VJHH4bcRTPY33HmJ8/edit#). The formula for estimating the carbon emission risk due to land use change (tCO2/ year) would be:\n",
"\n",
" Carbon emissions due to land use change (tCO2e / year) = total tree cover area lost (Hectare) * Net forest GHG flux - 2001-2020 (tCO₂e / Hectare) * 10000/19 (unit conversion) * probability purchase area (unitless) \n",
"\n",
" \n",
"However, as the final impact metric would be dependent on the probability purchase area, for this first iteration we would be estimating alternative the deforestation metric withouth this alternative approcah and therefore we will use the following formula to estimate the risk map:\n",
"\n",
" Carbon emissions due to land use change (tCO2e / year *tonne) = deforestation risk (ha/year * tonne) * Net forest GHG flux - 2001-2020 (tCO₂e / Hectare) * 10000/19 (unit conversion)\n",
"\n",
"NOTE: For calculating this first mock data we will be using, temporarily, [the carbon emissions estimations from earthstat.](http://www.earthstat.org/greenhouse-gas-emissions-croplands/)\n",
"\n",
"#### Exploring carbon emissions\n",
"\n",
"The carbon emissions dataset exploration has been done in the notebooj 4_carbon_indicator_v2. "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"total_carbon_emissions_2000 = '../../datasets/raw/carbon_indicators/GHGEmissions_Geotiff/total_emissions.tif'\n",
"cotton_deforestation_risk_2000 = '../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000.tif'\n",
"rubber_deforestation_risk_2000 = '../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000.tif'\n",
"pasture_deforestation_risk_2000 = '../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000.tif'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'blockxsize': 432, 'blockysize': 224, 'tiled': True, 'compress': 'packbits', 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default pasture risk map\n",
"a = rasterio.open(total_carbon_emissions_2000)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Cotton emissions due to land use change risk:\n",
"\n",
"As we have no data vlues for the deforestation risk dataset, we need to set those to 0 in order to compute the emissions risk and remove the inf values ffrom the result dataset. For that we will do the following:\n",
"\n",
"- translate deforestation dataset with gdal to set no data values to 0\n",
"- gdal calc to get the deforestation risk dataset with no data values\n",
"- produce the final calculation"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 2160\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdal_translate -a_nodata 0.0 -of GTiff '../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000.tif' '../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000_0_nd.tif'"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"!gdal_calc.py --calc \"A*(A<3.40282e+38)\" --format GTiff --type Float32 --NoDataValue 0.0 -A '../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000_0_nd.tif' --A_band 1 --outfile '../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000_NoData.tif'"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.. 1.. 2.. 3.. 4.. 5.. 6.. 7.. 8.. 9.. 10.. 11.. 12.. 13.. 14.. 15.. 16.. 17.. 18.. 19.. 20.. 21.. 22.. 23.. 24.. 25.. 26.. 27.. 28.. 29.. 30.. 31.. 32.. 33.. 34.. 35.. 36.. 37.. 38.. 39.. 40.. 41.. 42.. 43.. 44.. 45.. 46.. 47.. 48.. 49.. 50.. 51.. 52.. 53.. 54.. 55.. 56.. 57.. 58.. 59.. 60.. 61.. 62.. 63.. 64.. 65.. 66.. 67.. 68.. 69.. 70.. 71.. 72.. 73.. 74.. 75.. 76.. 77.. 78.. 79.. 80.. 81.. 82.. 83.. 84.. 85.. 86.. 87.. 88.. 89.. 90.. 91.. 92.. 93.. 94.. 95.. 96.. 97.. 98.. 99.. 100 - Done\r\n"
]
}
],
"source": [
"#calculate global cotton deforestation risk in 2000\n",
"!gdal_calc.py -A $total_carbon_emissions_2000 -B '../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000_NoData.tif' --outfile='../../datasets/processed/carbon_indicators/carbon_emissions_risk_cotton_4326_2000.tif' --calc=\"A*B\""
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default pasture risk map\n",
"a = rasterio.open('../../datasets/processed/carbon_indicators/carbon_emissions_risk_cotton_4326_2000.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Rubber emissions due to land use change risk:\n",
"\n",
"We will do the same for the rubber and pasture."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 2160\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdal_translate -a_nodata 0.0 -of GTiff '../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000.tif' '../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000_0_nd.tif'"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"!gdal_calc.py --calc \"A*(A<3.40282e+38)\" --format GTiff --type Float32 --NoDataValue 0.0 -A '../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000_0_nd.tif' --A_band 1 --outfile '../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000_NoData.tif'"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.. 1.. 2.. 3.. 4.. 5.. 6.. 7.. 8.. 9.. 10.. 11.. 12.. 13.. 14.. 15.. 16.. 17.. 18.. 19.. 20.. 21.. 22.. 23.. 24.. 25.. 26.. 27.. 28.. 29.. 30.. 31.. 32.. 33.. 34.. 35.. 36.. 37.. 38.. 39.. 40.. 41.. 42.. 43.. 44.. 45.. 46.. 47.. 48.. 49.. 50.. 51.. 52.. 53.. 54.. 55.. 56.. 57.. 58.. 59.. 60.. 61.. 62.. 63.. 64.. 65.. 66.. 67.. 68.. 69.. 70.. 71.. 72.. 73.. 74.. 75.. 76.. 77.. 78.. 79.. 80.. 81.. 82.. 83.. 84.. 85.. 86.. 87.. 88.. 89.. 90.. 91.. 92.. 93.. 94.. 95.. 96.. 97.. 98.. 99.. 100 - Done\r\n"
]
}
],
"source": [
"#calculate global cotton deforestation risk in 2000\n",
"!gdal_calc.py -A $total_carbon_emissions_2000 -B '../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000_NoData.tif' --outfile='../../datasets/processed/carbon_indicators/carbon_emissions_risk_rubber_4326_2000.tif' --calc=\"A*B\""
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default pasture risk map\n",
"a = rasterio.open('../../datasets/processed/carbon_indicators/carbon_emissions_risk_rubber_4326_2000.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Pasture emissions due to land use change risk:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 4320, 2160\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdal_translate -a_nodata 0.0 -of GTiff '../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_v2.tif' '../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_v2_0_nd_v2.tif'"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 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94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"!gdal_calc.py --calc \"A*(A>-3.40282e+38)\" --format GTiff --type Float32 --NoDataValue 0.0 -A '../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_v2_0_nd_v2.tif' --A_band 1 --outfile '../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_NoData_v2.tif'"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.. 1.. 2.. 3.. 4.. 5.. 6.. 7.. 8.. 9.. 10.. 11.. 12.. 13.. 14.. 15.. 16.. 17.. 18.. 19.. 20.. 21.. 22.. 23.. 24.. 25.. 26.. 27.. 28.. 29.. 30.. 31.. 32.. 33.. 34.. 35.. 36.. 37.. 38.. 39.. 40.. 41.. 42.. 43.. 44.. 45.. 46.. 47.. 48.. 49.. 50.. 51.. 52.. 53.. 54.. 55.. 56.. 57.. 58.. 59.. 60.. 61.. 62.. 63.. 64.. 65.. 66.. 67.. 68.. 69.. 70.. 71.. 72.. 73.. 74.. 75.. 76.. 77.. 78.. 79.. 80.. 81.. 82.. 83.. 84.. 85.. 86.. 87.. 88.. 89.. 90.. 91.. 92.. 93.. 94.. 95.. 96.. 97.. 98.. 99.. 100 - Done\r\n"
]
}
],
"source": [
"!gdal_calc.py -A $total_carbon_emissions_2000 -B '../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_NoData_v2.tif' --outfile='../../datasets/processed/carbon_indicators/carbon_emissions_risk_pasture_4326_2000_v2.tif' --calc=\"A*B\""
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-90.0, right=179.99999999997954, top=89.99999999998977)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 89.99999999998977), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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F0pCkPZm2CdciaX1a/4Ck9Y3uqwN19UkaTNttl6S1mWUfS3Xtl3R5pr3jz7ekpZIelPSkpL2SbkrthW63MeoqfLtJOlfSo5IeT7V9IrVfKOmRdD9fkzQrtZ+T5gfS8jeMV3MXartd0qHMdluZ2nN7H6TbnC7pe5LuS/OFb7OGIqJ0F2A68H3gjcAs4HFgRQF1PAPMH9X2aWBjmt4I/GWaXgv8CyDgYuCRDtfybmAVsKfdWoB5wMF0PTdNz+1CXX3AHzdYd0V6Ls8BLkzP8fRuPd/AImBVmn4V8HSqodDtNkZdhW+39NjPT9MzgUfStrgLuCq1fxH4gzR9PfDFNH0V8LWxau5SbbcDv95g/dzeB+l2/xD4CnBfmi98mzW6lLWnvxoYiIiDEfEz4E5gXcE1DVsHbE3TW4ErM+13RN3DwBylfxzfCRHxEDD630tOtJbLge0RcSIinge2A2u6UFcz64A7I+KnEXGI+r/UXE2Xnu+IOBIR303TPwaeAhZT8HYbo65mcttu6bH/JM3OTJcA3gPcndpHb7PhbXk38F5JGqPmbtTWTG7vA0lLgPcDf5fmRQm2WSNlDf3FwHOZ+cOM/abolgC+JWmnpA2pbWHU/9E7wA+AhWm6iJonWkueNd6YPlJvGR4+KbKu9BH67dR7h6XZbqPqghJstzRMsQsYoh6I3wd+FBGnG9zP2RrS8heA1+ZVW0QMb7c/T9vts5LOGV3bqBq6UdvngD8BzqT511KSbTZaWUO/LN4VEauAK4AbJL07uzDqn8lKcc5rmWoBbgXeBKwEjgCfKbIYSecD9wAfjogXs8uK3G4N6irFdouIlyJiJbCEek9zeRF1NDK6NklvBT5GvcZfoj5k89E8a5L0AWAoInbmeb/tKmvoDwJLM/NLUluuImIwXQ8B36T+Bjg6PGyTrofS6kXUPNFacqkxIo6mN+cZ4DZe/oiae12SZlIP1i9HxDdSc+HbrVFdZdpuqZ4fAQ8Cv0x9aGT4t7qy93O2hrT8NcAPc6xtTRoui4j4KfAP5L/d3gl8UNIz1IfY3gP8LSXbZmd1+iBBJy7UfwjuIPWDGcMHqN6Scw3nAa/KTP8X9XG/v2LkQcBPp+n3M/Kg0aNdqOkNjDxgOqFaqPeCDlE/eDU3Tc/rQl2LMtMfoT5OCfAWRh6oOkj9YGRXnu/0+O8APjeqvdDtNkZdhW83YAEwJ03/HPAfwAeArzPyoOT1afoGRh6UvGusmrtU26LMdv0ccHMR74N025fw8oHcwrdZwxo7fYMdK6x+5P1p6uOJHy/g/t+YnoDHgb3DNVAfe9sBHAD+bfjFkl5Yn0/1PgHUOlzPV6l/5D9FfazvunZqAX6X+gGiAeDaLtX1j+l+dwPbGBlmH0917Qeu6ObzDbyL+tDNbmBXuqwteruNUVfh2w14G/C9VMMe4M8y74dH0+P/OnBOaj83zQ+k5W8cr+Yu1PbvabvtAf6Jl8/wye19kLndS3g59AvfZo0u/hkGM7MKKeuYvpmZdYFD38ysQhz6ZmYV4tA3M6sQh76ZWYU49M3MKsShb2ZWIf8Pe2woPC+kZT0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#explore default pasture risk map\n",
"a = rasterio.open('../../datasets/processed/carbon_indicators/carbon_emissions_risk_pasture_4326_2000_v2.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='biodiversityRisk'></a>\n",
"### 3.4 Biodiverstity loss due to land use change risk\n",
"\n",
"Notes for the iodiversity risk due to land use change methodology can be found [here](https://docs.google.com/document/d/1MqkKVFG5jZkAMGh3ZZHROhhc6y4PA11l8qysI1cSjF0/edit#). The formula for estimating the biodiversity risk due to land use change (PDF / year) would be:\n",
"\n",
" Biodiversity loss due to land use change (PDF / year) = = area with tree cover loss and land use change (hectares) * taxa-aggregated characterization factors for Regional Potential Species Loss (PSL) (PDF / year * m2) * (1/ 0.0001) (conversion units) * probability purchase area (unitless) \n",
"\n",
" \n",
"However, as the final impact metric would be dependent on the probability purchase area, for this first iteration we would be estimating alternative the deforestation metric withouth this alternative approcah and therefore we will use the following formula to estimate the risk map:\n",
"\n",
" Biodiversity loss due to land use change (PDF / year *tonnes) = deforestation risk (ha/year * tonne) *taxa-aggregated characterization factors for Regional Potential Species Loss (PSL) (PDF / year * m2) * (1/ 0.0001) (conversion units)\n",
" \n",
"\n",
"#### Exploring cotton biodiversity impact due to land use change\n",
"\n",
"The carbon emissions dataset exploration has been done in the notebook 1_biodiversity_indicator. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"taxa_cf_2000 = '../../datasets/processed/biodiversity_indicators/taxa_aggregated_characterization_factors_4326.tif'"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99999999998977, right=179.99999999997954, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 0.0, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = rasterio.open(taxa_cf_2000)\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"!gdal_calc.py -A $taxa_cf_2000 -B '../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000_NoData.tif' --outfile='../../datasets/processed/biodiversity_indicators/biodiversity_loss_risk_cotton_4326_2000.tif' --calc=\"A*B*(1/ 0.0001)\""
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99999999998977, right=179.99999999997954, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = rasterio.open('../../datasets/processed/biodiversity_indicators/biodiversity_loss_risk_cotton_4326_2000.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exploring rubber biodiversity impact due to land use change"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"!gdal_calc.py -A $taxa_cf_2000 -B '../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000_NoData.tif' --outfile='../../datasets/processed/biodiversity_indicators/biodiversity_loss_risk_rubber_4326_2000.tif' --calc=\"A*B*(1/ 0.0001)\""
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPSG:4326\n",
"BoundingBox(left=-180.0, bottom=-89.99999999998977, right=179.99999999997954, top=90.0)\n",
"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': 3.402823466e+38, 'width': 4320, 'height': 2160, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0833333333333286, 0.0, -180.0,\n",
" 0.0, -0.0833333333333286, 90.0), 'tiled': False, 'interleave': 'band'}\n"
]
},
{
"data": {
"image/png": 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++Q2tbaCq6lC7PwJ8kd4H4InJYZt2f6StPoya51rLQGqsqifah/M54BO88CvqwOtKcjK9YP1kVX2hNQ+936ara5T6rdXzA+CrwG/QGxqZvFZX/3aer6Et/wXgewOsbUsbLquq+jHwzwy+314P/HaSR+gNsb0J+HtGrM+et9gHCRbjRu9CcA/RO5gxeYDqNQOu4TTgFX3T/0Nv3O9vOfYg4Afb9Fs49qDRnUtQ09kce8B0TrXQ2wt6mN7Bq9Vt+owlqGtd3/Qf0RunBHgNxx6oeojewcgleb/b678R+MiU9qH22yx1Db3fgDXA6W3654D/At4KfJZjD0q+s01fxbEHJW+ereYlqm1dX79+BPjAMD4H7bkv4oUDuUPvs2lrXOwnXLTCekfev0NvPPG9Q9j+q9ob8E3g/ska6I293QYcAP5j8oel/WB9tNV7LzC2yPV8mt6v/D+lN9Z35XxqAX6f3gGiCeCKJarrX9p27wH2cmyYvbfV9SBw6VK+38Ab6A3d3APc3W5bh91vs9Q19H4DfgX4RqvhPuAv+z4Pd7bX/1ng1Nb+0jY/0Za/6ng1L0Ft/9n67T7gX3nhDJ+BfQ76nvciXgj9offZdDcvwyBJHTKqY/qSpCVg6EtShxj6ktQhhr4kdYihL0kdYuhLUocY+pLUIf8PKbQjcnspsAMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a = rasterio.open('../../datasets/processed/biodiversity_indicators/biodiversity_loss_risk_rubber_4326_2000.tif')\n",
"print(a.crs)\n",
"print(a.bounds)\n",
"print(a.profile)\n",
"plt.imshow(a.read(1), cmap='Oranges')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exploring rubber biodiversity impact due to land use change"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 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20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"!gdal_calc.py -A $taxa_cf_2000 -B '../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_NoData_v2.tif' --outfile='../../datasets/processed/biodiversity_indicators/biodiversity_loss_risk_pasture_4326_2000_v2.tif' --calc=\"A*B*(1/ 0.0001)\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='metric'></a>\n",
"## 4. Get metrics for user data\n",
"\n",
"Do the zonal stats on the geometries."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"## ruber\n",
"rubber_water_risk = '../../datasets/processed/water_indicators/water_risk_rubber_4326_2000_v2.tif'\n",
"rubber_deforestation_risk = '../../datasets/processed/deforestation_indicators/deforestation_risk_rubber_4326_2000_NoData.tif'\n",
"rubber_carbon_risk = '../../datasets/processed/carbon_indicators/carbon_emissions_risk_rubber_4326_2000_v2.tif'\n",
"rubber_biodiversity_risk = '../../datasets/processed/biodiversity_indicators/biodiversity_loss_risk_rubber_4326_2000_v2.tif'\n",
"\n",
"# cotton\n",
"cotton_water_risk = '../../datasets/processed/water_indicators/water_risk_cotton_4326_2000_v2.tif'\n",
"cotton_deforestation_risk = '../../datasets/processed/deforestation_indicators/deforestation_risk_cotton_4326_2000_NoData.tif'\n",
"cotton_carbon_risk = '../../datasets/processed/carbon_indicators/carbon_emissions_risk_cotton_4326_2000_v2.tif'\n",
"cotton_biodiversity_risk = '../../datasets/processed/biodiversity_indicators/biodiversity_loss_risk_cotton_4326_2000_v2.tif'\n",
"\n",
"# pasture\n",
"pasture_water_risk = \"../../datasets/processed/water_indicators/water_risk_pasture_4326_2000_v11.tif\"\n",
"pasture_deforestation_risk = '../../datasets/processed/deforestation_indicators/deforestation_risk_pasture_4326_2000_NoData_v2.tif'\n",
"pasture_carbon_risk = '../../datasets/processed/carbon_indicators/carbon_emissions_risk_pasture_4326_2000_v3.tif'\n",
"pasture_biodiversity_risk = '../../datasets/processed/biodiversity_indicators/biodiversity_loss_risk_pasture_4326_2000_v3.tif'"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Material</th>\n",
" <th>Material d</th>\n",
" <th>Volume</th>\n",
" <th>Country</th>\n",
" <th>Address</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Location t</th>\n",
" <th>Accuracy</th>\n",
" <th>geometry</th>\n",
" <th>...</th>\n",
" <th>cr_mean</th>\n",
" <th>cr_median</th>\n",
" <th>cr_std</th>\n",
" <th>cr_min</th>\n",
" <th>cr_max</th>\n",
" <th>bio_mean</th>\n",
" <th>bio_median</th>\n",
" <th>bio_std</th>\n",
" <th>bio_min</th>\n",
" <th>bio_max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>2400</td>\n",
" <td>China</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((73.49973 39.38174, 73.50468 39...</td>\n",
" <td>...</td>\n",
" <td>inf</td>\n",
" <td>inf</td>\n",
" <td>NaN</td>\n",
" <td>2.813154e-08</td>\n",
" <td>3.402823e+38</td>\n",
" <td>inf</td>\n",
" <td>inf</td>\n",
" <td>NaN</td>\n",
" <td>1.384501e-14</td>\n",
" <td>3.402823e+38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>1300</td>\n",
" <td>Malaysia</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((98.93721 5.68384, 98.93771 5.6...</td>\n",
" <td>...</td>\n",
" <td>inf</td>\n",
" <td>inf</td>\n",
" <td>NaN</td>\n",
" <td>3.402823e+38</td>\n",
" <td>3.402823e+38</td>\n",
" <td>inf</td>\n",
" <td>inf</td>\n",
" <td>NaN</td>\n",
" <td>3.402823e+38</td>\n",
" <td>3.402823e+38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>1000</td>\n",
" <td>United States</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((-180.00000 51.79409, -180.0000...</td>\n",
" <td>...</td>\n",
" <td>inf</td>\n",
" <td>3.402823e+38</td>\n",
" <td>NaN</td>\n",
" <td>3.402823e+38</td>\n",
" <td>3.402823e+38</td>\n",
" <td>inf</td>\n",
" <td>3.402823e+38</td>\n",
" <td>NaN</td>\n",
" <td>3.402823e+38</td>\n",
" <td>3.402823e+38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>730</td>\n",
" <td>Japan</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((122.71418 24.44983, 122.71457 ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>490</td>\n",
" <td>India</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((68.11138 23.60145, 68.13528 23...</td>\n",
" <td>...</td>\n",
" <td>inf</td>\n",
" <td>3.201923e+01</td>\n",
" <td>NaN</td>\n",
" <td>-2.434870e+02</td>\n",
" <td>3.402823e+38</td>\n",
" <td>inf</td>\n",
" <td>4.421980e-10</td>\n",
" <td>NaN</td>\n",
" <td>-5.642628e-10</td>\n",
" <td>3.402823e+38</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" Material Material d Volume Country Address Latitude Longitude \\\n",
"0 Rubber None 2400 China None None None \n",
"1 Rubber None 1300 Malaysia None None None \n",
"2 Rubber None 1000 United States None None None \n",
"3 Rubber None 730 Japan None None None \n",
"4 Rubber None 490 India None None None \n",
"\n",
" Location t Accuracy geometry ... \\\n",
"0 Unknown Low MULTIPOLYGON (((73.49973 39.38174, 73.50468 39... ... \n",
"1 Unknown Low MULTIPOLYGON (((98.93721 5.68384, 98.93771 5.6... ... \n",
"2 Unknown Low MULTIPOLYGON (((-180.00000 51.79409, -180.0000... ... \n",
"3 Unknown Low MULTIPOLYGON (((122.71418 24.44983, 122.71457 ... ... \n",
"4 Unknown Low MULTIPOLYGON (((68.11138 23.60145, 68.13528 23... ... \n",
"\n",
" cr_mean cr_median cr_std cr_min cr_max bio_mean \\\n",
"0 inf inf NaN 2.813154e-08 3.402823e+38 inf \n",
"1 inf inf NaN 3.402823e+38 3.402823e+38 inf \n",
"2 inf 3.402823e+38 NaN 3.402823e+38 3.402823e+38 inf \n",
"3 NaN NaN NaN NaN NaN NaN \n",
"4 inf 3.201923e+01 NaN -2.434870e+02 3.402823e+38 inf \n",
"\n",
" bio_median bio_std bio_min bio_max \n",
"0 inf NaN 1.384501e-14 3.402823e+38 \n",
"1 inf NaN 3.402823e+38 3.402823e+38 \n",
"2 3.402823e+38 NaN 3.402823e+38 3.402823e+38 \n",
"3 NaN NaN NaN NaN \n",
"4 4.421980e-10 NaN -5.642628e-10 3.402823e+38 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"supply_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- 13.230469942092896 seconds ---\n"
]
}
],
"source": [
"#water risk\n",
"water_risk_mean_list = []\n",
"water_risk_median_list = []\n",
"water_risk_std_list = []\n",
"water_risk_max_list = []\n",
"water_risk_min_list = []\n",
"\n",
"#deforestation risk\n",
"deforestation_risk_mean_list = []\n",
"deforestation_risk_median_list = []\n",
"deforestation_risk_std_list = []\n",
"deforestation_risk_max_list = []\n",
"deforestation_risk_min_list = []\n",
"\n",
"#carbon risk\n",
"carbon_risk_mean_list = []\n",
"carbon_risk_median_list = []\n",
"carbon_risk_std_list = []\n",
"carbon_risk_max_list = []\n",
"carbon_risk_min_list = []\n",
"\n",
"#biodiversity risk\n",
"biodiversity_risk_mean_list = []\n",
"biodiversity_risk_median_list = []\n",
"biodiversity_risk_std_list = []\n",
"biodiversity_risk_max_list = []\n",
"biodiversity_risk_min_list = []\n",
"\n",
"start_time = time.time()\n",
"for i, row in supply_data.iterrows():\n",
" material = row['Material']\n",
" geom = row['geometry']\n",
"\n",
" if material == 'Rubber':\n",
" #calculate risk for water\n",
" water_stats = zonal_stats(geom, rubber_water_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for deforestation\n",
" deforestation_stats = zonal_stats(geom, rubber_deforestation_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for carbon\n",
" carbon_stats = zonal_stats(geom, rubber_carbon_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for biodiversity\n",
" biodiversity_stats = zonal_stats(geom, rubber_biodiversity_risk,\n",
" stats=\"mean median std max min\")\n",
" \n",
" if material == 'Cotton':\n",
" #calculate risk for water\n",
" water_stats = zonal_stats(geom, cotton_water_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for deforestation\n",
" deforestation_stats = zonal_stats(geom, cotton_deforestation_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for carbon\n",
" carbon_stats = zonal_stats(geom, cotton_carbon_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for biodiversity\n",
" biodiversity_stats = zonal_stats(geom, cotton_biodiversity_risk,\n",
" stats=\"mean median std max min\")\n",
" \n",
" \n",
" if material == 'Leather':\n",
" #calculate risk for water \n",
" water_stats = zonal_stats(geom, pasture_water_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for deforestation\n",
" deforestation_stats = zonal_stats(geom, pasture_deforestation_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for carbon\n",
" carbon_stats = zonal_stats(geom, pasture_carbon_risk,\n",
" stats=\"mean median std max min\")\n",
" #calculate risk for biodiversity\n",
" biodiversity_stats = zonal_stats(geom, pasture_biodiversity_risk,\n",
" stats=\"mean median std max min\")\n",
"\n",
" \n",
" ##APPEND RISK\n",
" #water risk\n",
" water_risk_mean_list.append(water_stats[0]['mean'])\n",
" water_risk_median_list.append(water_stats[0]['median'])\n",
" water_risk_std_list.append(water_stats[0]['std'])\n",
" water_risk_max_list.append(water_stats[0]['max'])\n",
" water_risk_min_list.append(water_stats[0]['min'])\n",
" \n",
" #deforestation risk\n",
" deforestation_risk_mean_list.append(deforestation_stats[0]['mean'])\n",
" deforestation_risk_median_list.append(deforestation_stats[0]['median'])\n",
" deforestation_risk_std_list.append(deforestation_stats[0]['std'])\n",
" deforestation_risk_max_list.append(deforestation_stats[0]['max'])\n",
" deforestation_risk_min_list.append(deforestation_stats[0]['min'])\n",
"\n",
" #carbon risk\n",
" carbon_risk_mean_list.append(carbon_stats[0]['mean'])\n",
" carbon_risk_median_list.append(carbon_stats[0]['median'])\n",
" carbon_risk_std_list.append(carbon_stats[0]['std'])\n",
" carbon_risk_max_list.append(carbon_stats[0]['max'])\n",
" carbon_risk_min_list.append(carbon_stats[0]['min'])\n",
"\n",
" #biodiversity risk\n",
" biodiversity_risk_mean_list.append(biodiversity_stats[0]['mean'])\n",
" biodiversity_risk_median_list.append(biodiversity_stats[0]['median'])\n",
" biodiversity_risk_std_list.append(biodiversity_stats[0]['std'])\n",
" biodiversity_risk_max_list.append(biodiversity_stats[0]['max'])\n",
" biodiversity_risk_min_list.append(biodiversity_stats[0]['min'])\n",
" \n",
"print(\"--- %s seconds ---\" % (time.time() - start_time))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Len of supply data:46\n",
"water Len of stats: 46\n",
"water Len of stats: 46\n",
"water Len of stats: 46\n",
"water Len of stats: 46\n",
"water Len of stats: 46\n",
"deforestation Len of stats: 46\n",
"deforestation Len of stats: 46\n",
"deforestation Len of stats: 46\n",
"deforestation Len of stats: 46\n",
"deforestation Len of stats: 46\n",
"carbon Len of stats: 46\n",
"carbon Len of stats: 46\n",
"carbon Len of stats: 46\n",
"carbon Len of stats: 46\n",
"carbon Len of stats: 46\n",
"biodiversity Len of stats: 46\n",
"biodiversity Len of stats: 46\n",
"biodiversity Len of stats: 46\n",
"biodiversity Len of stats: 46\n",
"biodiversity Len of stats: 46\n"
]
}
],
"source": [
"print(f'Len of supply data:{len(supply_data)}')\n",
"print(f'water Len of stats: {len(water_risk_mean_list)}')\n",
"print(f'water Len of stats: {len(water_risk_median_list)}')\n",
"print(f'water Len of stats: {len(water_risk_std_list)}')\n",
"print(f'water Len of stats: {len(water_risk_min_list)}')\n",
"print(f'water Len of stats: {len(water_risk_max_list)}')\n",
"print(f'deforestation Len of stats: {len(deforestation_risk_mean_list)}')\n",
"print(f'deforestation Len of stats: {len(deforestation_risk_median_list)}')\n",
"print(f'deforestation Len of stats: {len(deforestation_risk_std_list)}')\n",
"print(f'deforestation Len of stats: {len(deforestation_risk_min_list)}')\n",
"print(f'deforestation Len of stats: {len(deforestation_risk_max_list)}')\n",
"print(f'carbon Len of stats: {len(carbon_risk_mean_list)}')\n",
"print(f'carbon Len of stats: {len(carbon_risk_median_list)}')\n",
"print(f'carbon Len of stats: {len(carbon_risk_std_list)}')\n",
"print(f'carbon Len of stats: {len(carbon_risk_min_list)}')\n",
"print(f'carbon Len of stats: {len(carbon_risk_max_list)}')\n",
"print(f'biodiversity Len of stats: {len(biodiversity_risk_mean_list)}')\n",
"print(f'biodiversity Len of stats: {len(biodiversity_risk_median_list)}')\n",
"print(f'biodiversity Len of stats: {len(biodiversity_risk_std_list)}')\n",
"print(f'biodiversity Len of stats: {len(biodiversity_risk_min_list)}')\n",
"print(f'biodiversity Len of stats: {len(biodiversity_risk_max_list)}')\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"## apend to supply dataframe\n",
"supply_data['wr_mean'] = water_risk_mean_list\n",
"supply_data['wr_median'] = water_risk_median_list\n",
"supply_data['wr_std'] = water_risk_std_list\n",
"supply_data['wr_max'] = water_risk_max_list\n",
"supply_data['wr_min'] = water_risk_min_list\n",
"supply_data['df_mean'] = deforestation_risk_mean_list\n",
"supply_data['df_median'] = deforestation_risk_median_list\n",
"supply_data['df_std'] = deforestation_risk_std_list\n",
"supply_data['df_min'] = deforestation_risk_min_list\n",
"supply_data['df_max'] = deforestation_risk_max_list\n",
"supply_data['cr_mean'] = carbon_risk_mean_list\n",
"supply_data['cr_median'] = carbon_risk_median_list\n",
"supply_data['cr_std'] = carbon_risk_std_list\n",
"supply_data['cr_min'] = carbon_risk_min_list\n",
"supply_data['cr_max'] = carbon_risk_max_list\n",
"supply_data['bio_mean'] = biodiversity_risk_mean_list\n",
"supply_data['bio_median'] = biodiversity_risk_median_list\n",
"supply_data['bio_std'] = biodiversity_risk_std_list\n",
"supply_data['bio_min'] = biodiversity_risk_min_list\n",
"supply_data['bio_max'] = biodiversity_risk_max_list"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Material</th>\n",
" <th>Material d</th>\n",
" <th>Volume</th>\n",
" <th>Country</th>\n",
" <th>Address</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Location t</th>\n",
" <th>Accuracy</th>\n",
" <th>geometry</th>\n",
" <th>...</th>\n",
" <th>cr_mean</th>\n",
" <th>cr_median</th>\n",
" <th>cr_std</th>\n",
" <th>cr_min</th>\n",
" <th>cr_max</th>\n",
" <th>bio_mean</th>\n",
" <th>bio_median</th>\n",
" <th>bio_std</th>\n",
" <th>bio_min</th>\n",
" <th>bio_max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>2400</td>\n",
" <td>China</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((73.49973 39.38174, 73.50468 39...</td>\n",
" <td>...</td>\n",
" <td>2.783592e-13</td>\n",
" <td>0.000000</td>\n",
" <td>8.849066e-11</td>\n",
" <td>0.000000</td>\n",
" <td>2.813154e-08</td>\n",
" <td>1.369952e-19</td>\n",
" <td>0.000000e+00</td>\n",
" <td>4.355090e-17</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.384501e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>1300</td>\n",
" <td>Malaysia</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((98.93721 5.68384, 98.93771 5.6...</td>\n",
" <td>...</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>1000</td>\n",
" <td>United States</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((-180.00000 51.79409, -180.0000...</td>\n",
" <td>...</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>730</td>\n",
" <td>Japan</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((122.71418 24.44983, 122.71457 ...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rubber</td>\n",
" <td>None</td>\n",
" <td>490</td>\n",
" <td>India</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>Unknown</td>\n",
" <td>Low</td>\n",
" <td>MULTIPOLYGON (((68.11138 23.60145, 68.13528 23...</td>\n",
" <td>...</td>\n",
" <td>1.307325e+03</td>\n",
" <td>31.787052</td>\n",
" <td>1.153720e+04</td>\n",
" <td>-243.486969</td>\n",
" <td>6.503950e+05</td>\n",
" <td>2.728085e-08</td>\n",
" <td>4.168217e-10</td>\n",
" <td>7.774599e-08</td>\n",
" <td>-5.642628e-10</td>\n",
" <td>7.001327e-07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" Material Material d Volume Country Address Latitude Longitude \\\n",
"0 Rubber None 2400 China None None None \n",
"1 Rubber None 1300 Malaysia None None None \n",
"2 Rubber None 1000 United States None None None \n",
"3 Rubber None 730 Japan None None None \n",
"4 Rubber None 490 India None None None \n",
"\n",
" Location t Accuracy geometry ... \\\n",
"0 Unknown Low MULTIPOLYGON (((73.49973 39.38174, 73.50468 39... ... \n",
"1 Unknown Low MULTIPOLYGON (((98.93721 5.68384, 98.93771 5.6... ... \n",
"2 Unknown Low MULTIPOLYGON (((-180.00000 51.79409, -180.0000... ... \n",
"3 Unknown Low MULTIPOLYGON (((122.71418 24.44983, 122.71457 ... ... \n",
"4 Unknown Low MULTIPOLYGON (((68.11138 23.60145, 68.13528 23... ... \n",
"\n",
" cr_mean cr_median cr_std cr_min cr_max \\\n",
"0 2.783592e-13 0.000000 8.849066e-11 0.000000 2.813154e-08 \n",
"1 0.000000e+00 0.000000 0.000000e+00 0.000000 0.000000e+00 \n",
"2 0.000000e+00 0.000000 0.000000e+00 0.000000 0.000000e+00 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 1.307325e+03 31.787052 1.153720e+04 -243.486969 6.503950e+05 \n",
"\n",
" bio_mean bio_median bio_std bio_min bio_max \n",
"0 1.369952e-19 0.000000e+00 4.355090e-17 0.000000e+00 1.384501e-14 \n",
"1 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"2 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"3 NaN NaN NaN NaN NaN \n",
"4 2.728085e-08 4.168217e-10 7.774599e-08 -5.642628e-10 7.001327e-07 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"supply_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"supply_data.to_file('../../datasets/processed/user_data/located_lg_data_polygon_v2_stats.shp', driver='ESRI Shapefile',)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='finalNotes'></a>\n",
"## 5. Final notes\n",
"\n",
"- We should clean the data with no data information before performing any caculation\n",
"- Need to extend the notebook to cover the distributed area methodology and produce a final heatmap."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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