data/notebooks/Lab/0_5_Area_distribution.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "2d57f2ef",
"metadata": {},
"source": [
"## The case for Area as the fundamental unit\n",
"\n",
"This notebook contains a prototype for exploring the case for area as the fundamental unit of measure instead of the volume, following the description here: https://docs.google.com/document/d/1GVUEv8l9T3zogKvIThCUAWN_tbZdpu2-MbE3PyOQepw/edit#heading=h.9qcog0t53hjq\n",
"\n",
"The idea behind would be to use Purchase Area as equivalent to Volume (i.e. _what is the area required to produce the purchased volume?_)\n",
"\n",
"For that we need the yield in each location to distribute that area. Ths can be done following this formula if we have pixel-resolution yield:\n",
"\n",
"\tf’ = f.V / a.y Σf\n",
" \n",
"- f': fractional area used to produce the commodity such that the sum of all pixels within some area of interest equals the Purchase Area (unitless)\n",
"\n",
"- f: the (estimated) fractional area used to to produce the commodity, where Σf is equal to the total possible commodity production area (unitless)\n",
"\n",
"- a: pixel area (ha)\n",
"\n",
"- y: the yeild of the commodity in a given pixel (t/ha)\n",
"\n",
"- V: the known purchased Volume (t)\n",
"\n",
"\n",
"## Table of Contents\n",
"- ### [1. Import libraries](#libraries)\n",
"- ### [2. Import user data](#user_data)\n",
"- ### [3. Generate purchase area probability map](#probability_map)\n",
" - #### [3.1. Generate raster with volume info](#volume)\n",
" - #### [3.2. Obtain raster with total fraction area](#area_fraction)\n",
" - #### [3.2.1. Calculate raster with pixel area](#pixel_area)\n",
" - #### [3.2.2. Calculate raster for total area fraction](#total_area)\n",
" - #### [3.2.2. Calculate zonal statistics for user data and raster](#zonal_stats)\n",
" - #### [3.3. Calculate probability map](#calculation)\n",
"- ### [4. Final notes](#finalNotes)"
]
},
{
"cell_type": "markdown",
"id": "ae9ae989",
"metadata": {},
"source": [
"<a id='libraries'></a>\n",
"### 1. Import libraries:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b94c9394",
"metadata": {},
"outputs": [],
"source": [
"## import libraries\n",
"import geopandas as gpd\n",
"import time\n",
"\n",
"from rasterstats import zonal_stats\n",
"import rasterio as rio\n",
"import rasterio.plot\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "784910ef",
"metadata": {},
"source": [
"<a id='user_data'></a>\n",
"### 2. Import user data\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4543bfca",
"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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## import user data\n",
"## projection of user data is epsg:4326\n",
"input_data = gpd.read_file('../../datasets/processed/user_data/located_lg_data_polygon_v2.shp') \n",
"input_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d75d1802",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The uniques commodities from the user data are: {'Leather', 'Cotton', 'Rubber'}\n"
]
}
],
"source": [
"## check the commodities materials - we will need to generate a distribution map for each of the commodities.\n",
"\n",
"print(f'The uniques commodities from the user data are:', set(input_data['Material']))"
]
},
{
"cell_type": "markdown",
"id": "984e8f86",
"metadata": {},
"source": [
"We are going to use a test location from the user data to generate the purchase area probability map. Therefore we're going to select a test location from the gdf above:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "405b41a9",
"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>18</th>\n",
" <td>Cotton</td>\n",
" <td>None</td>\n",
" <td>745</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 Location t \\\n",
"18 Cotton None 745 India None None None Unknown \n",
"\n",
" Accuracy geometry \n",
"18 Low MULTIPOLYGON (((68.11138 23.60145, 68.13528 23... "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_location = input_data.loc[(input_data['Material']=='Cotton') & (input_data['Country']=='India') & ((input_data['Volume']==745) )]\n",
"test_location"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e2c5d668",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"projection of user data is: EPSG:4326\n"
]
}
],
"source": [
"test_location = test_location.set_crs(\"EPSG:4326\")\n",
"print(f'projection of user data is: {test_location.crs}')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "f065daca",
"metadata": {},
"outputs": [],
"source": [
"#reproject to epsg3857\n",
"test_location = test_location.to_crs(\"EPSG:3857\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "9413dcde",
"metadata": {},
"outputs": [],
"source": [
"test_location.to_file('../../datasets/raw/probability_map/test_location_epsg3857.shp', driver='ESRI Shapefile')"
]
},
{
"cell_type": "markdown",
"id": "c3dc9918",
"metadata": {},
"source": [
"<a id='probability_map'></a>\n",
"### 3. Generate purchase area probability map\n",
"\n",
"For the purchase area probability map we will need:\n",
"\n",
"- The purchased volume which is provided by the user as we can see above\n",
"- The area fraction for the commodity of interest (in this case cotton) - obtained from EarthStat\n",
"- The yield of the commodity of interest (in this case cotton) - obtained from EarthStat\n",
"\n",
"The formula (per pixel) is:\n",
"\n",
" f'= Ap*Af/At\n",
" \n",
" where:\n",
" Ap = Volume/pixel_yield\n",
" Af = Area fraction for that user\n",
" At = Total area fraction in area of interest\n",
" \n",
" NOTE: The probability map test is going to be produced just for a commodity and for a particular location. However this would need to be updated for the differenet commodities and for all the user data.\n",
" \n",
"In that case we can produce an intermediate dataset whit a world coverage for each commodity following the formula:\n",
"\n",
" f'' = Af (commodity) / Yield(commodity)\n",
" \n",
" This could be multiplied by the user volume and the total area within a particular location later on."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6b7893ff",
"metadata": {},
"outputs": [],
"source": [
"yield_cotton = '../../datasets/raw/crop_data/cotton_YieldPerHectare.tif'\n",
"harvest_portion_cotton = '../../datasets/raw/crop_data/cotton_HarvestedAreaFraction.tif'\n"
]
},
{
"cell_type": "markdown",
"id": "d3ce865b",
"metadata": {},
"source": [
"<a id='volume'></a>\n",
"#### 3.1. Generate raster with volume info"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "5ec49bd4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: ../../datasets/raw/crop_data/cotton_HarvestedAreaFraction.tif\n",
" ../../datasets/raw/crop_data/cotton_HarvestedAreaFraction.tif.aux.xml\n",
"Size is 4320, 2160\n",
"Coordinate System is:\n",
"GEOGCRS[\"WGS 84\",\n",
" DATUM[\"World Geodetic System 1984\",\n",
" ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
" LENGTHUNIT[\"metre\",1]]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" CS[ellipsoidal,2],\n",
" AXIS[\"geodetic latitude (Lat)\",north,\n",
" ORDER[1],\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" AXIS[\"geodetic longitude (Lon)\",east,\n",
" ORDER[2],\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" ID[\"EPSG\",4326]]\n",
"Data axis to CRS axis mapping: 2,1\n",
"Origin = (-180.000000000000000,89.999999999989768)\n",
"Pixel Size = (0.083333333333329,-0.083333333333329)\n",
"Metadata:\n",
" AREA_OR_POINT=Area\n",
" TIFFTAG_SOFTWARE=MATLAB 8.0, Mapping Toolbox 3.6\n",
"Image Structure Metadata:\n",
" COMPRESSION=PACKBITS\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left (-180.0000000, 90.0000000) (180d 0' 0.00\"W, 90d 0' 0.00\"N)\n",
"Lower Left (-180.0000000, -90.0000000) (180d 0' 0.00\"W, 90d 0' 0.00\"S)\n",
"Upper Right ( 180.0000000, 90.0000000) (180d 0' 0.00\"E, 90d 0' 0.00\"N)\n",
"Lower Right ( 180.0000000, -90.0000000) (180d 0' 0.00\"E, 90d 0' 0.00\"S)\n",
"Center ( -0.0000000, -0.0000000) ( 0d 0' 0.00\"W, 0d 0' 0.00\"S)\n",
"Band 1 Block=432x224 Type=Float32, ColorInterp=Gray\n",
" Min=0.000 Max=1.909 \n",
" Minimum=0.000, Maximum=1.909, Mean=0.002, StdDev=0.018\n",
" 256 buckets from -0.00374299 to 1.91267:\n",
" 2110053 41165 15154 8384 5224 3545 2988 2914 2242 1815 1631 1315 1197 1059 850 604 633 645 562 617 602 774 784 317 237 211 189 146 162 128 110 88 124 79 76 82 69 87 58 105 41 33 32 38 36 17 27 29 28 20 34 26 31 25 24 17 24 15 19 14 27 20 18 15 19 22 18 24 29 20 18 8 13 11 20 8 11 13 6 9 13 4 6 10 9 11 4 7 5 7 7 5 7 8 5 15 9 7 8 3 3 5 4 4 3 4 2 3 2 3 8 6 4 2 7 0 2 4 0 4 5 1 4 2 2 1 1 1 2 3 3 2 6 2 4 2 3 2 15 1 0 0 0 1 1 2 2 2 0 0 0 2 0 0 0 1 1 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 \n",
" Metadata:\n",
" STATISTICS_MAXIMUM=1.9089248180389\n",
" STATISTICS_MEAN=0.0018505645049267\n",
" STATISTICS_MINIMUM=0\n",
" STATISTICS_STDDEV=0.017516194975611\n",
" STATISTICS_VALID_PERCENT=23.66\n"
]
}
],
"source": [
"!gdalinfo -stats -hist '../../datasets/raw/crop_data/cotton_HarvestedAreaFraction.tif'"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "9c6622c7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: ../../datasets/raw/crop_data/cotton_YieldPerHectare.tif\n",
" ../../datasets/raw/crop_data/cotton_YieldPerHectare.tif.aux.xml\n",
"Size is 4320, 2160\n",
"Coordinate System is:\n",
"GEOGCRS[\"WGS 84\",\n",
" DATUM[\"World Geodetic System 1984\",\n",
" ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
" LENGTHUNIT[\"metre\",1]]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" CS[ellipsoidal,2],\n",
" AXIS[\"geodetic latitude (Lat)\",north,\n",
" ORDER[1],\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" AXIS[\"geodetic longitude (Lon)\",east,\n",
" ORDER[2],\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" ID[\"EPSG\",4326]]\n",
"Data axis to CRS axis mapping: 2,1\n",
"Origin = (-180.000000000000000,89.999999999989768)\n",
"Pixel Size = (0.083333333333329,-0.083333333333329)\n",
"Metadata:\n",
" AREA_OR_POINT=Area\n",
" TIFFTAG_SOFTWARE=MATLAB 8.0, Mapping Toolbox 3.6\n",
"Image Structure Metadata:\n",
" COMPRESSION=PACKBITS\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left (-180.0000000, 90.0000000) (180d 0' 0.00\"W, 90d 0' 0.00\"N)\n",
"Lower Left (-180.0000000, -90.0000000) (180d 0' 0.00\"W, 90d 0' 0.00\"S)\n",
"Upper Right ( 180.0000000, 90.0000000) (180d 0' 0.00\"E, 90d 0' 0.00\"N)\n",
"Lower Right ( 180.0000000, -90.0000000) (180d 0' 0.00\"E, 90d 0' 0.00\"S)\n",
"Center ( -0.0000000, -0.0000000) ( 0d 0' 0.00\"W, 0d 0' 0.00\"S)\n",
"Band 1 Block=432x224 Type=Float32, ColorInterp=Gray\n",
" Min=0.000 Max=2.912 \n",
" Minimum=0.000, Maximum=11.050, Mean=0.289, StdDev=0.746\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n",
" 256 buckets from -0.0216672 to 11.0719:\n",
" 1786952 529 1262 4792 3497 4139 4274 3854 2139 16656 2655 10608 9624 6993 10570 8853 7318 7219 2762 3407 6272 6427 8229 6143 22811 6294 5699 11426 19586 7293 23411 3184 10354 4897 2700 2672 6162 5513 3926 9655 2683 1438 3004 2859 20293 2634 1462 2700 12762 1352 1668 5249 1497 3340 2500 1539 2509 2340 3723 4594 2234 1501 1444 898 2640 1583 1349 3588 1819 1949 1048 1323 1309 4714 635 1365 1006 1915 1408 980 697 1587 376 115 569 582 1241 833 509 472 1045 356 327 408 249 371 905 630 478 341 855 128 388 344 52 442 188 762 175 1176 1836 309 149 721 206 243 187 2 660 66 43 15 518 33 0 39 60 112 318 0 0 1 0 16 0 13 0 0 0 2 0 0 11 0 0 0 0 0 0 37 120 0 0 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 44 0 0 46 0 0 0 0 0 0 0 0 0 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 90 \n",
" Metadata:\n",
" STATISTICS_MAXIMUM=11.050269126892\n",
" STATISTICS_MEAN=0.28938767150612\n",
" STATISTICS_MINIMUM=0\n",
" STATISTICS_STDDEV=0.74634719219215\n",
" STATISTICS_VALID_PERCENT=23.66\n"
]
}
],
"source": [
"!gdalinfo -stats -hist '../../datasets/raw/crop_data/cotton_YieldPerHectare.tif'"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "97706e68",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 3325P x 36024L.\n",
"Processing ../../datasets/raw/crop_data/cotton_HarvestedAreaFraction.tif [1/1] : 0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"# reproject harvest area fraction\n",
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:3857 -tr 12051.131160772875 12051.131160772875 -r near -of GTiff ../../datasets/raw/crop_data/cotton_HarvestedAreaFraction.tif ../../datasets/raw/crop_data/cotton_HarvestedAreaFraction_epsg3857.tif"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "6572bb69",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 3325P x 36024L.\n",
"Processing ../../datasets/raw/crop_data/cotton_YieldPerHectare.tif [1/1] : 0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"# reproject yield \n",
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:3857 -tr 12051.131160772875 12051.131160772875 -r near -of GTiff ../../datasets/raw/crop_data/cotton_YieldPerHectare.tif ../../datasets/raw/crop_data/cotton_YieldPerHectare_epsg3857.tif"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "653d513c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: ../../datasets/raw/crop_data/cotton_HarvestedAreaFraction_epsg3857.tif\n",
"Size is 3325, 36024\n",
"Coordinate System is:\n",
"PROJCRS[\"WGS 84 / Pseudo-Mercator\",\n",
" BASEGEOGCRS[\"WGS 84\",\n",
" ENSEMBLE[\"World Geodetic System 1984 ensemble\",\n",
" MEMBER[\"World Geodetic System 1984 (Transit)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G730)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G873)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1150)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1674)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1762)\"],\n",
" ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
" LENGTHUNIT[\"metre\",1]],\n",
" ENSEMBLEACCURACY[2.0]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" ID[\"EPSG\",4326]],\n",
" CONVERSION[\"Popular Visualisation Pseudo-Mercator\",\n",
" METHOD[\"Popular Visualisation Pseudo Mercator\",\n",
" ID[\"EPSG\",1024]],\n",
" PARAMETER[\"Latitude of natural origin\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433],\n",
" ID[\"EPSG\",8801]],\n",
" PARAMETER[\"Longitude of natural origin\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433],\n",
" ID[\"EPSG\",8802]],\n",
" PARAMETER[\"False easting\",0,\n",
" LENGTHUNIT[\"metre\",1],\n",
" ID[\"EPSG\",8806]],\n",
" PARAMETER[\"False northing\",0,\n",
" LENGTHUNIT[\"metre\",1],\n",
" ID[\"EPSG\",8807]]],\n",
" CS[Cartesian,2],\n",
" AXIS[\"easting (X)\",east,\n",
" ORDER[1],\n",
" LENGTHUNIT[\"metre\",1]],\n",
" AXIS[\"northing (Y)\",north,\n",
" ORDER[2],\n",
" LENGTHUNIT[\"metre\",1]],\n",
" USAGE[\n",
" SCOPE[\"Web mapping and visualisation.\"],\n",
" AREA[\"World between 85.06°S and 85.06°N.\"],\n",
" BBOX[-85.06,-180,85.06,180]],\n",
" ID[\"EPSG\",3857]]\n",
"Data axis to CRS axis mapping: 1,2\n",
"Origin = (-20037508.342789243906736,191642979.083299666643143)\n",
"Pixel Size = (12051.131160772874864,-12051.131160772874864)\n",
"Metadata:\n",
" AREA_OR_POINT=Area\n",
" TIFFTAG_SOFTWARE=MATLAB 8.0, Mapping Toolbox 3.6\n",
"Image Structure Metadata:\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left (-20037508.343,191642979.083) (180d 0' 0.00\"W, 90d 0' 0.00\"N)\n",
"Lower Left (-20037508.343,-242486969.852) (180d 0' 0.00\"W, 90d 0' 0.00\"S)\n",
"Upper Right (20032502.767,191642979.083) (179d57'18.12\"E, 90d 0' 0.00\"N)\n",
"Lower Right (20032502.767,-242486969.852) (179d57'18.12\"E, 90d 0' 0.00\"S)\n",
"Center ( -2502.788,-25421995.385) ( 0d 1'20.94\"W, 87d52'17.10\"S)\n",
"Band 1 Block=3325x1 Type=Float32, ColorInterp=Gray\n",
" Minimum=0.000, Maximum=1.909, Mean=0.000, StdDev=0.005\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n",
" 256 buckets from -0.00374299 to 1.91267:\n",
" 15825808 27432 10109 5624 3603 2484 2077 2024 1560 1247 1109 933 789 745 608 437 446 452 414 408 418 556 549 220 171 164 142 110 111 88 82 56 92 58 55 62 51 63 38 84 26 27 18 21 23 11 16 19 23 12 25 20 24 16 14 11 15 13 12 9 15 12 11 12 12 15 9 13 20 13 12 5 10 8 16 6 7 7 3 7 9 2 3 7 6 7 3 7 3 5 4 3 6 3 4 11 6 4 5 2 1 2 4 3 3 2 0 3 2 1 5 3 3 2 5 0 1 4 0 1 4 0 2 2 1 1 1 1 2 2 2 1 5 1 3 1 2 2 7 1 0 0 0 0 1 1 1 1 0 0 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 \n",
" Metadata:\n",
" STATISTICS_MAXIMUM=1.9089248180389\n",
" STATISTICS_MEAN=0.00017691249737549\n",
" STATISTICS_MINIMUM=0\n",
" STATISTICS_STDDEV=0.0054078695166677\n",
" STATISTICS_VALID_PERCENT=13.27\n"
]
}
],
"source": [
"#check infor of the reprojected raster\n",
"!gdalinfo -stats -hist '../../datasets/raw/crop_data/cotton_HarvestedAreaFraction_epsg3857.tif'\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "4c9899a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: ../../datasets/raw/crop_data/cotton_YieldPerHectare_epsg3857.tif\n",
"Size is 3325, 36024\n",
"Coordinate System is:\n",
"PROJCRS[\"WGS 84 / Pseudo-Mercator\",\n",
" BASEGEOGCRS[\"WGS 84\",\n",
" ENSEMBLE[\"World Geodetic System 1984 ensemble\",\n",
" MEMBER[\"World Geodetic System 1984 (Transit)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G730)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G873)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1150)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1674)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1762)\"],\n",
" ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
" LENGTHUNIT[\"metre\",1]],\n",
" ENSEMBLEACCURACY[2.0]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" ID[\"EPSG\",4326]],\n",
" CONVERSION[\"Popular Visualisation Pseudo-Mercator\",\n",
" METHOD[\"Popular Visualisation Pseudo Mercator\",\n",
" ID[\"EPSG\",1024]],\n",
" PARAMETER[\"Latitude of natural origin\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433],\n",
" ID[\"EPSG\",8801]],\n",
" PARAMETER[\"Longitude of natural origin\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433],\n",
" ID[\"EPSG\",8802]],\n",
" PARAMETER[\"False easting\",0,\n",
" LENGTHUNIT[\"metre\",1],\n",
" ID[\"EPSG\",8806]],\n",
" PARAMETER[\"False northing\",0,\n",
" LENGTHUNIT[\"metre\",1],\n",
" ID[\"EPSG\",8807]]],\n",
" CS[Cartesian,2],\n",
" AXIS[\"easting (X)\",east,\n",
" ORDER[1],\n",
" LENGTHUNIT[\"metre\",1]],\n",
" AXIS[\"northing (Y)\",north,\n",
" ORDER[2],\n",
" LENGTHUNIT[\"metre\",1]],\n",
" USAGE[\n",
" SCOPE[\"Web mapping and visualisation.\"],\n",
" AREA[\"World between 85.06°S and 85.06°N.\"],\n",
" BBOX[-85.06,-180,85.06,180]],\n",
" ID[\"EPSG\",3857]]\n",
"Data axis to CRS axis mapping: 1,2\n",
"Origin = (-20037508.342789243906736,191642979.083299666643143)\n",
"Pixel Size = (12051.131160772874864,-12051.131160772874864)\n",
"Metadata:\n",
" AREA_OR_POINT=Area\n",
" TIFFTAG_SOFTWARE=MATLAB 8.0, Mapping Toolbox 3.6\n",
"Image Structure Metadata:\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left (-20037508.343,191642979.083) (180d 0' 0.00\"W, 90d 0' 0.00\"N)\n",
"Lower Left (-20037508.343,-242486969.852) (180d 0' 0.00\"W, 90d 0' 0.00\"S)\n",
"Upper Right (20032502.767,191642979.083) (179d57'18.12\"E, 90d 0' 0.00\"N)\n",
"Lower Right (20032502.767,-242486969.852) (179d57'18.12\"E, 90d 0' 0.00\"S)\n",
"Center ( -2502.788,-25421995.385) ( 0d 1'20.94\"W, 87d52'17.10\"S)\n",
"Band 1 Block=3325x1 Type=Float32, ColorInterp=Gray\n",
" Minimum=0.000, Maximum=11.050, Mean=0.027, StdDev=0.248\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n",
" 256 buckets from -0.0216672 to 11.0719:\n",
" 15615060 339 783 2931 2268 2524 2723 2398 1314 10403 1665 6545 5837 4356 6718 5546 4558 4441 1742 2125 3977 4234 5130 4002 13812 3996 3587 7026 12168 4661 14025 2034 6714 3219 1777 1865 4325 3816 2661 5921 1791 969 2189 1891 18141 1743 1067 1918 8037 929 1092 3762 963 2243 1792 1088 1687 1593 2455 3393 1587 987 1029 577 1831 1008 928 2415 1221 1317 757 934 861 3216 430 955 730 1397 1006 706 501 1143 254 83 419 434 861 636 346 324 765 262 244 302 200 281 691 457 375 281 604 97 305 246 35 315 149 548 137 827 1333 249 98 493 139 169 138 2 472 42 32 8 352 19 0 28 45 80 217 0 0 1 0 12 0 8 0 0 0 1 0 0 8 0 0 0 0 0 0 29 92 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0 35 0 0 0 0 0 0 0 0 0 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 68 \n",
" Metadata:\n",
" STATISTICS_MAXIMUM=11.050269126892\n",
" STATISTICS_MEAN=0.027370458163487\n",
" STATISTICS_MINIMUM=0\n",
" STATISTICS_STDDEV=0.24773890204693\n",
" STATISTICS_VALID_PERCENT=13.27\n"
]
}
],
"source": [
"!gdalinfo -stats -hist '../../datasets/raw/crop_data/cotton_YieldPerHectare_epsg3857.tif'"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "f40d966e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"## generate a raster with same resolution as the yield and harvest portion\n",
"## with the user volume\n",
"\n",
"## this should be run by each of the user data - producing a final raster with the total volume for each location\n",
"!gdal_rasterize -l test_location_epsg3857 -a Volume -tr 12051.131160772875 12051.131160772875 -a_nodata 0.0 -te -20037508.3428 -242486969.8524 20032502.7668 191642979.0833 -ot Float32 -of GTiff '../../datasets/raw/probability_map/test_location_epsg3857.shp' '../../datasets/raw/probability_map/test_location_raster_volume_epsg3857.tif'"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "f48e2693",
"metadata": {},
"outputs": [],
"source": [
"## add projection - same as the other ones for the calculations\n",
"!gdal_edit.py -a_srs EPSG:3857 '../../datasets/raw/probability_map/test_location_raster_volume_epsg3857.tif'"
]
},
{
"cell_type": "markdown",
"id": "7acd6560",
"metadata": {},
"source": [
"<a id='area_fraction'></a>\n",
"### 3.2. Obtain raster with total fraction area"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "4e872ea8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The pixel area is 145229762.254151 m2\n"
]
}
],
"source": [
"## calculate pixel area based on pixel size\n",
"pixel_area = 12051.131160772874864 * 12051.131160772874864\n",
"print(f'The pixel area is {pixel_area} m2')\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "2c94a9e3",
"metadata": {},
"source": [
"<a id='pixel_area'></a>\n",
"#### 3.2.1. Calculate raster with pixel area"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "fbc18d9e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 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.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 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99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 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": [
"#generate raster with pixel area raster\n",
"# reclasifies the raster into 0 and pixel area being the pixel area just on thise locations with harvest area fraction\n",
"!gdal_calc.py -A '../../datasets/raw/crop_data/cotton_HarvestedAreaFraction_epsg3857.tif' --outfile='../../datasets/raw/probability_map/pixel_area_cotton_raster_epsg3857.tif' --calc=\"(A > 0) * 145229762.254151\""
]
},
{
"cell_type": "markdown",
"id": "aaaf6339",
"metadata": {},
"source": [
"<a id='total_area'></a>\n",
"#### 3.2.2. Calculate raster for total area fraction"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "d7b90fc0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 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 the raster equal to pixel_are * area fraction to get the total area within a loctaion with summary statistics\n",
"!gdal_calc.py -A '../../datasets/raw/probability_map/pixel_area_cotton_raster_epsg3857.tif' -B '../../datasets/raw/crop_data/cotton_HarvestedAreaFraction_epsg3857.tif' --outfile='../../datasets/raw/probability_map/area_total_cotton_raster_epsg3857.tif' --calc=\"(A * B)\""
]
},
{
"cell_type": "markdown",
"id": "2b5992b5",
"metadata": {},
"source": [
"<a id='zonal_stats'></a>\n",
"#### 3.2.2. Calculate zonal statistics for user data and raster\n",
"\n",
"NOTE: We will need to extend this to all user data"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "e59156e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- 0.19099926948547363 seconds ---\n"
]
}
],
"source": [
"#zonal stats in india to get the sum of all fraction harvest area\n",
"total_harves_area_cotton = '../../datasets/raw/probability_map/area_total_cotton_raster_epsg3857.tif'\n",
"start_time = time.time()\n",
"zs_india_test = zonal_stats(\n",
" test_location,\n",
" total_harves_area_cotton,\n",
" stats=\"sum\")\n",
"print(\"--- %s seconds ---\" % (time.time() - start_time))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "c699c9ad",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" The total cotton harvest area in india is : {83570565120.0} m2\n"
]
}
],
"source": [
"print(f' The total cotton harvest area in india is :', {zs_india_test[0]['sum']}, 'm2')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "e867b684",
"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>Total_af</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Cotton</td>\n",
" <td>None</td>\n",
" <td>745</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 (((7582123.994 2704917.800, 75847...</td>\n",
" <td>8.357057e+10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Material Material d Volume Country Address Latitude Longitude Location t \\\n",
"18 Cotton None 745 India None None None Unknown \n",
"\n",
" Accuracy geometry Total_af \n",
"18 Low MULTIPOLYGON (((7582123.994 2704917.800, 75847... 8.357057e+10 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## ad field to gdf\n",
"test_location['Total_af'] = zs_india_test[0]['sum']\n",
"test_location"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "ce6d7b80",
"metadata": {},
"outputs": [],
"source": [
"test_location.to_file('../../datasets/raw/probability_map/test_location_epsg3857.shp', driver='ESRI Shapefile')"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "c808a082",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"## generate a raster with same extent as the other ones with this total area fraction value\n",
"!gdal_rasterize -l test_location_epsg3857 -a Total_af -tr 12051.131160772875 12051.131160772875 -a_nodata 0.0 -te -20037508.3428 -242486969.8524 20032502.7668 191642979.0833 -ot Float32 -of GTiff '../../datasets/raw/probability_map/test_location_epsg3857.shp' '../../datasets/raw/probability_map/test_location_raster_total_af.tif'\n"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "9c396787",
"metadata": {},
"outputs": [],
"source": [
"## add projection - same as the other ones for the calculations\n",
"!gdal_edit.py -a_srs EPSG:3857 '../../datasets/raw/probability_map/test_location_raster_total_af.tif'"
]
},
{
"cell_type": "markdown",
"id": "d878793e",
"metadata": {},
"source": [
"<a id='calculation'></a>\n",
"### 3.3. Calculate probability map\n",
"\n",
"The formula would be: \n",
"\n",
" f' = (V * Af) / (AT * Yield)\n",
" \n",
"to do this we will need the folloing datasets:\n",
"\n",
"- volume raster derived from user data: '../../datasets/raw/probability_map/test_location_raster_volume_epsg3857.tif'\n",
"- area fraction for a particular commodity: '../../datasets/raw/crop_data/cotton_HarvestedAreaFraction_epsg3857.tif'\n",
"- the total area fraction for the user location: '../../datasets/raw/probability_map/area_total_cotton_raster_epsg3857.tif'\n",
"- yield information for a particular commodity: '../../datasets/raw/crop_data/cotton_YieldPerHectare_epsg3857.tif'\n",
" \n",
"NOTE: all the rasters have been reprojected to epsg 3857."
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "c5d2fe6a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 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.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 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"text": [
"<string>:1: RuntimeWarning: invalid value encountered in true_divide\n"
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]
}
],
"source": [
"!gdal_calc.py -A '../../datasets/raw/probability_map/test_location_raster_volume_epsg3857.tif' -B '../../datasets/raw/crop_data/cotton_HarvestedAreaFraction_epsg3857.tif' -C '../../datasets/raw/probability_map/area_total_cotton_raster_epsg3857.tif' -D '../../datasets/raw/crop_data/cotton_YieldPerHectare_epsg3857.tif' --outfile='../../datasets/processed/probability_map/purchase_area_distribution_cotton_epsg3857.tif' --calc=\"(A*B)/(C*D)\""
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "35e856b5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: GTiff/GeoTIFF\n",
"Files: ../../datasets/processed/probability_map/purchase_area_distribution_cotton_epsg3857.tif\n",
"Size is 3325, 36024\n",
"Coordinate System is:\n",
"PROJCRS[\"WGS 84 / Pseudo-Mercator\",\n",
" BASEGEOGCRS[\"WGS 84\",\n",
" ENSEMBLE[\"World Geodetic System 1984 ensemble\",\n",
" MEMBER[\"World Geodetic System 1984 (Transit)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G730)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G873)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1150)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1674)\"],\n",
" MEMBER[\"World Geodetic System 1984 (G1762)\"],\n",
" ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
" LENGTHUNIT[\"metre\",1]],\n",
" ENSEMBLEACCURACY[2.0]],\n",
" PRIMEM[\"Greenwich\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
" ID[\"EPSG\",4326]],\n",
" CONVERSION[\"Popular Visualisation Pseudo-Mercator\",\n",
" METHOD[\"Popular Visualisation Pseudo Mercator\",\n",
" ID[\"EPSG\",1024]],\n",
" PARAMETER[\"Latitude of natural origin\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433],\n",
" ID[\"EPSG\",8801]],\n",
" PARAMETER[\"Longitude of natural origin\",0,\n",
" ANGLEUNIT[\"degree\",0.0174532925199433],\n",
" ID[\"EPSG\",8802]],\n",
" PARAMETER[\"False easting\",0,\n",
" LENGTHUNIT[\"metre\",1],\n",
" ID[\"EPSG\",8806]],\n",
" PARAMETER[\"False northing\",0,\n",
" LENGTHUNIT[\"metre\",1],\n",
" ID[\"EPSG\",8807]]],\n",
" CS[Cartesian,2],\n",
" AXIS[\"easting (X)\",east,\n",
" ORDER[1],\n",
" LENGTHUNIT[\"metre\",1]],\n",
" AXIS[\"northing (Y)\",north,\n",
" ORDER[2],\n",
" LENGTHUNIT[\"metre\",1]],\n",
" USAGE[\n",
" SCOPE[\"Web mapping and visualisation.\"],\n",
" AREA[\"World between 85.06°S and 85.06°N.\"],\n",
" BBOX[-85.06,-180,85.06,180]],\n",
" ID[\"EPSG\",3857]]\n",
"Data axis to CRS axis mapping: 1,2\n",
"Origin = (-20037508.342799998819828,191642979.083299994468689)\n",
"Pixel Size = (12051.131160772874864,-12051.131160772874864)\n",
"Metadata:\n",
" AREA_OR_POINT=Area\n",
"Image Structure Metadata:\n",
" INTERLEAVE=BAND\n",
"Corner Coordinates:\n",
"Upper Left (-20037508.343,191642979.083) (180d 0' 0.00\"E, 90d 0' 0.00\"N)\n",
"Lower Left (-20037508.343,-242486969.852) (180d 0' 0.00\"E, 90d 0' 0.00\"S)\n",
"Upper Right (20032502.767,191642979.083) (179d57'18.12\"E, 90d 0' 0.00\"N)\n",
"Lower Right (20032502.767,-242486969.852) (179d57'18.12\"E, 90d 0' 0.00\"S)\n",
"Center ( -2502.788,-25421995.385) ( 0d 1'20.94\"W, 87d52'17.10\"S)\n",
"Band 1 Block=3325x1 Type=Float32, ColorInterp=Gray\n",
" Minimum=0.000, Maximum=0.000, Mean=0.000, StdDev=0.000\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n",
" 256 buckets from 1.84589e-06 to 0.000133166:\n",
" 16 10 544 467 103 700 471 1080 897 1163 1402 1692 1131 915 237 289 557 620 448 458 8 2905 6 913 0 313 2 665 17 2 504 3 0 245 0 1 1 1049 0 1 2 1 0 262 0 0 3 0 0 638 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 \n",
" NoData Value=3.40282346600000016e+38\n",
" Metadata:\n",
" STATISTICS_MAXIMUM=0.000132909452077\n",
" STATISTICS_MEAN=1.0740570812899e-05\n",
" STATISTICS_MINIMUM=2.1023777208029e-06\n",
" STATISTICS_STDDEV=5.6940475240774e-06\n",
" STATISTICS_VALID_PERCENT=0.01732\n"
]
}
],
"source": [
"!gdalinfo -stats -hist '../../datasets/processed/probability_map/purchase_area_distribution_cotton_epsg3857.tif'"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "1dd076a3",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"with rio.open('../../datasets/processed/probability_map/purchase_area_distribution_cotton_epsg3857.tif') as src:\n",
" image_array = src.read(1)\n",
" fig, ax = plt.subplots(figsize=[15,10])\n",
" ax.set_ylim((695174.093781,4.255931e+06))\n",
" ax.set_xlim((7.582124e+06,1.084202e+07))\n",
" rio.plot.show(image_array, vmin=2.1023777208029e-06, vmax=1.0740570812899e-05, cmap='Oranges', ax=ax, transform=src.transform)\n",
" ax.set_title('Geospatial responsibility - test location')"
]
},
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"<a id='finalNotes'></a>\n",
"### 4. Final notes\n",
"\n",
"Based on the calculations done above:\n",
"\n",
"- we can generate a worldwide pixel area raster instead of the one specific for each commodity\n",
"\n",
"- we need to extend everyting to all the user data"
]
},
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