diff --git a/Pipfile b/Pipfile index 1eb3f2d..dd63684 100644 --- a/Pipfile +++ b/Pipfile @@ -43,9 +43,11 @@ catboost = "*" shap = "*" modapsclient = "*" pyqt5 = "*" +ipykernel = "*" +pyreadstat = "*" [dev-packages] autopep8 = "*" [requires] -python_version = "3.8" +python_version = "3.9" diff --git a/Pipfile.lock b/Pipfile.lock index e41d83c..e041b0a 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -1,11 +1,11 @@ { "_meta": { "hash": { - "sha256": "eab16c07adf0f12eb6edf1b07178c65a8f737f8c59286d03b6db016102a0f9ab" + "sha256": "a3e625e387a4f43ab3b14ea9d01f432dd2b513566d061c73d403a566b47d2a3d" }, "pipfile-spec": 6, "requires": { - "python_version": "3.8" + "python_version": "3.9" }, "sources": [ { @@ -78,11 +78,11 @@ }, "attrs": { "hashes": [ - "sha256:31b2eced602aa8423c2aea9c76a724617ed67cf9513173fd3a4f03e3a929c7e6", - "sha256:832aa3cde19744e49938b91fea06d69ecb9e649c93ba974535d08ad92164f700" + "sha256:3901be1cb7c2a780f14668691474d9252c070a756be0a9ead98cfeabfa11aeb8", + "sha256:8ee1e5f5a1afc5b19bdfae4fdf0c35ed324074bdce3500c939842c8f818645d9" ], "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'", - 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"sha256:780082b43506eccb3fb39ed9306300b637245e622a9644701c60d89992468822", - "sha256:7d2cff62d0b0cf5267f5cd9edb4bd04591f68aa919bf026e7787f0424c0e7c55" + "sha256:b3fb2cb0049fa3b974e08b67b0f65504d31dc4a7f131275e52e9f91c19125777", + "sha256:ddaf8ae4e415dedd183d335650d30eb4419c58720f216ce9bafa68c6ea8b5dd1" ], "markers": "python_version >= '3.6'", - "version": "==0.9.2" + "version": "==0.10.1" }, "jupyter-server": { "hashes": [ @@ -1303,11 +1303,11 @@ }, "pandas-profiling": { "hashes": [ - "sha256:ccf1bf10f5e88516fd0d807462e553c2bfcf2c0939f231fb8818b243c72eb9a1", - "sha256:ee3c427436a0ba2a797c35b5c4dc04a1ebc7dc0b755ec48a05bd52552dc898fa" + "sha256:0cad96968b634033b4dd3347505f6730b4527e5853519cca89b62518df6ccf6d", + "sha256:3b62223f685a851c3c45a954ec4958fb9067dcc2570a0ee715fb017925153267" ], "index": "pypi", - "version": "==2.11.0" + "version": "==2.12.0" }, "pandocfilters": { "hashes": [ @@ -1517,11 +1517,11 @@ }, "pygments": { "hashes": [ - "sha256:2656e1a6edcdabf4275f9a3640db59fd5de107d88e8663c5d4e9a0fa62f77f94", - 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"markers": "python_version >= '3'", + "markers": "python_version >= '3.0'", "version": "==2.2.1" }, "statsmodels": { diff --git a/dssg/data-exploration/araria_voronoi.ipynb b/dssg/data-exploration/araria_voronoi.ipynb index a0b6226..79c8247 100644 --- a/dssg/data-exploration/araria_voronoi.ipynb +++ b/dssg/data-exploration/araria_voronoi.ipynb @@ -10,13 +10,18 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5-final" + "version": "3.9.0" }, "orig_nbformat": 2, "kernelspec": { - "name": "python38564bitwriindiaexthyhlp5re4d96c4cb0fb248a699be028bbf9263df", - "display_name": "Python 3.8.5 64-bit ('WRI_India_ext-hyHLP5Re')", + "name": "python385jvsc74a57bd0d4a53db61837b04487d02c25116133aa28f6a79c740d093360b2328df5f2ed08", + "display_name": "Python 3.8.5 64-bit ('WRI_WellBeing_Data_Layer-3UVuR9IU')", "language": "python" + }, + "metadata": { + "interpreter": { + "hash": "d4a53db61837b04487d02c25116133aa28f6a79c740d093360b2328df5f2ed08" + } } }, "nbformat": 4, @@ -24,52 +29,106 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd \n", + "import pandas as pd\n", + "import dssg.dataio.osm_data_extraction as ode\n", "import os\n", "from dotenv import load_dotenv\n", - "load_dotenv()" + "load_dotenv()\n", + "import pyreadstat" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Import the voronoi shape of India\n", "import dssg.dataio.osm_to_voronoi_mapping as ovm\n", "data_dir = os.environ.get(\"DATA_DIR\")\n", - "india_voronoi_gpd = gpd.read_file(data_dir + \"voronoi3_clip/voronoi3_clip.shp\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "india_voronoi_gpd = gpd.read_file(data_dir + \"voronoi/IAGE71FL_Voronoi_Clipped/IAGE71FL_Voronoi_Clipped.shp\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " DHSID DHSCLUST ADM1DHS DHSREGCO DHSREGNA URBAN_RURA \\\n", + "0 IA201400310502 310502.0 31.0 602.0 Thiruvallur R \n", + "1 IA201400310190 310190.0 31.0 602.0 Thiruvallur R \n", + "2 IA201400310070 310070.0 31.0 602.0 Thiruvallur R \n", + "3 IA201400310716 310716.0 31.0 602.0 Thiruvallur R \n", + "4 IA201400310592 310592.0 31.0 602.0 Thiruvallur R \n", + "\n", + " LATNUM LONGNUM ALT_DEM DATUM WEIGHT \\\n", + "0 13.320202 80.010414 39.0 WGS84 0.04504 \n", + "1 13.118380 79.803917 56.0 WGS84 0.04504 \n", + "2 13.147002 79.804755 44.0 WGS84 0.04504 \n", + "3 13.263585 80.188453 14.0 WGS84 0.04504 \n", + "4 13.261215 80.222348 18.0 WGS84 0.04504 \n", + "\n", + " geometry \n", + "0 POLYGON ((79.95699 13.28227, 79.95030 13.37055... \n", + "1 POLYGON ((79.77943 12.97075, 79.71600 12.99485... \n", + "2 POLYGON ((79.83122 13.24096, 79.87546 13.14136... \n", + "3 POLYGON ((80.20192 13.21268, 80.10237 13.25050... \n", + "4 POLYGON ((80.27104 13.24175, 80.20483 13.20947... 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DHSIDDHSCLUSTADM1DHSDHSREGCODHSREGNAURBAN_RURALATNUMLONGNUMALT_DEMDATUMWEIGHTgeometry
0IA201400310502310502.031.0602.0ThiruvallurR13.32020280.01041439.0WGS840.04504POLYGON ((79.95699 13.28227, 79.95030 13.37055...
1IA201400310190310190.031.0602.0ThiruvallurR13.11838079.80391756.0WGS840.04504POLYGON ((79.77943 12.97075, 79.71600 12.99485...
2IA201400310070310070.031.0602.0ThiruvallurR13.14700279.80475544.0WGS840.04504POLYGON ((79.83122 13.24096, 79.87546 13.14136...
3IA201400310716310716.031.0602.0ThiruvallurR13.26358580.18845314.0WGS840.04504POLYGON ((80.20192 13.21268, 80.10237 13.25050...
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" + }, + "metadata": {}, + "execution_count": 3 + } + ], "source": [ "india_voronoi_gpd.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "name 'os' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindia_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"DATA_DIR\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"/gadm36_shp/gadm36_IND_2.shp\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" + ] + } + ], "source": [ - "import dssg.dataio.osm_data_extraction as ode\n", + "\n", "india_shape = os.environ.get(\"DATA_DIR\") + \"/gadm36_shp/gadm36_IND_2.shp\"" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " \n*** Profile printout saved to text file '../profile/extract_district_dataframe'. \n" + ] + } + ], "source": [ "%%prun -s cumulative -q -l 10 -T ../profile/extract_district_dataframe\n", "import matplotlib.pyplot as plt \n", @@ -81,18 +140,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " 43085 function calls (39448 primitive calls) in 1.387 seconds\n\n Ordered by: cumulative time\n List reduced from 717 to 10 due to restriction <10>\n\n ncalls tottime percall cumtime percall filename:lineno(function)\n 1 0.000 0.000 1.387 1.387 {built-in method builtins.exec}\n 1 0.000 0.000 1.387 1.387 :1()\n 1 0.001 0.001 1.314 1.314 file.py:66(_read_file)\n 1 0.495 0.495 0.760 0.760 geodataframe.py:505(from_features)\n 1 0.000 0.000 0.388 0.388 env.py:231(__enter__)\n 1 0.019 0.019 0.388 0.388 env.py:279(defenv)\n 1 0.306 0.306 0.369 0.369 {method 'start' of 'fiona._env.GDALEnv' objects}\n 3 0.000 0.000 0.167 0.056 geodataframe.py:103(__init__)\n 666 0.003 0.000 0.133 0.000 geo.py:62(shape)\n 1475 0.031 0.000 0.111 0.000 polygon.py:500(geos_polygon_from_py)\n" + ] + } + ], "source": [ "print(open('../profile/extract_district_dataframe', 'r').read())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " \n*** Profile printout saved to text file '../profile/extract_district_voronoi_clipped'. \n" + ] + } + ], "source": [ "%%prun -s cumulative -q -l 10 -T ../profile/extract_district_voronoi_clipped\n", "#Extract the GeoDataFrame of the voronoi clipped to the district boundary\n", @@ -101,27 +176,78 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " 61054379 function calls (60252728 primitive calls) in 68.880 seconds\n\n Ordered by: cumulative time\n List reduced from 1480 to 10 due to restriction <10>\n\n ncalls tottime percall cumtime percall filename:lineno(function)\n 3/1 0.000 0.000 68.880 68.880 {built-in method builtins.exec}\n 1 0.026 0.026 68.880 68.880 :2()\n 1 0.002 0.002 68.835 68.835 osm_to_voronoi_mapping.py:43(extract_district_voronoi_clipped)\n 1 0.051 0.051 62.999 62.999 osm_data_extraction.py:56(create_district_knots_and_edges_model)\n 1 0.394 0.394 62.776 62.776 graph.py:354(graph_from_polygon)\n 2 0.039 0.020 38.037 19.019 truncate.py:120(truncate_graph_polygon)\n 2 0.029 0.014 24.032 12.016 utils_geo.py:339(_intersect_index_quadrats)\n 629 0.030 0.000 17.761 0.028 geodataframe.py:103(__init__)\n 633 0.015 0.000 16.340 0.026 geodataframe.py:201(set_geometry)\n 615 0.005 0.000 12.130 0.020 generic.py:3591(_take_with_is_copy)\n" + ] + } + ], "source": [ "print(open('../profile/extract_district_voronoi_clipped', 'r').read())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" 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DHSIDDHSCLUSTADM1DHSDHSREGCODHSREGNAURBAN_RURALATNUMLONGNUMALT_DEMDATUMWEIGHTgeometry
24079IA20140005104551045.05.0208.0SupaulR26.39938387.05247573.0WGS840.04504MULTIPOLYGON (((87.09631 26.39683, 87.05918 26...
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24099IA20140005046250462.05.0208.0SupaulR26.52517787.03342182.0WGS840.04504POLYGON ((87.08476 26.50668, 87.04326 26.49498...
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" + }, + "metadata": {}, + "execution_count": 10 + } + ], "source": [ "araria_voronoi_gpd_clipped.head()" ] @@ -165,9 +291,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " \n*** Profile printout saved to text file '../profile/extract_district_voronoi_wo_clipping'. \n" + ] + } + ], "source": [ "%%prun -s cumulative -q -l 10 -T ../profile/extract_district_voronoi_wo_clipping\n", "araria_voronoi_joined = ovm.extract_district_voronoi_wo_clipping(india_voronoi_gpd, araria_gdf)" @@ -175,27 +309,78 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " DHSID DHSCLUST ADM1DHS DHSREGCO DHSREGNA URBAN_RURA \\\n", + "24079 IA201400051045 51045.0 5.0 208.0 Supaul R \n", + "24081 IA201400051659 51659.0 5.0 208.0 Supaul R \n", + "24099 IA201400050462 50462.0 5.0 208.0 Supaul R \n", + "24111 IA201400050932 50932.0 5.0 208.0 Supaul R \n", + "24113 IA201400051032 51032.0 5.0 208.0 Supaul R \n", + "\n", + " LATNUM LONGNUM ALT_DEM DATUM WEIGHT \\\n", + "24079 26.399383 87.052475 73.0 WGS84 0.04504 \n", + "24081 26.464088 87.050647 80.0 WGS84 0.04504 \n", + "24099 26.525177 87.033421 82.0 WGS84 0.04504 \n", + "24111 26.145699 87.065582 58.0 WGS84 0.04504 \n", + "24113 26.083933 87.084018 55.0 WGS84 0.04504 \n", + "\n", + " geometry index_right NAME_2 \n", + "24079 POLYGON ((87.09631 26.39683, 87.03554 26.33137... 61 Araria \n", + "24081 POLYGON ((87.08912 26.48314, 87.06270 26.43205... 61 Araria \n", + "24099 POLYGON ((87.08567 26.50694, 87.01083 26.48583... 61 Araria \n", + "24111 POLYGON ((87.06560 26.21225, 87.10250 26.20820... 61 Araria \n", + "24113 POLYGON ((87.15693 26.03450, 87.14677 26.01600... 61 Araria " + ], + "text/html": "
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DHSIDDHSCLUSTADM1DHSDHSREGCODHSREGNAURBAN_RURALATNUMLONGNUMALT_DEMDATUMWEIGHTgeometryindex_rightNAME_2
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" + }, + "metadata": {}, + "execution_count": 12 + } + ], "source": [ "araria_voronoi_joined.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " 3264283 function calls (3263945 primitive calls) in 5.041 seconds\n\n Ordered by: cumulative time\n List reduced from 719 to 10 due to restriction <10>\n\n ncalls tottime percall cumtime percall filename:lineno(function)\n 1 0.000 0.000 5.041 5.041 {built-in method builtins.exec}\n 1 0.000 0.000 5.041 5.041 :1()\n 1 0.000 0.000 5.041 5.041 osm_to_voronoi_mapping.py:68(extract_district_voronoi_wo_clipping)\n 1 0.004 0.004 5.041 5.041 sjoin.py:9(sjoin)\n 1 0.000 0.000 4.673 4.673 sjoin.py:146(_geom_predicate_query)\n 1 0.113 0.113 4.665 4.665 sindex.py:441(query_bulk)\n 28393 0.182 0.000 4.274 0.000 sindex.py:361(query)\n 28394 0.077 0.000 2.309 0.000 base.py:473(bounds)\n 28394 0.745 0.000 1.991 0.000 coords.py:164(__call__)\n 28393 0.056 0.000 1.157 0.000 sindex.py:453(intersection)\n" + ] + } + ], "source": [ "print(open('../profile/extract_district_voronoi_wo_clipping', 'r').read())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ "\n", "ovm.plot_district_voronoi(araria_voronoi_joined, \"Araria\")" @@ -203,52 +388,108 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/media/veracrypt1/.virtualenvs/WRI_WellBeing_Data_Layer-3UVuR9IU/lib/python3.8/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", + " return _prepare_from_string(\" \".join(pjargs))\n", + "/media/veracrypt1/geospatial/WRI_India_ext/dssg/dataio/osm_to_voronoi_mapping.py:85: FutureWarning: CRS mismatch between CRS of the passed geometries and 'crs'. Use 'GeoDataFrame.set_crs(crs, allow_override=True)' to overwrite CRS or 'GeoDataFrame.to_crs(crs)' to reproject geometries. CRS mismatch will raise an error in the future versions of GeoPandas.\n", + "/media/veracrypt1/geospatial/WRI_India_ext/dssg/dataio/osm_to_voronoi_mapping.py:87: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n" + ] + } + ], "source": [ "#Write the district voronoi geodataframe to a file\n", - "ovm.write_district_voronoi_to_shapefile(araria_voronoi_joined, os.environ.get(\"DATA_DIR\") + \"/voronoi3_clip/araria-voronoi.shp\")" + "ovm.write_district_voronoi_to_shapefile(araria_voronoi_joined, os.environ.get(\"DATA_DIR\") + \"/voronoi/araria-voronoi.shp\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "image/svg+xml": "" + }, + "metadata": {}, + "execution_count": 16 + } + ], "source": [ "araria_voronoi_joined.unary_union" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " \n*** Profile printout saved to text file '../profile/create_knots_and_edges_from_boundary'. \n" + ] + } + ], "source": [ "%%prun -s cumulative -q -l 10 -T ../profile/create_knots_and_edges_from_boundary\n", "# Extract OSM dataframe based on voronoi boundary of the district to prepare to get osm data.\n", "from shapely.geometry import mapping, Polygon\n", "import osmnx as ox \n", "# Date based extraction of polygon and graph\n", - "cs = '[out:json][timeout:180][date:\"2021-03-01T00:00:00Z\"]'\n", + "cs = '[out:json][timeout:180][date:\"2021-05-01T00:00:00Z\"]'\n", "(araria_voronoi_poly, araria_voronoi_graph) = ode.create_knots_and_edges_from_boundary(araria_voronoi_joined, cs)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " 66459997 function calls (65508249 primitive calls) in 76.231 seconds\n\n Ordered by: cumulative time\n List reduced from 1115 to 10 due to restriction <10>\n\n ncalls tottime percall cumtime percall filename:lineno(function)\n 1 0.000 0.000 76.231 76.231 {built-in method builtins.exec}\n 1 0.000 0.000 76.230 76.230 :2()\n 1 0.050 0.050 76.230 76.230 osm_data_extraction.py:65(create_knots_and_edges_from_boundary)\n 1 0.621 0.621 76.157 76.157 graph.py:354(graph_from_polygon)\n 2 0.042 0.021 48.180 24.090 truncate.py:120(truncate_graph_polygon)\n 2 0.035 0.018 28.195 14.097 utils_geo.py:339(_intersect_index_quadrats)\n 758 0.033 0.000 21.396 0.028 geodataframe.py:103(__init__)\n 762 0.018 0.000 20.281 0.027 geodataframe.py:201(set_geometry)\n 748 0.006 0.000 14.532 0.019 generic.py:3591(_take_with_is_copy)\n 748 0.011 0.000 14.457 0.019 generic.py:3492(take)\n" + ] + } + ], "source": [ "print(open('../profile/create_knots_and_edges_from_boundary', 'r').read())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n \n \n \n \n 2021-05-05T09:10:01.313304\n image/svg+xml\n \n \n Matplotlib v3.4.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
" + }, + "metadata": {} + } + ], "source": [ "import osmnx as ox\n", "araria_voronoi_kefig, araria_voronoi_ax = ox.plot_graph(araria_voronoi_graph)\n", @@ -257,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -267,34 +508,51 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "araria_voronoi_osmdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/media/veracrypt1/.virtualenvs/WRI_WellBeing_Data_Layer-3UVuR9IU/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n and should_run_async(code)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(53758, 74)" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], "source": [ "araria_voronoi_osmdf.shape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + } + ], "source": [ "print(type(araria_voronoi_osmdf))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -317,13 +575,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/media/veracrypt1/.virtualenvs/WRI_WellBeing_Data_Layer-3UVuR9IU/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n and should_run_async(code)\n" + ] + } + ], "source": [ - "import pandas as pd \n", - "osm_dhs_df = pd.DataFrame(columns=['OSMID', 'DHSID', 'DHSCLUST', 'DISTRICTID'])\n", - "osm_dhs_df.head()" + "osm_dhs_gdf = araria_voronoi_osm_gdf.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/media/veracrypt1/.virtualenvs/WRI_WellBeing_Data_Layer-3UVuR9IU/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n and should_run_async(code)\n" + ] + } + ], + "source": [ + "# Add new columns to an existing geodataframe\n", + "import numpy as np\n", + "osm_dhs_gdf['DHSID'] = \"\"\n", + "osm_dhs_gdf['DHSCLUST'] = np.float64(0.0)\n", + "# Since district ID remains constant\n", + "osm_dhs_gdf['DISTRICTID'] = araria_voronoi_joined.iloc[0]['index_right']\n" ] }, { @@ -331,18 +617,56 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "# Check which voronoi cell a point is in point.\n", + "def point_check(row, district_voronoi_joined):\n", + " for ind, ro in district_voronoi_joined.iterrows():\n", + " if ro.geometry.contains(row.geometry):\n", + " row['DHSID'] = ro.DHSID\n", + " row['DHSCLUST'] = ro.DHSCLUST \n", + " break \n", + " return row \n", + "\n", + "# Check which voronoi cell a polygon is in \n", + "def polygon_check(row, district_voronoi_joined):\n", + " for ind, ro in district_voronoi_joined.iterrows():\n", + " if ro.geometry.contains(row.geometry.centroid):\n", + " row['DHSID'] = ro.DHSID\n", + " row['DHSCLUST'] = ro.DHSCLUST\n", + " break\n", + " return row\n", + "\n", + "# Check all the voronoi cells a linestring is in" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/media/veracrypt1/.virtualenvs/WRI_WellBeing_Data_Layer-3UVuR9IU/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", + " and should_run_async(code)\n", + "Total Pts = 45\n", + "Total Polylines = 4831\n", + "Total polygons = 48880\n", + "Total Multi Polygons = 2\n" + ] + } + ], "source": [ "pt_count = 0\n", "poly_count = 0\n", "ln_str_count = 0\n", "mp_count = 0\n", - "for index, row in araria_voronoi_osm_gdf.iterrows():\n", + "from tqdm import tqdm\n", + "for index, row in tqdm(osm_dhs_gdf.iterrows()):\n", " if row.geometry.geom_type == 'Point':\n", " pt_count += 1\n", - " for ind, ro in araria_voronoi_joined.iterrows():\n", - " if ro.geometry.contains(row.geometry):\n", - " osm_dhs_df = osm_dhs_df.append({'OSMID':row.osmid, 'DHSID':ro.DHSID, 'DHSCLUST': ro.DHSCLUST, 'DISTRICTID': ro.index_right}, ignore_index=True)\n", - " break\n", + " osm_dhs_gdf[index] = point_check(row, )\n", " elif row.geometry.geom_type == 'Polygon':\n", " poly_count += 1\n", " for ind, ro in araria_voronoi_joined.iterrows():\n", diff --git a/dssg/data-exploration/read_sav.ipynb b/dssg/data-exploration/read_sav.ipynb new file mode 100644 index 0000000..6393de5 --- /dev/null +++ b/dssg/data-exploration/read_sav.ipynb @@ -0,0 +1,183 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python385jvsc74a57bd0d4a53db61837b04487d02c25116133aa28f6a79c740d093360b2328df5f2ed08", + "display_name": "Python 3.9.0 64-bit ('WRI_WellBeing_Data_Layer': pipenv)" + }, + "metadata": { + "interpreter": { + "hash": "d4a53db61837b04487d02c25116133aa28f6a79c740d093360b2328df5f2ed08" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from dotenv import load_dotenv\n", + "load_dotenv()\n", + "import dssg.dataio.data_prep_voronoi as dpv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = os.environ.get(\"DATA_DIR\")\n", + "sav_file = data_dir + \"DHS-Raw-Data/IAHR74SV_household_recode/IAHR74FL.SAV\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df = dpv.read_and_reduce_sav(sav_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(28524, 3)" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " DHSCLUST HV270 HV271\n", + "0 10001.0 Richer 107648.636364\n", + "1 10002.0 Middle 25279.409091\n", + "2 10003.0 Middle 4725.681818\n", + "3 10004.0 Middle 17484.909091\n", + "4 10005.0 Middle 25083.454545" + ], + "text/html": "
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DHSCLUSTHV270HV271
010001.0Richer107648.636364
110002.0Middle25279.409091
210003.0Middle4725.681818
310004.0Middle17484.909091
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" + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Combine weighted voronoi shape file with df1\n", + "import geopandas as gpd\n", + "india_voronoi_shp = data_dir + \"voronoi/IAGE71FL_Voronoi_Clipped/IAGE71FL_Voronoi_Clipped.shp\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "india_voronoi_merged_gpd = dpv.merge_clipped_voronoi_and_wealth_index(india_voronoi_shp, df)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " DHSID DHSCLUST ADM1DHS DHSREGCO DHSREGNA URBAN_RURA \\\n", + "0 IA201400310502 310502.0 31.0 602.0 Thiruvallur R \n", + "1 IA201400310190 310190.0 31.0 602.0 Thiruvallur R \n", + "2 IA201400310070 310070.0 31.0 602.0 Thiruvallur R \n", + "3 IA201400310716 310716.0 31.0 602.0 Thiruvallur R \n", + "4 IA201400310592 310592.0 31.0 602.0 Thiruvallur R \n", + "\n", + " LATNUM LONGNUM ALT_DEM DATUM WEIGHT \\\n", + "0 13.320202 80.010414 39.0 WGS84 0.04504 \n", + "1 13.118380 79.803917 56.0 WGS84 0.04504 \n", + "2 13.147002 79.804755 44.0 WGS84 0.04504 \n", + "3 13.263585 80.188453 14.0 WGS84 0.04504 \n", + "4 13.261215 80.222348 18.0 WGS84 0.04504 \n", + "\n", + " geometry HV270 HV271 \n", + "0 POLYGON ((79.95699 13.28227, 79.95030 13.37055... Poorer 2846.727273 \n", + "1 POLYGON ((79.77943 12.97075, 79.71600 12.99485... Middle 32007.000000 \n", + "2 POLYGON ((79.83122 13.24096, 79.87546 13.14136... Richer -20213.045455 \n", + "3 POLYGON ((80.20192 13.21268, 80.10237 13.25050... Richer 13922.863636 \n", + "4 POLYGON ((80.27104 13.24175, 80.20483 13.20947... Middle 25076.500000 " + ], + "text/html": "
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DHSIDDHSCLUSTADM1DHSDHSREGCODHSREGNAURBAN_RURALATNUMLONGNUMALT_DEMDATUMWEIGHTgeometryHV270HV271
0IA201400310502310502.031.0602.0ThiruvallurR13.32020280.01041439.0WGS840.04504POLYGON ((79.95699 13.28227, 79.95030 13.37055...Poorer2846.727273
1IA201400310190310190.031.0602.0ThiruvallurR13.11838079.80391756.0WGS840.04504POLYGON ((79.77943 12.97075, 79.71600 12.99485...Middle32007.000000
2IA201400310070310070.031.0602.0ThiruvallurR13.14700279.80475544.0WGS840.04504POLYGON ((79.83122 13.24096, 79.87546 13.14136...Richer-20213.045455
3IA201400310716310716.031.0602.0ThiruvallurR13.26358580.18845314.0WGS840.04504POLYGON ((80.20192 13.21268, 80.10237 13.25050...Richer13922.863636
4IA201400310592310592.031.0602.0ThiruvallurR13.26121580.22234818.0WGS840.04504POLYGON ((80.27104 13.24175, 80.20483 13.20947...Middle25076.500000
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" + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "india_voronoi_merged_gpd.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#Write to shape file\n", + "india_voronoi_merged_gpd.to_file(data_dir + \"voronoi/IAGE71FL_Voronoi_Clipped/IAGE71FL_Voronoi_IAHR74FL_Wealth.shp\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/dssg/dataio/data_prep_voronoi.py b/dssg/dataio/data_prep_voronoi.py index 76ed0b0..fed0b77 100644 --- a/dssg/dataio/data_prep_voronoi.py +++ b/dssg/dataio/data_prep_voronoi.py @@ -6,6 +6,9 @@ from shapely.ops import unary_union import numpy as np from dotenv import load_dotenv +import pandas as pd +import pyreadstat +import statistics load_dotenv() @@ -52,7 +55,8 @@ def modify_dhs_shapefile(filename: str): # Create a geometry column reduced_dhs_gpd = gpd.GeoDataFrame( reduced_dhs_gpd, - geometry=gpd.points_from_xy(reduced_dhs_gpd.LONGNUM, reduced_dhs_gpd.LATNUM), + geometry=gpd.points_from_xy( + reduced_dhs_gpd.LONGNUM, reduced_dhs_gpd.LATNUM), ) # extract all the points as list from geoseries @@ -72,3 +76,41 @@ def modify_dhs_shapefile(filename: str): def coord_lister_of_point_series(geom): coords = list(geom.coords) return coords + + +def read_and_reduce_sav(sav_file: str) -> pd.DataFrame: + """Reads the DHS SAV file for household recode and extracts only the columns related to DHS Cluster ID and wealth indexes with + column names HV001, HV270, HV271. Then reduce by grouping by DHS Cluster ID and aggregating based on mode for wealth indexes. + + Args: + sav_file (str): filename with the path for DHS SPSS SAV file. + + Returns: + pd.DataFrame: returns the reduced dataframe + """ + df = pd.read_spss(sav_file, usecols=['HV001', 'HV270', 'HV271']) + df2 = df.groupby('HV001').agg( + {'HV270': statistics.mode, 'HV271': 'mean'}).reset_index() + + # Rename column HV001 to DHSCLUST + df2 = df2.rename(columns={'HV001': 'DHSCLUST'}) + + return df2 + + +def merge_clipped_voronoi_and_wealth_index(clipped_voronoi_shapefile: str, wealth_index_df: pd.DataFrame) -> gpd.geodataframe.GeoDataFrame: + """Merge the clipped voronoi geodataframe with wealth index dataframe into a single geodataframe + + Args: + clipped_voronoi_shapefile (str): shapefile containing the clipped weighted voronoi geometry for a specific country. + wealth_index_df (pd.DataFrame): wealth index for each DHS cluster + + Returns: + gpd.geodataframe.GeoDataFrame: dataframe obtained by merging the two dataframes. + """ + country_voronoi_gpd = gpd.read_file(clipped_voronoi_shapefile) + merged_df = country_voronoi_gpd.merge( + wealth_index_df, on='DHSCLUST', how='left') + country_voronoi_merged_gpd = gpd.GeoDataFrame( + merged_df, crs='EPSG:4326', geometry=merged_df.geometry) + return country_voronoi_merged_gpd