{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Enrich Store Locations\n", "\n", "Enriching store locations from a Web GIS Feature Layer is a very common workflow. Here, we detail how to enrich store locations both using current capabilities (v2.0.0) and soon to be released capabilties (v2.0.1)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from arcgis.gis import GIS\n", "from arcgis.features import GeoAccessor\n", "from arcgis.geoenrichment import get_countries, Country\n", "from arcgis.features import use_proximity\n", "from arcgis.features.use_proximity import create_drive_time_areas\n", "from arcgis.features.enrich_data import enrich_layer\n", "from IPython.display import display\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using Python-DotEnv\n", "\n", "To not inadvertantly share a credentials, I make a habit of using the `python-dotenv` package. The url, username and password are saved in a file called `.env` in the project directory, and this file is always excluded from version tracking in the `.gitignore` file. The contents of the `.env` file are really simple.\n", "\n", "```\n", " ESRI_GIS_URL = https://raddudes.maps.arcgis.com\n", " ESRI_GIS_USERNAME = oneRadDude\n", " ESRI_GIS_PASSWORD = P@$$w0rd\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "from dotenv import find_dotenv, load_dotenv\n", "\n", "# recursively crawls up directories until finding a .env file, and then loads it\n", "load_dotenv(find_dotenv())\n", "\n", "url = os.getenv('ESRI_GIS_URL')\n", "usrnm = os.getenv('ESRI_GIS_USERNAME')\n", "pswd = os.getenv('ESRI_GIS_PASSWORD')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameterizing\n", "\n", "To make the notebook a little more portable, this cell is where values unique to your specific data or preferences can be easily modified." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "stores_itm_id = '3adf57606db84d7e81240479347907e1'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create GIS Object Instance\n", "\n", "Now, to get to data and services, we need a connection to a Web GIS. This is accomplished by connecting to ArcGIS Online in this case through the GIS object." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "GIS @ https://bateam.maps.arcgis.com" ], "text/plain": [ "GIS @ https://bateam.maps.arcgis.com version:9.4" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gis =GIS(url, username=usrnm, password=pswd)\n", "\n", "gis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get Store Locations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create Item Instance\n", "\n", "We now get the reference to the Item in the Web GIS using the `stores_itm_id` from above." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Locations for demonstration and testing.Feature Layer Collection by jmccune_bateam\n", "
Last Modified: March 15, 2022\n", "
0 comments, 6 views\n", "
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\n", " " ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stores_itm = gis.content.get(stores_itm_id)\n", "\n", "stores_itm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get the Layer\n", "\n", "The item only saves the reference to the layer, and the layer is how we access data, so we need to get the first layer saved as part of the item (the only layer) to access the data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stores_lyr = stores_itm.layers[0]\n", "\n", "stores_lyr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get the Data\n", "\n", "Now, we can retrieve and examine the data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 6 entries, 0 to 5\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 OBJECTID 6 non-null int64 \n", " 1 LOCNUM 6 non-null object \n", " 2 SHAPE 6 non-null geometry\n", "dtypes: geometry(1), int64(1), object(1)\n", "memory usage: 272.0+ bytes\n" ] }, { "data": { "text/html": [ "
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OBJECTIDLOCNUMSHAPE
01164204208{\"x\": -97.37453699999998, \"y\": 27.712800000000...
12402166529{\"x\": -97.323093, \"y\": 27.71141400000015, \"spa...
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" ], "text/plain": [ " OBJECTID LOCNUM SHAPE\n", "0 1 164204208 {\"x\": -97.37453699999998, \"y\": 27.712800000000...\n", "1 2 402166529 {\"x\": -97.323093, \"y\": 27.71141400000015, \"spa...\n", "2 3 720384210 {\"x\": -97.36875000000002, \"y\": 27.709965000000...\n", "3 4 736055028 {\"x\": -97.37815552999999, \"y\": 27.682539720000...\n", "4 5 743788118 {\"x\": -97.63392599999996, \"y\": 27.855611999999..." ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stores_df = stores_lyr.query(\n", " out_fields=['LOCNUM'] # this only really applies to this demonstration if you're using your own data, so please feel free to remove\n", ").sdf\n", "\n", "stores_df.info()\n", "stores_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Enrich Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a Country\n", "\n", "Since working with in the United States, we need a Country object instance referencing the USA to access this data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usa = Country('usa')\n", "\n", "usa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filter to Variables of Interest\n", "\n", "With over 19 thousand variables available, we are going to reduce it to something at least under 100." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Available United States Enrichment Variables: 19,153\n" ] }, { "data": { "text/html": [ "
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namealiasdata_collectionenrich_nameenrich_field_namedescriptionvintageunits
0TSEGNUM2021 Dominant Tapestry NumbertapestryhouseholdsNEWtapestryhouseholdsNEW.TSEGNUMtapestryhouseholdsNEW_TSEGNUM2021 Dominant Tapestry Number (Esri)2021count
1TSEGCODE2021 Dominant Tapestry SegmenttapestryhouseholdsNEWtapestryhouseholdsNEW.TSEGCODEtapestryhouseholdsNEW_TSEGCODE2021 Dominant Tapestry Segment (Esri)2021text
2TSEGNAME2021 Dom Tapestry Segment NametapestryhouseholdsNEWtapestryhouseholdsNEW.TSEGNAMEtapestryhouseholdsNEW_TSEGNAME2021 Dominant Tapestry Segment Name (Esri)2021text
3THHBASE2021 Tapestry Household BasetapestryhouseholdsNEWtapestryhouseholdsNEW.THHBASEtapestryhouseholdsNEW_THHBASE2021 Households by Tapestry Segment Base2021count
4THH012021 HHs in Tapestry Seg 1AtapestryhouseholdsNEWtapestryhouseholdsNEW.THH01tapestryhouseholdsNEW_THH012021 Top Tier (1A) Tapestry Households (Esri)2021count
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87THHGRPU22021 HHs: Urbanization Group 2tapestryhouseholdsNEWtapestryhouseholdsNEW.THHGRPU2tapestryhouseholdsNEW_THHGRPU22021 Urban Periphery Tapestry Urbanization Gro...2021count
88THHGRPU32021 HHs: Urbanization Group 3tapestryhouseholdsNEWtapestryhouseholdsNEW.THHGRPU3tapestryhouseholdsNEW_THHGRPU32021 Metro Cities Tapestry Urbanization Group ...2021count
89THHGRPU42021 HHs: Urbanization Group 4tapestryhouseholdsNEWtapestryhouseholdsNEW.THHGRPU4tapestryhouseholdsNEW_THHGRPU42021 Suburban Periphery Tapestry Urbanization ...2021count
90THHGRPU52021 HHs: Urbanization Group 5tapestryhouseholdsNEWtapestryhouseholdsNEW.THHGRPU5tapestryhouseholdsNEW_THHGRPU52021 Semirural Tapestry Urbanization Group U5 ...2021count
91THHGRPU62021 HHs: Urbanization Group 6tapestryhouseholdsNEWtapestryhouseholdsNEW.THHGRPU6tapestryhouseholdsNEW_THHGRPU62021 Rural Tapestry Urbanization Group U6 Hous...2021count
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92 rows × 8 columns

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" ], "text/plain": [ " name alias data_collection \\\n", "0 TSEGNUM 2021 Dominant Tapestry Number tapestryhouseholdsNEW \n", "1 TSEGCODE 2021 Dominant Tapestry Segment tapestryhouseholdsNEW \n", "2 TSEGNAME 2021 Dom Tapestry Segment Name tapestryhouseholdsNEW \n", "3 THHBASE 2021 Tapestry Household Base tapestryhouseholdsNEW \n", "4 THH01 2021 HHs in Tapestry Seg 1A tapestryhouseholdsNEW \n", ".. ... ... ... \n", "87 THHGRPU2 2021 HHs: Urbanization Group 2 tapestryhouseholdsNEW \n", "88 THHGRPU3 2021 HHs: Urbanization Group 3 tapestryhouseholdsNEW \n", "89 THHGRPU4 2021 HHs: Urbanization Group 4 tapestryhouseholdsNEW \n", "90 THHGRPU5 2021 HHs: Urbanization Group 5 tapestryhouseholdsNEW \n", "91 THHGRPU6 2021 HHs: Urbanization Group 6 tapestryhouseholdsNEW \n", "\n", " enrich_name enrich_field_name \\\n", "0 tapestryhouseholdsNEW.TSEGNUM tapestryhouseholdsNEW_TSEGNUM \n", "1 tapestryhouseholdsNEW.TSEGCODE tapestryhouseholdsNEW_TSEGCODE \n", "2 tapestryhouseholdsNEW.TSEGNAME tapestryhouseholdsNEW_TSEGNAME \n", "3 tapestryhouseholdsNEW.THHBASE tapestryhouseholdsNEW_THHBASE \n", "4 tapestryhouseholdsNEW.THH01 tapestryhouseholdsNEW_THH01 \n", ".. ... ... \n", "87 tapestryhouseholdsNEW.THHGRPU2 tapestryhouseholdsNEW_THHGRPU2 \n", "88 tapestryhouseholdsNEW.THHGRPU3 tapestryhouseholdsNEW_THHGRPU3 \n", "89 tapestryhouseholdsNEW.THHGRPU4 tapestryhouseholdsNEW_THHGRPU4 \n", "90 tapestryhouseholdsNEW.THHGRPU5 tapestryhouseholdsNEW_THHGRPU5 \n", "91 tapestryhouseholdsNEW.THHGRPU6 tapestryhouseholdsNEW_THHGRPU6 \n", "\n", " description vintage units \n", "0 2021 Dominant Tapestry Number (Esri) 2021 count \n", "1 2021 Dominant Tapestry Segment (Esri) 2021 text \n", "2 2021 Dominant Tapestry Segment Name (Esri) 2021 text \n", "3 2021 Households by Tapestry Segment Base 2021 count \n", "4 2021 Top Tier (1A) Tapestry Households (Esri) 2021 count \n", ".. ... ... ... \n", "87 2021 Urban Periphery Tapestry Urbanization Gro... 2021 count \n", "88 2021 Metro Cities Tapestry Urbanization Group ... 2021 count \n", "89 2021 Suburban Periphery Tapestry Urbanization ... 2021 count \n", "90 2021 Semirural Tapestry Urbanization Group U5 ... 2021 count \n", "91 2021 Rural Tapestry Urbanization Group U6 Hous... 2021 count \n", "\n", "[92 rows x 8 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ev = usa.enrich_variables\n", "\n", "print(f'Total Available {usa.properties.country_name} Enrichment Variables: {len(ev.index):,}')\n", "\n", "enrich_vars = ev[ev.data_collection == 'tapestryhouseholdsNEW'].drop_duplicates('name').reset_index(drop=True)\n", "\n", "enrich_vars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pre-Python API v2.0.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create Geometries to Enrich\n", "\n", "Although there is a way to pass these parameters direclty in the JSON payload in the `geographies` parameter, the somewhat more obvious method is creation of the geometries using available methods." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LOCNUMSHAPE
0164204208{\"rings\": [[[-97.37453699978266, 27.7563684127...
1402166529{\"rings\": [[[-97.32309299984206, 27.7549824208...
2720384210{\"rings\": [[[-97.36875000001248, 27.7535334304...
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" ], "text/plain": [ " LOCNUM SHAPE\n", "0 164204208 {\"rings\": [[[-97.37453699978266, 27.7563684127...\n", "1 402166529 {\"rings\": [[[-97.32309299984206, 27.7549824208...\n", "2 720384210 {\"rings\": [[[-97.36875000001248, 27.7535334304...\n", "3 736055028 {\"rings\": [[[-97.37815552995369, 27.7261083231...\n", "4 743788118 {\"rings\": [[[-97.63392599968512, 27.8991795108..." ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_buf_fc = use_proximity.create_buffers(stores_df, distances=[3], units='miles')\n", "store_buf_df = store_buf_fc.query().sdf.loc[:,['LOCNUM', 'SHAPE']]\n", "store_buf_df.spatial.set_geometry('SHAPE')\n", "\n", "store_buf_df.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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locnumsource_countryaggregation_methodpopulation_to_polygon_size_ratingapportionment_confidencehas_datatsegnumtsegcodetsegnamethhbase...thhgrpl12thhgrpl13thhgrpl14thhgrpu1thhgrpu2thhgrpu3thhgrpu4thhgrpu5thhgrpu6SHAPE
0164204208USABlockApportionment:US.BlockGroups;PointsLayer:...2.1912.5761358CBright Young Professionals50364...9530671017744195341186112250{\"rings\": [[[-97.37453699978266, 27.7563684127...
1402166529USABlockApportionment:US.BlockGroups;PointsLayer:...2.1912.5761358CBright Young Professionals17285...00673066927926266600{\"rings\": [[[-97.32309299984206, 27.7549824208...
2720384210USABlockApportionment:US.BlockGroups;PointsLayer:...2.1912.5761358CBright Young Professionals47095...9180671015891184251154112390{\"rings\": [[[-97.36875000001248, 27.7535334304...
3736055028USABlockApportionment:US.BlockGroups;PointsLayer:...2.1912.5761358CBright Young Professionals45787...621077012072136131886312390{\"rings\": [[[-97.37815552995369, 27.7261083231...
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5 rows × 99 columns

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" ], "text/plain": [ " locnum source_country \\\n", "0 164204208 USA \n", "1 402166529 USA \n", "2 720384210 USA \n", "3 736055028 USA \n", "4 743788118 USA \n", "\n", " aggregation_method \\\n", "0 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "1 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "2 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "3 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "4 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "\n", " population_to_polygon_size_rating apportionment_confidence has_data \\\n", "0 2.191 2.576 1 \n", "1 2.191 2.576 1 \n", "2 2.191 2.576 1 \n", "3 2.191 2.576 1 \n", "4 2.191 2.576 1 \n", "\n", " tsegnum tsegcode tsegname thhbase ... thhgrpl12 \\\n", "0 35 8C Bright Young Professionals 50364 ... 953 \n", "1 35 8C Bright Young Professionals 17285 ... 0 \n", "2 35 8C Bright Young Professionals 47095 ... 918 \n", "3 35 8C Bright Young Professionals 45787 ... 621 \n", "4 14 4B Home Improvement 6098 ... 0 \n", "\n", " thhgrpl13 thhgrpl14 thhgrpu1 thhgrpu2 thhgrpu3 thhgrpu4 thhgrpu5 \\\n", "0 0 671 0 17744 19534 11861 1225 \n", "1 0 673 0 6692 7926 2666 0 \n", "2 0 671 0 15891 18425 11541 1239 \n", "3 0 77 0 12072 13613 18863 1239 \n", "4 0 0 0 1916 12 2977 336 \n", "\n", " thhgrpu6 SHAPE \n", "0 0 {\"rings\": [[[-97.37453699978266, 27.7563684127... \n", "1 0 {\"rings\": [[[-97.32309299984206, 27.7549824208... \n", "2 0 {\"rings\": [[[-97.36875000001248, 27.7535334304... \n", "3 0 {\"rings\": [[[-97.37815552995369, 27.7261083231... \n", "4 857 {\"rings\": [[[-97.63392599968512, 27.8991795108... \n", "\n", "[5 rows x 99 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_buf_enrich_df = usa.enrich(store_buf_df, enrich_variables=enrich_vars)\n", "\n", "store_buf_enrich_df.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LOCNUMSHAPE
0164204208{\"rings\": [[[-97.42162212699998, 27.7564393460...
1402166529{\"rings\": [[[-97.36121070099995, 27.7353290330...
2720384210{\"rings\": [[[-97.42229586099995, 27.7517232120...
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" ], "text/plain": [ " LOCNUM SHAPE\n", "0 164204208 {\"rings\": [[[-97.42162212699998, 27.7564393460...\n", "1 402166529 {\"rings\": [[[-97.36121070099995, 27.7353290330...\n", "2 720384210 {\"rings\": [[[-97.42229586099995, 27.7517232120...\n", "3 736055028 {\"rings\": [[[-97.38658799099994, 27.7124220980...\n", "4 743788118 {\"rings\": [[[-97.65450929899998, 27.9466567370..." ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_dt_fc = create_drive_time_areas(stores_df, break_values=[7], travel_mode='Driving Time')\n", "store_dt_df = store_dt_fc.query().sdf.loc[:,['LOCNUM', 'SHAPE']]\n", "store_dt_df.spatial.set_geometry('SHAPE')\n", "\n", "store_dt_df.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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locnumsource_countryaggregation_methodpopulation_to_polygon_size_ratingapportionment_confidencehas_datatsegnumtsegcodetsegnamethhbase...thhgrpl12thhgrpl13thhgrpl14thhgrpu1thhgrpu2thhgrpu3thhgrpu4thhgrpu5thhgrpu6SHAPE
0164204208USABlockApportionment:US.BlockGroups;PointsLayer:...2.1912.5761195DRustbelt Traditions20675...1095000103097356301000{\"rings\": [[[-97.42162212699998, 27.7564393460...
1402166529USABlockApportionment:US.BlockGroups;PointsLayer:...2.1912.57616614BCollege Towns1871...0067100137349800{\"rings\": [[[-97.36121070099995, 27.7353290330...
2720384210USABlockApportionment:US.BlockGroups;PointsLayer:...2.1912.5761195DRustbelt Traditions31131...1026067001373212963443600{\"rings\": [[[-97.42229586099995, 27.7517232120...
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5 rows × 99 columns

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" ], "text/plain": [ " locnum source_country \\\n", "0 164204208 USA \n", "1 402166529 USA \n", "2 720384210 USA \n", "3 736055028 USA \n", "4 743788118 USA \n", "\n", " aggregation_method \\\n", "0 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "1 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "2 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "3 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "4 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "\n", " population_to_polygon_size_rating apportionment_confidence has_data \\\n", "0 2.191 2.576 1 \n", "1 2.191 2.576 1 \n", "2 2.191 2.576 1 \n", "3 2.191 2.576 1 \n", "4 2.191 2.576 1 \n", "\n", " tsegnum tsegcode tsegname thhbase ... thhgrpl12 \\\n", "0 19 5D Rustbelt Traditions 20675 ... 1095 \n", "1 66 14B College Towns 1871 ... 0 \n", "2 19 5D Rustbelt Traditions 31131 ... 1026 \n", "3 35 8C Bright Young Professionals 28242 ... 0 \n", "4 13 4A Workday Drive 7296 ... 0 \n", "\n", " thhgrpl13 thhgrpl14 thhgrpu1 thhgrpu2 thhgrpu3 thhgrpu4 thhgrpu5 \\\n", "0 0 0 0 10309 7356 3010 0 \n", "1 0 671 0 0 1373 498 0 \n", "2 0 670 0 13732 12963 4436 0 \n", "3 0 0 0 8487 6327 12188 1239 \n", "4 0 0 0 2766 656 2457 500 \n", "\n", " thhgrpu6 SHAPE \n", "0 0 {\"rings\": [[[-97.42162212699998, 27.7564393460... \n", "1 0 {\"rings\": [[[-97.36121070099995, 27.7353290330... \n", "2 0 {\"rings\": [[[-97.42229586099995, 27.7517232120... \n", "3 0 {\"rings\": [[[-97.38658799099994, 27.7124220980... \n", "4 918 {\"rings\": [[[-97.65450929899998, 27.9466567370... \n", "\n", "[5 rows x 99 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_dt_enrich_df = usa.enrich(store_dt_df, enrich_variables=enrich_vars)\n", "\n", "store_dt_enrich_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Post Python API v2.0.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Travel Modes\n", "\n", "Travel modes can be specified as input into the `Country.enrich` method using " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namealiasdescriptiontypeimpedanceimpedance_categorytime_attribute_namedistance_attribute_nametravel_mode_idtravel_mode_dict
0driving_timeDriving TimeModels the movement of cars and other similar ...AUTOMOBILETravelTimetemporalTravelTimeKilometersFEgifRtFndKNcJMJ{\"attributeParameterValues\": [{\"attributeName\"...
1driving_distanceDriving DistanceModels the movement of cars and other similar ...AUTOMOBILEKilometersdistanceTravelTimeKilometersiKjmHuBSIqdEfOVr{\"attributeParameterValues\": [{\"attributeName\"...
2trucking_timeTrucking TimeModels basic truck travel by preferring design...TRUCKTruckTravelTimetemporalTruckTravelTimeKilometersZzzRtYcPLjXFBKwr{\"attributeParameterValues\": [{\"attributeName\"...
3trucking_distanceTrucking DistanceModels basic truck travel by preferring design...TRUCKKilometersdistanceTruckTravelTimeKilometersUBaNfFWeKcrRVYIo{\"attributeParameterValues\": [{\"attributeName\"...
4walking_timeWalking TimeFollows paths and roads that allow pedestrian ...WALKWalkTimetemporalWalkTimeKilometerscaFAgoThrvUpkFBW{\"attributeParameterValues\": [{\"attributeName\"...
5walking_distanceWalking DistanceFollows paths and roads that allow pedestrian ...WALKKilometersdistanceWalkTimeKilometersyFuMFwIYblqKEefX{\"attributeParameterValues\": [{\"attributeName\"...
6rural_driving_timeRural Driving TimeModels the movement of cars and other similar ...AUTOMOBILETravelTimetemporalTravelTimeKilometersNmNhNDUwZmE1YTlj{\"attributeParameterValues\": [{\"attributeName\"...
7rural_driving_distanceRural Driving DistanceModels the movement of cars and other similar ...AUTOMOBILEKilometersdistanceTravelTimeKilometersYzk3NjI1NTU5NjVj{\"attributeParameterValues\": [{\"attributeName\"...
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" ], "text/plain": [ " name alias \\\n", "0 driving_time Driving Time \n", "1 driving_distance Driving Distance \n", "2 trucking_time Trucking Time \n", "3 trucking_distance Trucking Distance \n", "4 walking_time Walking Time \n", "5 walking_distance Walking Distance \n", "6 rural_driving_time Rural Driving Time \n", "7 rural_driving_distance Rural Driving Distance \n", "\n", " description type \\\n", "0 Models the movement of cars and other similar ... AUTOMOBILE \n", "1 Models the movement of cars and other similar ... AUTOMOBILE \n", "2 Models basic truck travel by preferring design... TRUCK \n", "3 Models basic truck travel by preferring design... TRUCK \n", "4 Follows paths and roads that allow pedestrian ... WALK \n", "5 Follows paths and roads that allow pedestrian ... WALK \n", "6 Models the movement of cars and other similar ... AUTOMOBILE \n", "7 Models the movement of cars and other similar ... AUTOMOBILE \n", "\n", " impedance impedance_category time_attribute_name \\\n", "0 TravelTime temporal TravelTime \n", "1 Kilometers distance TravelTime \n", "2 TruckTravelTime temporal TruckTravelTime \n", "3 Kilometers distance TruckTravelTime \n", "4 WalkTime temporal WalkTime \n", "5 Kilometers distance WalkTime \n", "6 TravelTime temporal TravelTime \n", "7 Kilometers distance TravelTime \n", "\n", " distance_attribute_name travel_mode_id \\\n", "0 Kilometers FEgifRtFndKNcJMJ \n", "1 Kilometers iKjmHuBSIqdEfOVr \n", "2 Kilometers ZzzRtYcPLjXFBKwr \n", "3 Kilometers UBaNfFWeKcrRVYIo \n", "4 Kilometers caFAgoThrvUpkFBW \n", "5 Kilometers yFuMFwIYblqKEefX \n", "6 Kilometers NmNhNDUwZmE1YTlj \n", "7 Kilometers Yzk3NjI1NTU5NjVj \n", "\n", " travel_mode_dict \n", "0 {\"attributeParameterValues\": [{\"attributeName\"... \n", "1 {\"attributeParameterValues\": [{\"attributeName\"... \n", "2 {\"attributeParameterValues\": [{\"attributeName\"... \n", "3 {\"attributeParameterValues\": [{\"attributeName\"... \n", "4 {\"attributeParameterValues\": [{\"attributeName\"... \n", "5 {\"attributeParameterValues\": [{\"attributeName\"... \n", "6 {\"attributeParameterValues\": [{\"attributeName\"... \n", "7 {\"attributeParameterValues\": [{\"attributeName\"... " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usa.travel_modes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Use _Rural_ Driving Modes\n", "\n", "I really enjoy pointing out this distinction because I frequenlty use it. Other driving modes on ArcGIS Online discourage travel on unpaved roads. In many locales, this is decidedly _not_ desirable for accurate analysis results. Where we are doing this analysis example, in Corpus Christi, Texas, you do not have to venure far out of town to find dirt and gravel roads, especially if you head south toward King Ranch. Consequently, for this example, to accurately model the way people travel, I am using _rural_ driving modes." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Models the movement of cars and other similar small automobiles, such as pickup trucks, and finds solutions that optimize travel distance. Travel obeys one-way roads, avoids illegal turns, and follows other rules that are specific to cars, but does not discourage travel on unpaved roads.'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usa.travel_modes[usa.travel_modes.name == 'rural_driving_distance'].description.values[0]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 6 entries, 0 to 5\n", "Columns: 104 entries, objectid to SHAPE\n", "dtypes: float64(2), geometry(1), int64(93), object(8)\n", "memory usage: 5.0+ KB\n" ] }, { "data": { "text/html": [ "
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objectidlocnumsource_countryarea_typebuffer_unitsbuffer_units_aliasbuffer_radiiaggregation_methodpopulation_to_polygon_size_ratingapportionment_confidence...thhgrpl12thhgrpl13thhgrpl14thhgrpu1thhgrpu2thhgrpu3thhgrpu4thhgrpu5thhgrpu6SHAPE
01164204208USARingBufferMilesMiles3BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...9530671017744195341186112250{\"x\": -97.37453699999998, \"y\": 27.712800000000...
12402166529USARingBufferMilesMiles3BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...00673066927926266600{\"x\": -97.323093, \"y\": 27.71141400000015, \"spa...
23720384210USARingBufferMilesMiles3BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...9180671015891184251154112390{\"x\": -97.36875000000002, \"y\": 27.709965000000...
34736055028USARingBufferMilesMiles3BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...621077012072136131886312390{\"x\": -97.37815552999999, \"y\": 27.682539720000...
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5 rows × 104 columns

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" ], "text/plain": [ " objectid locnum source_country area_type buffer_units \\\n", "0 1 164204208 USA RingBuffer Miles \n", "1 2 402166529 USA RingBuffer Miles \n", "2 3 720384210 USA RingBuffer Miles \n", "3 4 736055028 USA RingBuffer Miles \n", "4 5 743788118 USA RingBuffer Miles \n", "\n", " buffer_units_alias buffer_radii \\\n", "0 Miles 3 \n", "1 Miles 3 \n", "2 Miles 3 \n", "3 Miles 3 \n", "4 Miles 3 \n", "\n", " aggregation_method \\\n", "0 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "1 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "2 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "3 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "4 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "\n", " population_to_polygon_size_rating apportionment_confidence ... \\\n", "0 2.191 2.576 ... \n", "1 2.191 2.576 ... \n", "2 2.191 2.576 ... \n", "3 2.191 2.576 ... \n", "4 2.191 2.576 ... \n", "\n", " thhgrpl12 thhgrpl13 thhgrpl14 thhgrpu1 thhgrpu2 thhgrpu3 thhgrpu4 \\\n", "0 953 0 671 0 17744 19534 11861 \n", "1 0 0 673 0 6692 7926 2666 \n", "2 918 0 671 0 15891 18425 11541 \n", "3 621 0 77 0 12072 13613 18863 \n", "4 0 0 0 0 1916 12 2977 \n", "\n", " thhgrpu5 thhgrpu6 SHAPE \n", "0 1225 0 {\"x\": -97.37453699999998, \"y\": 27.712800000000... \n", "1 0 0 {\"x\": -97.323093, \"y\": 27.71141400000015, \"spa... \n", "2 1239 0 {\"x\": -97.36875000000002, \"y\": 27.709965000000... \n", "3 1239 0 {\"x\": -97.37815552999999, \"y\": 27.682539720000... \n", "4 336 857 {\"x\": -97.63392599999996, \"y\": 27.855611999999... \n", "\n", "[5 rows x 104 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enrich_straight_line_df = usa.enrich(stores_df, enrich_variables=enrich_vars, proximity_type='straight_line', proximity_value=3, proximity_metric='miles')\n", "\n", "enrich_straight_line_df.info()\n", "enrich_straight_line_df.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 6 entries, 0 to 5\n", "Columns: 104 entries, objectid to SHAPE\n", "dtypes: float64(2), geometry(1), int64(93), object(8)\n", "memory usage: 5.0+ KB\n" ] }, { "data": { "text/html": [ "
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objectidlocnumsource_countryarea_typebuffer_unitsbuffer_units_aliasbuffer_radiiaggregation_methodpopulation_to_polygon_size_ratingapportionment_confidence...thhgrpl12thhgrpl13thhgrpl14thhgrpu1thhgrpu2thhgrpu3thhgrpu4thhgrpu5thhgrpu6SHAPE
01164204208USANetworkServiceAreaMilesDrive Distance Miles5BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...15840671025091255562034717100{\"x\": -97.37453699999998, \"y\": 27.712800000000...
12402166529USANetworkServiceAreaMilesDrive Distance Miles5BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...49909360616599263454240{\"x\": -97.323093, \"y\": 27.71141400000015, \"spa...
23720384210USANetworkServiceAreaMilesDrive Distance Miles5BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...15190671023133251912089016450{\"x\": -97.36875000000002, \"y\": 27.709965000000...
34736055028USANetworkServiceAreaMilesDrive Distance Miles5BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...9180670013477184552144313660{\"x\": -97.37815552999999, \"y\": 27.682539720000...
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5 rows × 104 columns

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" ], "text/plain": [ " objectid locnum source_country area_type buffer_units \\\n", "0 1 164204208 USA NetworkServiceArea Miles \n", "1 2 402166529 USA NetworkServiceArea Miles \n", "2 3 720384210 USA NetworkServiceArea Miles \n", "3 4 736055028 USA NetworkServiceArea Miles \n", "4 5 743788118 USA NetworkServiceArea Miles \n", "\n", " buffer_units_alias buffer_radii \\\n", "0 Drive Distance Miles 5 \n", "1 Drive Distance Miles 5 \n", "2 Drive Distance Miles 5 \n", "3 Drive Distance Miles 5 \n", "4 Drive Distance Miles 5 \n", "\n", " aggregation_method \\\n", "0 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "1 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "2 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "3 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "4 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "\n", " population_to_polygon_size_rating apportionment_confidence ... \\\n", "0 2.191 2.576 ... \n", "1 2.191 2.576 ... \n", "2 2.191 2.576 ... \n", "3 2.191 2.576 ... \n", "4 2.191 2.576 ... \n", "\n", " thhgrpl12 thhgrpl13 thhgrpl14 thhgrpu1 thhgrpu2 thhgrpu3 thhgrpu4 \\\n", "0 1584 0 671 0 25091 25556 20347 \n", "1 499 0 936 0 6165 9926 3454 \n", "2 1519 0 671 0 23133 25191 20890 \n", "3 918 0 670 0 13477 18455 21443 \n", "4 0 0 0 0 2972 750 3257 \n", "\n", " thhgrpu5 thhgrpu6 SHAPE \n", "0 1710 0 {\"x\": -97.37453699999998, \"y\": 27.712800000000... \n", "1 24 0 {\"x\": -97.323093, \"y\": 27.71141400000015, \"spa... \n", "2 1645 0 {\"x\": -97.36875000000002, \"y\": 27.709965000000... \n", "3 1366 0 {\"x\": -97.37815552999999, \"y\": 27.682539720000... \n", "4 1136 1977 {\"x\": -97.63392599999996, \"y\": 27.855611999999... \n", "\n", "[5 rows x 104 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enrich_drive_dist_df = usa.enrich(stores_df, enrich_variables=enrich_vars, proximity_type='rural_driving_distance', proximity_value=5, proximity_metric='miles')\n", "\n", "enrich_drive_dist_df.info()\n", "enrich_drive_dist_df.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 6 entries, 0 to 5\n", "Columns: 104 entries, objectid to SHAPE\n", "dtypes: float64(2), geometry(1), int64(93), object(8)\n", "memory usage: 5.0+ KB\n" ] }, { "data": { "text/html": [ "
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01164204208USANetworkServiceAreaMinutesDrive Time Minutes7BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...1095000103097340303500{\"x\": -97.37453699999998, \"y\": 27.712800000000...
12402166529USANetworkServiceAreaMinutesDrive Time Minutes7BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...0067100137349800{\"x\": -97.323093, \"y\": 27.71141400000015, \"spa...
23720384210USANetworkServiceAreaMinutesDrive Time Minutes7BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...1026067001373212963443600{\"x\": -97.36875000000002, \"y\": 27.709965000000...
34736055028USANetworkServiceAreaMinutesDrive Time Minutes7BlockApportionment:US.BlockGroups;PointsLayer:...2.1912.576...0000848763271220712390{\"x\": -97.37815552999999, \"y\": 27.682539720000...
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5 rows × 104 columns

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" ], "text/plain": [ " objectid locnum source_country area_type buffer_units \\\n", "0 1 164204208 USA NetworkServiceArea Minutes \n", "1 2 402166529 USA NetworkServiceArea Minutes \n", "2 3 720384210 USA NetworkServiceArea Minutes \n", "3 4 736055028 USA NetworkServiceArea Minutes \n", "4 5 743788118 USA NetworkServiceArea Minutes \n", "\n", " buffer_units_alias buffer_radii \\\n", "0 Drive Time Minutes 7 \n", "1 Drive Time Minutes 7 \n", "2 Drive Time Minutes 7 \n", "3 Drive Time Minutes 7 \n", "4 Drive Time Minutes 7 \n", "\n", " aggregation_method \\\n", "0 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "1 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "2 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "3 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "4 BlockApportionment:US.BlockGroups;PointsLayer:... \n", "\n", " population_to_polygon_size_rating apportionment_confidence ... \\\n", "0 2.191 2.576 ... \n", "1 2.191 2.576 ... \n", "2 2.191 2.576 ... \n", "3 2.191 2.576 ... \n", "4 2.191 2.576 ... \n", "\n", " thhgrpl12 thhgrpl13 thhgrpl14 thhgrpu1 thhgrpu2 thhgrpu3 thhgrpu4 \\\n", "0 1095 0 0 0 10309 7340 3035 \n", "1 0 0 671 0 0 1373 498 \n", "2 1026 0 670 0 13732 12963 4436 \n", "3 0 0 0 0 8487 6327 12207 \n", "4 0 0 0 0 2748 656 2457 \n", "\n", " thhgrpu5 thhgrpu6 SHAPE \n", "0 0 0 {\"x\": -97.37453699999998, \"y\": 27.712800000000... \n", "1 0 0 {\"x\": -97.323093, \"y\": 27.71141400000015, \"spa... \n", "2 0 0 {\"x\": -97.36875000000002, \"y\": 27.709965000000... \n", "3 1239 0 {\"x\": -97.37815552999999, \"y\": 27.682539720000... \n", "4 500 910 {\"x\": -97.63392599999996, \"y\": 27.855611999999... \n", "\n", "[5 rows x 104 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enrich_drive_time_df = usa.enrich(stores_df, enrich_variables=enrich_vars, proximity_type='rural_driving_time', proximity_value=7, proximity_metric='minutes')\n", "\n", "enrich_drive_time_df.info()\n", "enrich_drive_time_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 4 }