{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Routing: Find the nearest hospital\n", "\n", ">* πŸ‘Ÿ Ready To Run!\n", "* πŸ–₯️ Requires Network Analysis Organization Configuration\n", "* πŸ–₯️ Requires Geocoding Organization Configuration\n", "* πŸ”¬ Data Science\n", "* πŸš€ Data Exploration and Analysis\n", "* πŸ•ΈοΈ Network Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `network` module of the ArcGIS API for Python can be used to solve different types of network analysis operations. In this sample, we see how to find the hospital that is closest to an incident. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Closest facility" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The closest facility solver provides functionality for finding out the closest locations to a particular input point. This solver would be useful in cases when you have an incident and need to find the closest facility or need to get information on the travel time and the distance to each of the facilities from an incident point for reporting purposes.\n", "\n", "![](http://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/GUID-96C273DB-6A24-4D42-AADA-975A33B44F3D-web.png)\n", "\n", "When finding closest facilities, you can specify how many to find and whether the direction of travel is toward or away from them. The closest facility solver displays the best routes between incidents and facilities, reports their travel costs, and returns driving directions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "\n", "* [Connect to your GIS](#Connect-to-your-GIS)\n", "* [Create a Network Layer](#Create-a-Network-Layer)\n", "* [Create a Facilities Layer](#Create-a-Facilities-layer)\n", "* [Create Incidents Layer](#Create-Incidents-Layer)\n", "* [Solve for Closest Facility](#Solve-for-Closest-Facility)\n", "* [Analyze Results in a Table](#Analyze-Results-in-a-Table)\n", "* [Conclusion](#Conclusion)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Connect to your GIS\n", "As a first step, import the necessary libraries and establish a connection to your Organization." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import traceback\n", "\n", "import pandas as pd\n", "from IPython.display import HTML\n", "\n", "from arcgis.gis import GIS\n", "from arcgis.network import ClosestFacilityLayer\n", "from arcgis import geocoding\n", "from arcgis.features import Feature, FeatureSet\n", "\n", "# Connect to your GIS\n", "gis = GIS(\"home\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a Network Layer\n", "To perform any network analysis (such as finding the closest facility, the best route between multiple stops, or service area around a facility), you would need to create a `NetworkLayer` object. In this sample, since we are solving for closest facilities, we need to create a `ClosestFacilityLayer` which is a type of `NetworkLayer`.\n", "\n", "To create any `NetworkLayer` object, you would need to provide the URL to the appropriate network analysis service. Hence, in this sample, we provide a `ClosestFacility` URL to create a `ClosestFacilityLayer` object. \n", "\n", "Since all ArcGIS Online organizations already have access to those routing services, you can access this URL through the `GIS` object's `helperServices` property. If you have your own ArcGIS Server-based map service with network analysis capability enabled, provide the URL for this service.\n", "\n", "Let's start by checking to make sure we have our Organization configured for Network Analysis, and getting the ClosestFacility URL from our `GIS`:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Closest Facility URL successfully retrieved\n" ] } ], "source": [ "try:\n", " analysis_url = gis.properties.helperServices.closestFacility.url\n", " print(\"Closest Facility URL successfully retrieved\")\n", "except Exception as e:\n", " traceback.print_exc()\n", " raise Exception(f\"Network Analysis is not properly configured \"\\\n", " f\"on your Organization: {e}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a `ClosestFacilityLayer` object from the `arcgis.network` module using the retrieved URL:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cf_layer = ClosestFacilityLayer(analysis_url, gis = gis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a Facilities layer\n", "In this sample, we will be looking for the closest hospital (facility) to an incident location. Even though we are interested in finding out the closest one, it would still be helpful to get the information on the distance and travel time to all of them for reference purposes.\n", "\n", "In the code below, we need to geocode the hospitals' addresses as well as do the reverse geocode for the incident location which has been supplied in the latitude/longitude format.\n", "\n", "To perform the geocode operations, we will use the `geocoding` module of the ArcGIS API." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this sample, we geocode addresses of hospitals to create the facility layer. In your workflows, this could be any layer of facilities to route from an incident. In this scenario, we'll create a list of hospitals in the Coachella Valley region of southern California." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "hospitals_addresses = [\n", " 'San Gorgonio Memorial Hospital,600 N. Highland Springs Ave.,'\\\n", " 'Banning,CA,92220',\n", " 'Desert Regional Medical Center,1150 N. Indian Canyon Dr.,'\\\n", " 'Palm Springs,CA,92262',\n", " 'JFK Memorial Hospital,47111 Monroe St.,Indio,CA,92201',\n", " 'Hi-Desert Medical Center,6601 White Feather Rd.,'\\\n", " 'Joshua Tree,CA,92252',\n", " 'Eisenhower Health,39000 Bob Hope Dr.,Rancho Mirage,CA,92270']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loop through each address and geocode it. The geocode operation returns a list of matches for each address. We pick the first result and extract the coordinates from it and construct a `Feature` object out of it. Then we combine all the `Feature`s representing the hospitals into a `FeatureSet` object." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "hosp_feat_list = []\n", "\n", "for address in hospitals_addresses:\n", " hit = geocoding.geocode(address)[0]\n", " hosp_feat = Feature(geometry=hit['location'],\n", " attributes=hit['attributes'])\n", " hosp_feat_list.append(hosp_feat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct a `FeatureSet` using each hospital `Feature`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "hospitals_fset = FeatureSet(features=hosp_feat_list, \n", " geometry_type='esriGeometryPoint', \n", " spatial_reference={'latestWkid': 4326})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's draw our hospitals on a map" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "map1 = gis.map('Palm Springs, CA')\n", "map1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "map1.zoom = 8" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "map1.draw(hospitals_fset,\n", " symbol={\"type\": \"esriSMS\",\"style\": \"esriSMSSquare\",\n", " \"color\": [76,115,0,255],\"size\": 8,})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create Incidents Layer\n", "Similarly, let us create the incident layer" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "incident_coords = '-116.391287,33.808053'\n", "reverse_geocode = geocoding.reverse_geocode(\n", " {\"x\": incident_coords.split(',')[0], \n", " \"y\": incident_coords.split(',')[1]})\n", "\n", "incident_feature = Feature(geometry=reverse_geocode['location'], \n", " attributes=reverse_geocode['address'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "incident_fset = FeatureSet([incident_feature],\n", " geometry_type='esriGeometryPoint',\n", " spatial_reference={'latestWkid': 4326})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add the incident to the map" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "map1.draw(incident_fset,\n", " symbol={\"type\": \"esriSMS\",\"style\": \"esriSMSDiamond\",\n", " \"size\": 12, \"color\":[255,0,0,255]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solve for Closest Facility\n", "By default the closest facility service would return only the closest location, so we need to specify explicitly the `default_target_facility_count` parameter as well as `return_facilities` to get multiple results." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "result = cf_layer.solve_closest_facility(\n", " incidents=incident_fset,\n", " facilities=hospitals_fset,\n", " default_target_facility_count=5,\n", " return_facilities=True,\n", " impedance_attribute_name='TravelTime',\n", " accumulate_attribute_names=['Kilometers','TravelTime'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the result dictionary" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['messages', 'routes', 'facilities'])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use the `routes` dictionary to construct line features out of the routes to display on the map. From the `routes` dictionary will find out the proper key to get each route: the `features` key." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['fieldAliases', 'geometryType', 'spatialReference', 'features'])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result['routes'].keys()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['attributes', 'geometry'])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result['routes']['features'][0].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct line features out of the routes that are returned." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "line_feat_list = []\n", "for line_dict in result['routes']['features']:\n", " f1 = Feature(line_dict['geometry'], line_dict['attributes'])\n", " line_feat_list.append(f1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "routes_fset = FeatureSet(line_feat_list, \n", " geometry_type=result['routes']['geometryType'],\n", " spatial_reference= result['routes']['spatialReference'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add the routes back to the map. The route to the closest hospital is in red" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "map1.draw(routes_fset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyze Results in a Table\n", "Since we parsed the routes as a `FeatureSet`, we can display the attributes easily as a `pandas` `DataFrame`. We'll sort the dataframe according to the `Name` attribute of the `solve_closest_facility` task results." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FacilityCurbApproachFacilityIDFacilityRankIncidentCurbApproachIncidentIDNameObjectIDSHAPEShape_LengthTotal_KilometersTotal_MilesTotal_TravelTime
311421I-10 W - Location 14{\"paths\": [[[-116.39135814899998, 33.807921482...0.59768256.73184435.25153434.462511
112221I-10 W - Location 22{\"paths\": [[[-116.39135814899998, 33.807921482...0.18000817.33120310.76911015.617433
213321I-10 W - Location 33{\"paths\": [[[-116.39135814899998, 33.807921482...0.25309824.96712615.51385318.835255
414521I-10 W - Location 45{\"paths\": [[[-116.39135814899998, 33.807921482...0.71942470.71583843.94078549.275969
015121I-10 W - Location 51{\"paths\": [[[-116.39135814899998, 33.807921482...0.0905729.7285576.0450459.560507
\n", "
" ], "text/plain": [ " FacilityCurbApproach FacilityID FacilityRank IncidentCurbApproach \\\n", "3 1 1 4 2 \n", "1 1 2 2 2 \n", "2 1 3 3 2 \n", "4 1 4 5 2 \n", "0 1 5 1 2 \n", "\n", " IncidentID Name ObjectID \\\n", "3 1 I-10 W - Location 1 4 \n", "1 1 I-10 W - Location 2 2 \n", "2 1 I-10 W - Location 3 3 \n", "4 1 I-10 W - Location 4 5 \n", "0 1 I-10 W - Location 5 1 \n", "\n", " SHAPE Shape_Length \\\n", "3 {\"paths\": [[[-116.39135814899998, 33.807921482... 0.597682 \n", "1 {\"paths\": [[[-116.39135814899998, 33.807921482... 0.180008 \n", "2 {\"paths\": [[[-116.39135814899998, 33.807921482... 0.253098 \n", "4 {\"paths\": [[[-116.39135814899998, 33.807921482... 0.719424 \n", "0 {\"paths\": [[[-116.39135814899998, 33.807921482... 0.090572 \n", "\n", " Total_Kilometers Total_Miles Total_TravelTime \n", "3 56.731844 35.251534 34.462511 \n", "1 17.331203 10.769110 15.617433 \n", "2 24.967126 15.513853 18.835255 \n", "4 70.715838 43.940785 49.275969 \n", "0 9.728557 6.045045 9.560507 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1 = routes_fset.sdf.sort_values(by=['Name'])\n", "df1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add the hospital addresses and incident address to this table and display only the relevant columns" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "df1['facility_address'] = hospitals_addresses\n", "df1['incident_address'] = [incident_feature.attributes['Match_addr'] \\\n", " for i in range(len(hospitals_addresses))]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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facility_addressincident_addressTotal_MilesTotal_TravelTime
0Eisenhower Health,39000 Bob Hope Dr.,Rancho Mirage,CA,92270I-10 W, Thousand Palms, California, 922766.0450459.560507
1Desert Regional Medical Center,1150 N. Indian Canyon Dr.,Palm Springs,CA,92262I-10 W, Thousand Palms, California, 9227610.76911015.617433
2JFK Memorial Hospital,47111 Monroe St.,Indio,CA,92201I-10 W, Thousand Palms, California, 9227615.51385318.835255
3San Gorgonio Memorial Hospital,600 N. Highland Springs Ave.,Banning,CA,92220I-10 W, Thousand Palms, California, 9227635.25153434.462511
4Hi-Desert Medical Center,6601 White Feather Rd.,Joshua Tree,CA,92252I-10 W, Thousand Palms, California, 9227643.94078549.275969
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" ], "text/plain": [ " facility_address \\\n", "0 Eisenhower Health,39000 Bob Hope Dr.,Rancho Mirage,CA,92270 \n", "1 Desert Regional Medical Center,1150 N. Indian Canyon Dr.,Palm Springs,CA,92262 \n", "2 JFK Memorial Hospital,47111 Monroe St.,Indio,CA,92201 \n", "3 San Gorgonio Memorial Hospital,600 N. Highland Springs Ave.,Banning,CA,92220 \n", "4 Hi-Desert Medical Center,6601 White Feather Rd.,Joshua Tree,CA,92252 \n", "\n", " incident_address Total_Miles Total_TravelTime \n", "0 I-10 W, Thousand Palms, California, 92276 6.045045 9.560507 \n", "1 I-10 W, Thousand Palms, California, 92276 10.769110 15.617433 \n", "2 I-10 W, Thousand Palms, California, 92276 15.513853 18.835255 \n", "3 I-10 W, Thousand Palms, California, 92276 35.251534 34.462511 \n", "4 I-10 W, Thousand Palms, California, 92276 43.940785 49.275969 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('max_colwidth', 80)\n", "\n", "df1[['facility_address','incident_address','Total_Miles',\n", " 'Total_TravelTime']].sort_values(by=['Total_TravelTime'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion\n", "Using the `network` module of the ArcGIS API for Python, you can solve for closest facilities from an incident location." ] } ], "metadata": { "anaconda-cloud": {}, "esriNotebookRuntime": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }