Perform a feature analysis

Learn how to perform a hot spot feature analysis with the spatial analysis service.

Run a find hot spot analysis on traffic and parking violations in San Francisco.ss

A feature analysis is the process of using the spatial analysis service to perform server-side geometric and analytic operations on feature data. All feature analysis requests are job requests. The easiest way to programmatically run an analysis request to the spatial analysis service is to use ArcGIS REST JS, which provides a Job class that handles long- running operations.

In this tutorial, you use ArcGIS REST JS to perform a hot spot analysis to find statistically significant clusters of parking violations.

Prerequisites

You need the following to access the spatial analysis service and perform feature analysis operations:

Steps

Get the starter code

  1. Download the tutorial starter code zip file from the portal.
  2. Unzip the folder and open it in a text editor of your choice, such as Visual Studio Code. The starter app includes the following:
    • callback.html: This contains callback as part of the authentication process.
    • index.html: This contains app logic and the OAuth 2.0 code necessary to perform the authentication.

Set up authentication

Create a new OAuth credential to register the application.

  1. Go to the Create OAuth credentials for user authentication tutorial to get an OAuth credential.
  2. Copy the Client ID and Redirect URL from your OAuth credentials item and paste them to a safe location. They will be used in a later step.

All users that access this application need an ArcGIS account with privileges to access the basemap styles service and spatial analysis service.

Set developer credentials

  1. In both the index.html and callback.html files, replace YOUR_CLIENT_ID and YOUR_REDIRECT_URL with the client ID and redirect URL of your OAuth credentials.

    index.html
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          // Your client ID from OAuth credentials
          const clientId = "YOUR_CLIENT_ID";
          // The redirect URL registered in your OAuth credentials
          const redirectUri = "YOUR_REDIRECT_URL";
    
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          // Your client ID from OAuth credentials
          const clientId = "YOUR_CLIENT_ID";
          // The redirect URL registered in your OAuth credentials
          const redirectUri = "YOUR_REDIRECT_URL";
    
  2. Run the app and ensure you can sign in successfully.

Add script references

In addition to ArcGIS REST JS Request, you also reference the Portal helper class from ArcGIS REST JS to obtain the spatial analysis service URL.

  1. Add references to the ArcGIS REST JS Portal helper class.

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      <!-- ArcGIS REST JS used for user authentication. -->
      <script src="https://unpkg.com/@esri/arcgis-rest-request@4/dist/bundled/request.umd.js"></script>
      <!-- MapLibre GL JS-->
      <script src=https://unpkg.com/maplibre-gl@4/dist/maplibre-gl.js></script>
      <link href=https://unpkg.com/maplibre-gl@4/dist/maplibre-gl.css rel="stylesheet" />
    
      <!-- ArcGIS REST JS used for spatial analysis URL -->
      <script src="https://unpkg.com/@esri/arcgis-rest-portal@4/dist/bundled/portal.umd.js"></script>
    

Update the map

  1. Update the map's viewpoint to [-122.445, 37.760] and a zoom level of 11 to focus in San Fransisco, CA. Then, update the basemap to arcgis/human-geography.

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        const basemapEnum = "arcgis/human-geography";
    
        const map = new maplibregl.Map({
          container: "map", // the id of the div element
          style: `https://basemapstyles-api.arcgis.com/arcgis/rest/services/styles/v2/styles/${basemapEnum}?token=${accessToken}`,
    
          zoom: 11, // starting zoom
          center: [-122.445, 37.760]
    
        });
    
    Expand

Display parking violations

To perform feature analysis, you need to provide feature data as input. In this tutorial, you use the SF parking violations hosted feature layer as input data for the hot spot analysis.

  1. Add the parking violation feature layer and the corresponding vector tile layer to the points and pointsVTL variables.

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        const points =
          'https://services3.arcgis.com/GVgbJbqm8hXASVYi/arcgis/rest/services/sf_traffic_parking_violations_sa_osapi/FeatureServer/0';
    
        const pointsVTL =
          'https://vectortileservices3.arcgis.com/GVgbJbqm8hXASVYi/arcgis/rest/services/sf_traffic_parking_violations_sa_osapi/VectorTileServer';
    
    Expand
  2. Add the SF parking violations vector tile layer and its style definition to the map.

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        map.once('load', () => {
    
          map.addSource('violations', {
            type: 'vector',
            tiles: [`${pointsVTL}/tile/{z}/{y}/{x}.pbf`],
    
          });
    
          map.addLayer({
            id: 'vtlLayer',
            type: 'circle',
            source: 'violations',
            'source-layer': 'DeriveNewLocationsOutput',
            paint: {
              'circle-color': 'rgb(0,0,0)',
              'circle-radius': 1.5,
            },
          });
    
        });
    
    Expand
  3. Add the data attribution for the vector tile layer source.

    • Go to the sf_traffic_parking_violations_sa_osapi_vtl item.
    • Scroll down to the Credits (Attribution) section and copy its value.
    • Create an attribution property and paste the attribution value from the item.
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          map.once('load', () => {
      
            map.addSource('violations', {
              type: 'vector',
              tiles: [`${pointsVTL}/tile/{z}/{y}/{x}.pbf`],
      
              // Attribution text retrieved from https://arcgis.com/home/item.html?id=a9b4d69b02ee4365a0fcb9610c69db9b
              attribution: "San Francisco 311, City and County of San Francisco"
      
            });
      
            map.addLayer({
              id: 'vtlLayer',
              type: 'circle',
              source: 'violations',
              'source-layer': 'DeriveNewLocationsOutput',
              paint: {
                'circle-color': 'rgb(0,0,0)',
                'circle-radius': 1.5,
              },
            });
      
          });
      
      Expand
  4. Run the application and navigate to your localhost, for example https://localhost:8080. After you sign in, you will see the vector tile layer rendered on the map.

Get the analysis URL

To make a request to the spatial analysis service, you need to get the URL first. The analysis service URL is unique to your organization.

  1. Call the getSelf operation from ArcGIS REST JS to obtain the analysis URL.

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        const getAnalysisUrl = async (session) => {
          const portalSelf = await arcgisRest.getSelf({
            authentication: session,
          });
    
          return portalSelf.helperServices.analysis.url;
        };
    
    Expand

Make the request

Use the Job class and set the operationURL and params required for the selected analysis.

  1. Create a function that submits a Job request to the spatial analysis service using your organization's analysis URL. Set the params required for a hot spot analysis and authenticate using session.

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        const runAnalysis = async () => {
          const analysisUrl = await getAnalysisUrl(session);
          const operationUrl = `${analysisUrl}/FindHotSpots/submitJob`;
          const params = {
            analysisLayer: { url: points },
            shapeType: 'Hexagon',
            outputName: {
              serviceProperties: {
                name: `MapLibreGLJS_find_hot_spots_${new Date().getTime()}`,
              },
            }, //Outputs results as a hosted feature serivce.
          };
    
          const jobReq = await arcgisRest.Job.submitJob({
            url: operationUrl,
            params: params,
            authentication: session,
          });
    
          // get all the results, this will start monitoring and trigger events
          const jobResp = await jobReq.getAllResults();
    
          // jobResp.aggregatedLayer.value.url
          return jobResp;
        };
    
    
    Expand
  2. Create an HTML div element called status and a loader to display the job status.

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    <body>
    
      <div id="status">
        <span class="loader"></span>
        <div id="info"></div>
      </div>
    
      <div id="map"></div>
    
  3. Apply CSS styling to the status and the loader.

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        #status {
          width: 400px;
          background-color: #303336;
          color: #edffff;
          z-index: 0;
          position: absolute;
          font-family: Arial, Helvetica, sans-serif;
          top: 50%;
          left: 50%;
          transform: translate(-50%, -50%);
          display: flex;
          flex-direction: row;
          justify-content: center;
          padding: 5px;
          border-radius: 5px;
          gap: 10px;
          align-content: center;
          align-items: center;
          margin: auto;
        }
    
        .loader {
          width: 24px;
          height: 24px;
          border: 5px solid #edffff;
          border-bottom-color: #303336;
          border-radius: 50%;
          display: inline-block;
          box-sizing: border-box;
          animation: rotation 2s linear infinite;
        }
    
        @keyframes rotation {
          0% {
            transform: rotate(0deg);
          }
    
          100% {
            transform: rotate(360deg);
          }
        }
    
    
    Expand
  4. Create a function called showInfo to control the visibility and content of the status element.

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        const showInfo = (visible, msg) => {
          const statusUi = document.getElementById("status");
          !visible ? (statusUi.style.zIndex = 0) : (statusUi.style.zIndex = 1001);
          const infoUi = document.getElementById("info");
          infoUi.innerText = msg;
        };
    
        const runAnalysis = async () => {
          const analysisUrl = await getAnalysisUrl(session);
          const operationUrl = `${analysisUrl}/FindHotSpots/submitJob`;
          const params = {
            analysisLayer: { url: points },
            shapeType: 'Hexagon',
            outputName: {
              serviceProperties: {
                name: `MapLibreGLJS_find_hot_spots_${new Date().getTime()}`,
              },
            }, //Outputs results as a hosted feature serivce.
          };
    
          const jobReq = await arcgisRest.Job.submitJob({
            url: operationUrl,
            params: params,
            authentication: session,
          });
    
          // get all the results, this will start monitoring and trigger events
          const jobResp = await jobReq.getAllResults();
    
          // jobResp.aggregatedLayer.value.url
          return jobResp;
        };
    
    
    Expand
  5. Use the function to dynamically update the status element with the current status of the analysis.

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        const runAnalysis = async () => {
          const analysisUrl = await getAnalysisUrl(session);
          const operationUrl = `${analysisUrl}/FindHotSpots/submitJob`;
          const params = {
            analysisLayer: { url: points },
            shapeType: 'Hexagon',
            outputName: {
              serviceProperties: {
                name: `MapLibreGLJS_find_hot_spots_${new Date().getTime()}`,
              },
            }, //Outputs results as a hosted feature serivce.
          };
    
          const jobReq = await arcgisRest.Job.submitJob({
            url: operationUrl,
            params: params,
            authentication: session,
          });
    
          showInfo(true, "Initializing analysis...");
    
          // listen to the status event to get updates every time the job status is checked.
          jobReq.on(arcgisRest.JOB_STATUSES.Status, (jobInfo) => {
            showInfo(true, "Hot spot analysis running...");
            console.log(jobInfo.status);
          });
    
          // get all the results, this will start monitoring and trigger events
          const jobResp = await jobReq.getAllResults();
    
          // jobResp.aggregatedLayer.value.url
          return jobResp;
        };
    
    Expand
  6. Call the runAnalysis function and store its response in jobResponse.

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        const showInfo = (visible, msg) => {
          const statusUi = document.getElementById("status");
          !visible ? (statusUi.style.zIndex = 0) : (statusUi.style.zIndex = 1001);
          const infoUi = document.getElementById("info");
          infoUi.innerText = msg;
        };
    
        const runAnalysis = async () => {
          const analysisUrl = await getAnalysisUrl(session);
          const operationUrl = `${analysisUrl}/FindHotSpots/submitJob`;
          const params = {
            analysisLayer: { url: points },
            shapeType: 'Hexagon',
            outputName: {
              serviceProperties: {
                name: `MapLibreGLJS_find_hot_spots_${new Date().getTime()}`,
              },
            }, //Outputs results as a hosted feature serivce.
          };
    
          const jobReq = await arcgisRest.Job.submitJob({
            url: operationUrl,
            params: params,
            authentication: session,
          });
    
          showInfo(true, "Initializing analysis...");
    
          // listen to the status event to get updates every time the job status is checked.
          jobReq.on(arcgisRest.JOB_STATUSES.Status, (jobInfo) => {
            showInfo(true, "Hot spot analysis running...");
            console.log(jobInfo.status);
          });
    
          // get all the results, this will start monitoring and trigger events
          const jobResp = await jobReq.getAllResults();
    
          // jobResp.aggregatedLayer.value.url
          return jobResp;
        };
    
        const jobResponse = await runAnalysis();
    
    Expand

Display results

The results of a feature analysis are returned as feature data.

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        map.once('load', () => {
    
          map.addSource('violations', {
            type: 'vector',
            tiles: [`${pointsVTL}/tile/{z}/{y}/{x}.pbf`],
    
            // Attribution text retrieved from https://arcgis.com/home/item.html?id=a9b4d69b02ee4365a0fcb9610c69db9b
            attribution: "San Francisco 311, City and County of San Francisco"
    
          });
    
          map.addLayer({
            id: 'vtlLayer',
            type: 'circle',
            source: 'violations',
            'source-layer': 'DeriveNewLocationsOutput',
            paint: {
              'circle-color': 'rgb(0,0,0)',
              'circle-radius': 1.5,
            },
          });
    
          map.addSource('analysisResults', {
            type: 'geojson',
            data: {
              type: "FeatureCollection",
              features: []
            },
          });
    
          map.addLayer({
            id: 'results',
            type: 'fill',
            source: 'analysisResults',
            paint: {
              'fill-color': [
                'match',
                ['get', 'Gi_Text'],
                'Hot Spot with 99% Confidence',
                '#d62f27',
                'Hot Spot with 95% Confidence',
                '#ed7551',
                'Hot Spot with 90% Confidence',
                '#fab984',
                'Not Significant',
                '#f7f7f2',
                'Cold Spot with 90% Confidence',
                '#c0ccbe',
                'Cold Spot with 95% Confidence',
                '#849eba',
                'Cold Spot with 99% Confidence',
                '#4575b5',
                'blue',
              ],
            },
          });
    
        });
    
    Expand
  2. Access the URL of the resulting hosted feature layer from the job's response and pass it to the analysis result layer. Then, hide the status element.

    Expand
    Use dark colors for code blocks
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        map.getSource("analysisResults").setData(`${jobResponse.hotSpotsResultLayer.value.url}/query?f=geojson&where=Gi_Bin<>0&outFields=Gi_Text&token=${accessToken}`);
    
        showInfo(false, "");
    
    Expand

Run the app

Run the application and navigate to your localhost, for example https://localhost:8080.

After signing in, a request is sent to the spatial analysis service to perform a hot spot analysis. When the job is complete, the results of the analysis will display on the map. The analysis can take up to a minute to complete.

What's next?

To learn how to perform other types of feature analysis, go to the related tutorials in the Spatial analysis guide:

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