The Find Point Clusters task finds clusters of point features within surrounding noise based on their spatial distribution.
This task uses unsupervised machine learning clustering algorithms to detect patterns of point features based purely on spatial location and, optionally, the distance to a specified number of features.
The result map shows each cluster identified as well as features considered noise. Multiple clusters will be assigned each color. Colors will be assigned and repeated so that each cluster is visually distinct from its neighboring clusters.
This task utilizes two related algorithms. By default the HDBSCAN algorithm is used to find clusters. If a searchDistance is specified, the DBSCAN algorithm is used. DBSCAN is only appropriate if there is a very clear search distance to use for your analysis and will return clusters with similar densities. When no searchDistance is specified, HDBSCAN will use a range of distances to separate clusters of varying densities from sparser noise resulting in more data-driven clusters.
The point feature layer for which clusters will be found.
Syntax: As described in detail in the Feature Input topic, this parameter can be
A double value to use as the minimum number features to be considered a cluster. Any cluster with fewer features than the number provided will be considered noise.
Example: "minFeaturesCluster": "5"
A double value to use as the maximum distance to search for neighboring features.
The units of the search distance value is supplied by the searchDistanceUnit parameter.
Example: "searchDistance": "154"
The linear unit to be used with the distance value specified for searchDistance. You must provide a value if searchDistance has been set.
Values: Feet | Miles | Meters | Kilometers
The default is Miles
Example: "Units": "Kilometers"
If provided, the task will create a feature service of the results. You define the name of the service. If outputName is not supplied, the task will return a feature collection.
Context contains additional settings that affect task execution. For Find Point Clusters, there are two settings.
The response format. The default response format is html.
Values: html | json | kmz
When you submit a request, the service assigns a unique job ID for the transaction.
"jobId": "<unique job identifier>",
"jobStatus": "<job status>"
After the initial
request is submitted you can use the
jobId to periodically check the status of the job and messages as described in the topic Checking job status.
Once the job has successfully completed, you use
the jobId to retrieve the results. To track the status, you can make a request of the following form:
When the status of the job request is esriJobSucceded, you can access the results of the analysis by making a request of the following form.
http://<analysis url>/FindPointClusters/jobs/<jobId>/results/pointClustersResultLayer?token=<your token>&f=json
The result of Find Point Clusters is a layer containing clustered points.
The result has properties for parameter name, data type, and value. The contents of value depends upon the OutputName parameter provided in the initial request.
See Feature Output for more information about how the result layer or collection is accessed.