Find Hot Spots

The Find Hot Spots task analyzes point data (such as crime incidents, traffic accidents, trees, and so on) or field values associated with points. It finds statistically significant spatial clusters of high incidents (hot spots) and low incidents (cold spots). Hot spots are locations with lots of points and cold spots are locations with very few points.

The result map layer shows hot spots in red and cold spots in blue. The darkest red features indicate the strongest clustering of point densities; you can be 99 percent confident that the clustering associated with these features could not be the result of random chance. Similarly, the darkest blue features are associated with the strongest spatial clustering of the lowest point densities. Features that are beige are not part of a statistically significant cluster; the spatial pattern associated with these features could very likely be the result of random processes and random chance.

Request URL

http://<analysis url>/FindHotSpots/submitJob

Request parameters

Parameter

Description

pointLayer

(Required)

The point feature layer for which hot spots will be calculated.

Syntax: As described in detail in the Feature input topic, this parameter can be one of the following:

  • A URL to a feature service layer with an optional filter to select specific features
  • A URL to a big data catalog service layer with an optional filter to select specific features
  • A feature collection

Examples:

  • {"url": <feature service layer url>, "filter": <where clause>}
  • {"layerDefinition": {}, "featureSet": {}, "filter": <where clause>}

Analysis using bins requires a projected coordinate system. When aggregating layers into bins, it is required that the input layer or processing extent (processSR) has a projected coordinate system. At 10.5.1, if a projected coordinate system is not specified when running analysis the World Cylindrical Equal Area (wkid 54034) projection will be used.

binSize

(Required)

The distance for the square bins the pointLayer will be aggregated into.

Example: "binSize": 100

binSizeUnit

(Required)

The distance unit for the bins with which hot spots will be calculated. The linear unit to be used with the value specified in binSize. The default is Meters. When generating bins the number and units specified determine the height and length of the square.

Values: Meters | Kilometers | Feet | Miles | NauticalMiles | Yards

Example: "binSizeUnit" : "Miles"

timeStepInterval

A numeric value that specifies duration of the time step interval. The default is none. This option is only available if the input points are time-enabled and represent an instant in time.

Example: "timeStepInterval": 20

timeStepIntervalUnit

A string that specifies units of the time step interval. The default is none. This option is only available if the input points are time-enabled and represent an instant in time.

Values: Milliseconds | Seconds | Minutes | Hours | Days | Weeks| Months | Years

Example: "timeStepIntervalUnit" : "Minutes"

timeStepAlignment

Defines how aggregation will occur based on a given timeStepInterval. Options are as follows:

  • StartTime—Time is aligned to the first feature in time.
  • EndTime—Time is aligned to the last feature in time.
  • ReferenceTime—Time is aligned a specified time in timeStepReference.

timeStepReference

(Required if timeStepAlignment is ReferenceTime )

A date that specifies the reference time to align the time slices to, represented in milliseconds from epoch. The default is January 1, 1970, at 12:00 a.m. (epoch time stamp 0). This option is only available if the input points are time-enabled and of time type instant.

Example: "timeStepReference" : 946684800000

neighborhoodDistance

(Required)

The size of the neighborhood within which to calculate the hot spots. The radius size must be larger than binSize.

Example: "radius": 100

neighborhoodDistanceUnit

(Required)

The distance unit for the radius defining the neighborhood where the hot spots will be calculated. The linear unit to be used with the value specified in binSize. The default is Meters.

Values: Meters | Kilometers | Feet | Miles | NauticalMiles | Yards

Example: "radiusUnit" : "Miles"

outputName

(Required)

The task will create a feature service of the results. You define the name of the service.

context

Context contains additional settings that affect task execution. For this task, there are two settings:

  • Extent (extent)—A bounding box that defines the analysis area. Only those features that intersect the bounding box will be analyzed.
  • Processing Spatial Reference (processSR)—The features will be projected into this coordinate system for analysis.

Syntax:
{
"extent" : {extent},
"processSR" : {spatial reference},
"outSR" : {spatial reference},
"dataStore":{data store}
}

f

The response format. The default response format is html.

Values: html | json

Response

When you submit a request, the service assigns a unique job ID for the transaction.

Syntax:
{
"jobId": "<unique job identifier>",
"jobStatus": "<job status>"
}

After the initial request is submitted, you can use jobId to periodically check the status of the job and messages as described in Checking job status. Once the job has successfully completed, use the jobId to retrieve the results. To track the status, you can make a request of the following form:

http://<analysis url>/FindHotSpots/jobs/<jobId>

Analysis results

When the status of the job request is esriJobSucceeded, you can access the results of the analysis by making a request of the following form:

http://<analysis url>/FindHotSpots/jobs/<jobId>/results/output?token=<your token>&f=json

Parameter

Description

output

The result of Find Hot Spots is a feature layer that provides information about statistically significant hot and cold features. The output is always polygons of the bin size specified at tool run.

The result layer has the following attributes:

  • An ID field (FID)—An integer field with a unique value for every feature.
  • Join_Count—An integer field is created with values reflecting the number of points in each aggregation bin.
  • Hot/Cold Intensity—A numeric (double) field with standard deviations representing the intensity of spatial clustering.
  • Confidence Bin—Symbolizes the results. Values range from -3 to +3 and reflect statistical significance. Use blue to draw values less than zero and red to draw values greater than zero. Use the darkest blue for features equal to -3, medium blue for -2, and light blue for -1. Use the darkest red for features equal to 3, medium red for 2, and the lightest red or pink for 1. A confidence bin value of zero means no statistically significant clustering, and these features should be drawn in white or beige (the color selected should be neutral).

Example:
{"url": 
"http://<analysis url>/FindHotSpots/jobs/<jobId>/results/output"}

The result has properties for parameter name, data type, and value. The contents of value depend on the outputName parameter provided in the initial request. The value contains the URL of the feature service layer.

{
"paramName":"output", 
"dataType":"GPRecordSet",
"value":{"url":"<hosted featureservice layer url>"}
}

See Feature output for more information about how the result layer is accessed.

Science behind Hot Spot analysis

The Find Hot Spots task calculates the Getis-Ord Gi* statistic (pronounced G-i-star) for each feature in a feature layer. The service works by reviewing each feature within the context of neighboring features. To be a statistically significant hot spot, a feature will have a high incident count and will be surrounded by other features with incident counts. The local sum for a feature and its neighbors is compared proportionally to the sum of all features; when the local sum is very different from the expected local sum, and when that difference is too large to be the result of random chance, a statistically significant z-score results.

Potential applications

Applications can be found in crime analysis, epidemiology, voting pattern analysis, economic geography, retail analysis, traffic incident analysis, and demographics. Some examples include the following:

  • Where is the disease outbreak concentrated?
  • Where are kitchen fires a larger than expected proportion of all residential fires?
  • Where should the evacuation sites be located?
  • Where do peak intensities occur?
  • In which locations should we allocate more of our resources?

Calculations

Mathematics for the Gi* statistic

Additional resources

Mitchell, Andy. The ESRI Guide to GIS Analysis, Volume 2. ESRI Press, 2005.

Getis, A. and J.K. Ord. 1992. "The Analysis of Spatial Association by Use of Distance Statistics" in Geographical Analysis 24(3).

Ord, J.K. and A. Getis. 1995. "Local Spatial Autocorrelation Statistics: Distributional Issues and an Application" in Geographical Analysis 27(4).

The spatial statistics resource page has short videos, tutorials, web seminars, articles and a variety of other materials to help you get started with spatial statistics.

Scott, L. and N. Warmerdam. Extend Crime Analysis with ArcGIS Spatial Statistics Tools in ArcUser Online, April–June 2005.

10/6/2017