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.
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:
REST web example:
REST scripting example:
Analysis using bins requires a projected coordinate system. When aggregating layers into bins, the input layer or processing extent (processSR) must have 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.
The distance for the square bins the pointLayer will be aggregated into.
REST web example: 108.3
REST scripting example: "binSize" : 100
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
REST web example: Meters
REST scripting example: "binSizeUnit" : "Miles"
The size of the neighborhood within which to calculate the hot spots. The radius size must be larger than binSize.
REST web example: 150.5
REST scripting example: "neighborhood" : 100
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
REST web example: Miles
REST scripting example: "neighborhoodUnit" : "Miles"
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.
REST web example: 20
REST scripting example: "timeStepInterval" : 20
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
REST web example: Minutes
REST scripting example: "timeStepIntervalUnit" : "Minutes"
Defines how aggregation will occur based on a given timeStepInterval. Options are as follows:
REST web example:StartTime
REST scripting example:"timeStepAlignment" : "StartTime"
(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.
REST web example: 946684800000
REST scripting example: "timeStepReference" : 946684800000
The task will create a feature service of the results. You define the name of the service.
REST web example: myOutput
REST scripting example: "outputName" : "myOutput"
The context parameter contains additional settings that affect task execution. For this task, there are four settings:
The response format. The default response format is html.
Values: html | json
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 jobId to periodically check the status of the job and messages as described in Checking job status. Once the job has successfully completed, use 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 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
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:
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.
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.
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?
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.