- URL:
- https://<geoanalytics-url>/FindHotSpots
- Methods:
GET
- Version Introduced:
- 10.5
Description
The Find
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.
Science behind Hot Spot analysis
The Find
task calculate the Getis-Ord Gi* statistics (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.
See below for additional resources .
Potential applications
You can apply Hot Spot analysis to 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
Request parameters
Parameter | Details |
---|---|
| The point feature layer for which hot spots will be calculated. 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. If a projected coordinate system is not specified when running analysis, a projection will be picked based on the extent of the data. Syntax: As described in Feature input, this parameter can be one of the following:
REST examples
|
| The distance for the square bins the REST examples
|
| The distance unit for the bins with which hot spots will be calculated. The linear unit to be used with the value
specified in Values: REST examples
|
| The size of the neighborhood within which to calculate the hot spots. The radius size must be larger than REST examples
|
| 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 Values: REST examples
|
|
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 examples
|
|
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. REST examples
|
| Defines how aggregation will occur based on a given
REST examples
|
|
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 examples
|
|
The task will create a feature service of the results. You define the name of the service. REST examples
|
|
The
Syntax:
|
|
The response format. The default response format is Values: |
Example usage
Below is a sample request URL for Find
:
https://webadaptor.domain.com/server/rest/services/System/GeoAnalyticsTools/GPServer/FindHotSpots/submitJob?pointLayer={"url":"https://webadaptor.domain.com/server/rest/services/Hurricane/hurricaneTrack/0"&binSize=10&binSizeUnit=Miles&neighborhoodDistance=3&neighborhoodDistanceUnit=Miles&timeStepInterval=20&timeStepIntervalUnit=Minutes&timeStepAlignment=StartTime&timeStepReference=&outputName=myOutput&context={"extent":{"xmin":-122.68,"ymin":45.53,"xmax":-122.45,"ymax":45.6,"spatialReference":{"wkid":4326}}}&f=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 job
to periodically check the status of the job and messages as described in Check job status. Once the job has successfully completed, use job
to retrieve the results. To track the status, you can make a request of the following form:
https://<analysis-url>/FindHotSpots/jobs/<jobId>
Access results
When the status of the job request is esri
, you can access the results of the analysis by making a request of the following form:
https://<analysis-url>/FindHotSpots/jobs/<jobId>/results/output?token=<your token>&f=json
Response | Description |
---|---|
| The result of
The result has properties for parameter name, data type, and value. The contents of
See Feature output for more information about how the result layer is accessed. |
Additional resources
- Getis, A. and J.K. Ord. 1992. "The Analysis of Spatial Association by Use of Distance Statistics" in Geographical Analysis 24(3).
- Mitchell, Andy. The ESRI Guide to GIS Analysis, Volume 2. ESRI Press, 2005.
- Ord, J.K. and A. Getis. 1995. "Local Spatial Autocorrelation Statistics: Distributional Issues and an Application" in Geographical analysis 27(4).
- Scott, L. and N. Warmerdam. Extend Crime Analysis with ArcGIS Spatial Statistics Tools in ArcUser Inline, April-June 2005.
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.