Summary
Identifies statistically significant spatial clustering of high values (hot spots) or low values (cold spots), or data counts, in your data. Use this tool to uncover hot and cold spots of high and low home values, crime densities, traffic accident fatalities, unemployment or biodiversity, for example.
The result map layer shows hot spots in red and cold spots in blue. The darkest red features indicate the strongest clustering of high values or 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 low values or 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.
Illustration
Usage
Quite a lot of data is available for polygon features, such as census tracts, counties, voter districts, hospital regions, parcels, park and recreation boundaries, watersheds, land-cover classifications and climate zones. When your input layer contains polygon features, you will need to specify a numeric field that will be used to find clusters of high and low values. This field might represent the following:
- Counts (such as the number of households)
- Rates (such as the proportion of the population holding a college degree)
- Averages (such as the mean or median household income)
- Indices (such as a score indicating whether household spending on sporting goods is above or below the national average)
A variety of data is also available as point features. Examples of features most often represented as points include crime incidents, schools, hospitals, emergency call events, traffic accidents, water wells, trees, and boats. Sometimes you will be interested in analyzing data values (a field) associated with each point feature. In other cases, you will only be interested in evaluating the clustering of the points themselves. The decision to provide an analysis field or not will depend on the question you are asking. You will want to provide an analysis field to answer questions like Where do high and low values cluster?
For some point data, typically when each point represents an event, incident, or indication of presence/absence, there won't be an obvious analysis field to use. In these cases, you just want to know where clustering is unusually (statistically significant) intense or sparse.
For an analysis of point counts, polygon features are placed over the points and the number of points that fall within each area is counted. These polygon features can be a fishnet grid that the tool creates for you, or an area layer that you provide. The tool then finds clusters of high and low point counts associated with each area feature.
Syntax
FindHotSpots(analysisLayer, outputName, {analysisField}, {divideByField}, {boundingPolygonLayer}, {aggregatePolygonLayer})| Parameter | Explanation | Data Type |
analysisLayer | The point or polygon feature layer for which hot spots will be calculated. | Feature Set |
outputName | The name of the output layer to create on your portal. | String |
analysisField (Optional) | A numeric field (number of incidents, crime rates, test scores, and so on) to be evaluated. The field you select might represent the following:
| Field |
divideByField (Optional) | The numeric field in the input layer that will be used to normalize your data. For example, if your points represent crimes, dividing by total population would result in an analysis of crimes per capita rather than raw crime counts. | Field |
boundingPolygonLayer (Optional) | When the analysis layer is points and no analysis field is specified, you can provide polygon features that define where incidents could have occurred. For example, if you are analyzing boating accidents in a harbor, the outline of the harbor might provide a good boundary for where accidents could occur. When no bounding areas are provided, only locations with at least one point will be included in the analysis. | Feature Set |
aggregatePolygonLayer (Optional) | When the input layer contains points and no analysis field is specified, you can provide polygon features into which the points will be aggregated and analyzed, such as administrative units. The number of points that fall within each polygon is counted and the point count in each polygon is analyzed. | Feature Set |
Derived Output
| Name | Explanation | Data Type |
| outputLayer | The output hot spot layer. | Feature Set |
Environments
Licensing information
- Basic: Requires your account in ArcGIS Enterprise to have the Perform Analysis privilege
- Standard: Requires your account in ArcGIS Enterprise to have the Perform Analysis privilege
- Advanced: Requires your account in ArcGIS Enterprise to have the Perform Analysis privilege