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Even random spatial patterns exhibit some degree of clustering. In addition, our eyes and brains naturally try to find patterns even when none exist. Consequently, it can be difficult to know if the patterns in your data are the result of real spatial processes at work or just the result of random chance. This is why researchers and analysts use statistical methods like Find Hot Spots (Getis-Ord Gi*) to quantify spatial patterns. When you do find statistically significant clustering in your data, you have valuable information. Knowing where and when clustering occurs can provide important clues about the processes promoting the patterns you're seeing. Knowing that residential burglaries, for example, are consistently higher in particular neighborhoods is vital information if you need to design effective prevention strategies, allocate scarce police resources, initiate neighborhood watch programs, authorize in-depth criminal investigations, or identify potential suspects.
To create a hot spot layer, do the following:
The Hot spot analysis pane opens.
The point layer must contain at least 60 points to perform a hot spot analysis.
The available options depend on the number of points in your layer. Choose from the following:
When you choose to find hot and cold spots by point densities, you can restrict the analysis to a specific area of the map, rather than on the entire map. Check the Restrict analysis check box, and choose one of the following options from the drop-down menu:
When the analysis is complete, a new layer is created and appears in the Contents pane. For the points or the areas in this result layer, the darker the red or blue colors appear, the more confident you can be that clustering is not the result of random chance. Points or areas displayed in beige, on the other hand, are not part of any statistically significant cluster; the spatial pattern associated with these features could very likely be the result of random chance. Sometimes the results of your analysis will indicate that there aren't any statistically significant clusters at all. This is important information to have. When a spatial pattern is random, there are no clues about underlying causes. In these cases, all of the features in the results layer will be beige. When you do find statistically significant clustering, however, the locations where clustering occurs are important clues about what might be creating the clustering. Finding statistically significant spatial clustering of cancer associated with certain environmental toxins, for example, can lead to policies and actions designed to protect people. Similarly, finding cold spots of childhood obesity associated with schools promoting after-school sports programs can provide strong justification for encouraging these types of programs more broadly.
For technical details on how the Hot Spot tool works, see How hot spot analysis works.