How to Interpret Density Maps
Density Maps or “Hotspots” are a huge source of fascination for the public and law enforcement. The attraction is obvious—a picture is worth a thousand words—however, the science behind actually identifying a hotspot varies widely from user to user. Analysts are always looking for clusters, groups, and hotspots. Indeed, trying to use a set of points—such as crime events—to define an area such as a hotspot, hunting ground, activity space, etc., is an important activity for any mapper. After all, the police need to narrow their search, prioritize their deployments, or focus on an area when at all possible.
Density maps help determine areas of dense activity.
By analyzing the density of points, rather than their mere locations, it is possible for us to visualize the influence of events very clearly and to convert the locations of discrete points into areas of interest. These in turn can give us insight into where future events may occur, from where they may originate, and why certain targets may be selected.
Density is calculated by counting up the number of events within a selected range of each cell; cells with a higher count of nearby events have a higher density than cells with a lower count. This range, known as the Search Radius, must be chosen carefully. If the Search Radius is short, there may not be any cells which are in range of more than one or two events. On the other hand, if the radius is too large, every cell might be in range of every event, therefore giving a meaningless result. Search Radius selection is the most critical part of performing density analysis. RAIDS has a proprietary algorithm that determines a suitable search distance at any zoom level in hopes of providing you hotspots at any level.
