When a layer contains a large number of point features, showing each feature individually on the map is often not useful. In this scenario, point features often overlap, making it difficult to distinguish between features. Even when they do not overlap, it is usually difficult or impossible to visually extract meaningful information when hundreds or thousands of points are shown all at once.

One approach to resolving this issue is to create a heat map. Two types of heat maps can be created:

• Point density heat map—A point density heat map represents the geographic density of point features on a map. It uses the points in the layer to calculate and display the relative density of points as smoothly varying sets of colors ranging from cool (low density of points) to hot (many points).

  

• Weighted measure heat map—A weighted measure heat map is calculated and displayed based on numeric measure values in your data. For example, you can display your stores layer as a heat map that weights store locations by revenue—ranging from cool (low revenue) to hot (high revenue).

  

It is best to avoid heat maps if you have only a few point features; instead, map the actual points.