Note: When input features are spatially scattered (see Figure 7a), the KDE can only identify spatial cluster, but it mixes the cluster of high values (i.e., hotspots) and the cluster of low values (coldspots); that is, the KDE can neither tell what the cluster is nor whether it is significant. Fortunately, the Local Moran’s I and Getis-Ord Gi^* statistics can both make it. The difference is that the Local Moran’s I is more efficient at identifying the outliers (see Figure 7a-(2) above), while the Gi^* statistics is even better at identifying statistically significant hotspots and coldspots with different confidence levels (see Figure 7a-(3)). When input features are spatially uniform grid data (see Figure 7b), the KDE becomes inefficient, while the Local Moran’s I and Gi^* statistics work well in this case, and the Gi^* statistics especially holds its unique superiority.