When rates are used as estimates for an underlying risk of a rare event (e.g., cancer, AIDS, homicide), those with a small base population have high variance and are thus less reliable. The spatial smoothing techniques, such as the ﬂoating catchment area method and the empirical Bayesian smoothing method, as discussed in Chapter 2, can be used to mitigate the problem. This chapter begins with a survey of various approaches to the problem of analyzing rare events in a small population in Section 8.1. Two geographic approaches, namely, the ISD method and the spatialorder method, are fairly easy to implement and are introduced in Section 8.2. The spatial clustering method based on the scale-space theory requires some programming and is discussed in Section 8.3. In Section 8.4, the case study of analyzing homicide patterns in Chicago is presented to illustrate the scale-space melting method implemented in Visual Basic. The section also provides a brief review of the substantive issues: job access and crime patterns. The chapter is concluded in Section 8.5 with a brief summary.