Utilizing statistical methods as a risk assessment tool can lead to potentially effective solutions and policies that address various social issues. One usage for such methods is in observation of crime trends within a municipality. Cluster and hotspot analysis is often practiced in criminal statistics to delineate potential areas at-risk of recurring criminal activity. Two approaches to this analytical method are Nearest Neighbor Hierarchical Clustering (NNHC) and Kernel Density Estimation (KDE). Kernel Density Estimation fits incidence points on a grid based on a kernel and bandwidth determined by the analyst. Nearest Neighbor Hierarchical Clustering, a less common and less quantitative method, derives clusters based on the distance between observed points and the expected distance for points of a random distribution. Crime data originated from a public web map and database service that acquires data from the Johnson City Police Department, where each incident is organized into one of many broad categories such as assault, theft, etc. Preliminary analysis of raw volume data shows trends of high crime volume in expected locales; highly trafficked areas such as downtown, the Mall, both Walmarts, as well as low-income residential areas of town. The two methods, KDE and NNHC, dispute the size and location of many clusters. A more in-depth analysis of normalized data with refined parameters may provide further insight on crime in Johnson City.