Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey

Mapping of spatial hotspots, i.e., regions with significantly higher rates generating cases certain events (e.g., disease or crime cases), is an important task in diverse societal domains, including public health, safety, transportation, agriculture, environmental science, etc. Clustering techniques required by these domains differ from traditional clustering methods due to the high economic and social costs spurious results false alarms clusters). As a result, statistical rigor needed explicitly control rate detections. To address this challenge, for statistically-robust scan statistics) have been extensively studied data mining statistics communities. In survey we present up-to-date detailed review models algorithms developed field. We first general taxonomy clustering, covering key steps modeling, region enumeration maximization, significance testing. further discuss different paradigms within each steps. Finally, highlight research gaps potential future directions, which may serve as stepping stone new ideas thoughts growing field beyond.