When Hotspots Disagree: Reconciling Cluster Detection and Relative Risk Surface Estimation in Applied Spatial Analysis

Hotspot analysis is central to many point‐referenced geospatial applications, but the term “hotspot” is often used to describe different kinds of evidence: a statistically significant cluster, a region of high estimated intensity, or an elevated relative risk surface. As a result, outputs produced by different methods are often compared within a common visual and practical language, even when they support different spatial claims. We examine this problem through a focused comparison of scan‐statistic cluster detection and kernel‐smoothed relative risk estimation for case‐control point patterns. We develop an agreement‐profile framework that separates four dimensions of comparison: Detection state, Location, Scale, and Morphology. Across repeated simulations and three real‐data examples, we show that similar overall overlap can conceal different containment, extent, and component relationships; that agreement between fitted regions is distinct from recovery of a prespecified spatial target; and that interpretation can change under alternative analytical specifications and reference measures. Rather than ranking methods, it provides a reproducible vocabulary for interpreting and communicating how hotspot outputs relate across analyses.