A Two‐Stage Framework for Detecting Spatial Heterogeneity Boundaries From Irregular Point Observations

Detecting spatial heterogeneity boundaries from irregular point observations is important for understanding abrupt changes in spatial processes and producing interpretable boundary maps. However, irregularly sampled spatial data lack a natural ordering, making conventional one‐dimensional change point detection difficult to apply directly. We develop a modular two‐stage spatial analysis framework that connects spatial interpolation with established change point detection procedures. First, interpolation reconstructs a gridded representation of the underlying spatial field. Second, the interpolated surface is decomposed into geographically ordered one‐dimensional spatial profiles, where change point detection is used to identify boundaries between heterogeneous regions. Simulation experiments under different boundary configurations, spatial error structures, interpolation settings, and detection methods show that the framework can recover major boundary patterns, especially when appropriate interpolation smoothing is combined with stable detection methods. An application to January 2025 PM 2.5 monitoring data in China demonstrates its usefulness for extracting interpretable boundary structures from irregular environmental monitoring networks.