Identifying hotspots and classifying the spatial distribution pattern of karst collapse pillars with Moran’s index in coal mine

Identifying hazardous karst collapse pillars (KCPs) is critical for ensuring safe coal mining operations. While previous studies have focused primarily on physical detection, the spatial clustering characteristics of KCPs have often been overlooked. This study proposes a spatial hotspot identification method based on Moran’s index and applies it to the Wangpo Coal Mine in Shanxi, China. The method integrates morphological feature analysis of KCPs with a combined weighting scheme using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). A spatial distribution index (SDI) was constructed through geographic information system (GIS) overlay analysis and spatial coordinate calibration. Global Moran’s I (0.1110, p < 0.05) indicates a statistically significant positive spatial autocorrelation of KCP distribution. Local Moran’s I further reveals 11 spatially significant KCPs, including 5 high-high clusters. Geological interpretation shows that these high-risk KCPs are predominantly located near the intersections of faults and folds, highlighting the structural control on KCP formation. The proposed method provides a quantitative and spatially interpretable approach for KCP risk identification and has potential for application to other geohazards exhibiting spatial aggregation patterns.