Precision classification and governance of mining development based on resource endowment and spatial patterns: a case study of Northern Xinjiang, China

This study examines Northern Xinjiang in the Central Asian Orogenic Belt to systematically reveal the spatial mismatch between the natural clustering of mineral resources and artificially defined administrative boundaries. By constructing a dual-track analytical framework of “administrative units versus metallogenic belt units”, we apply spatial autocorrelation (Global/Anselin Local Moran’s I), Getis-Ord General G/Gi*, kernel density estimation, and centroid migration modeling to mine location data (2011–2021). Results indicate mining distribution is random across administrative units but shows significant, persistent clustering within metallogenic belts (Moran’s I = 0.205–0.262, p < 0.01). Notably, this clustering remained pronounced even as the total number of mines decreased by approximately 62%, highlighting the enduring control of geological endowment over mining spatial layout—an influence that transcends policy cycles and economic fluctuations. Based on these findings, we further propose a three-tier “endowment-pattern-policy” governance framework, which classifies metallogenic belts into Core Hot Spots, Emerging Potential Zones, and Marginal Scattered Areas, with differentiated management strategies. The study provides a systematic toolkit for spatial governance and supports a shift toward “nature-based precision governance” of mineral resources in China.