GIS-Based Analysis of Retail Spatial Distribution and Driving Mechanisms in a Resource-Based Transition City: Evidence from POI Data in Taiyuan, China

Rapid urbanization in China has reshaped retail spatial structures, creating challenges of accessibility and service equity. This study employs a Geographic Information Systems (GIS)-based analytical framework to examine the spatial distribution and driving mechanisms of retail outlets in Taiyuan, a resource-based transition city in central China. Using 2023 Point of Interest (POI) data and a 2 km × 2 km grid system, kernel density estimation (KDE), Average Nearest Neighbor (ANN) Analysis, Location Quotient (LQ), and spatial autocorrelation were applied to identify clustering patterns and functional specialization. The GeoDetector (Word version, downloaded 2025) model further quantified the explanatory power of twelve natural, social, economic, and transportation variables. Results reveal a polycentric retail structure, with high-density clusters in Yingze and Xiaodian districts and under-supply in Jiancaoping and Jinyuan. Population density, nighttime light (NTL) intensity, and school distribution emerged as the strongest drivers, while topography constrained expansion. By integrating GIS-based spatial statistics with GeoDetector, the study demonstrates a transferable framework for analyzing urban retail spatial patterns. The findings extend retail geography to transition cities and provide practical guidance for optimizing retail allocation, enhancing service equity, and supporting spatial decision-making for sustainable urban development.