Optimizing Retail Site Selection Using Geospatial Analytics

Geospatial analytics is a crucial tool in strategic retail decision - making, influencing store location selection, market segmentation, and supply chain efficiency. This study addresses the limitations of traditional site selection methods by integrating advanced spatial analysis techniques. Leveraging diverse datasets - including demographic, transportation, and competitive information - the proposed model employs principal component analysis for dimensionality reduction and geographically weighted regression to account for spatial heterogeneity. The findings indicate that this approach enhances predictive accuracy and optimizes retail location selection. By incorporating spatial autocorrelation measures and similarity metrics, this study contributes a novel framework for data - driven site selection, offering practical implications for business strategy and future research.