A Geospatial Retail Intelligence Database for Urban Commercial Planning: Integrating POIs, Street Views, Mobility Heat Maps, and Road Networks
Main article
Abstract
Urban commercial planning increasingly depends on data infrastructures that translate heterogeneous spatial signals into actionable knowledge. Existing retail-location studies often rely on one data family, such as points of interest or transport accessibility, and therefore underrepresent the combined influence of human mobility, streetscape perception, facility synergy, and network structure. This article develops a geospatial retail intelligence database for urban commercial planning by integrating retail and facility POIs, street-view perception features, mobility heat maps, and road-network indicators into a governed spatial feature store. The proposed database is designed around 500-meter planning grids, multi-source metadata, repeatable quality-control rules, and interpretable analytical outputs for retail density assessment. Using an illustrative Shenzhen-style urban dataset, the study demonstrates how the database supports density benchmarking, threshold-sensitive feature analysis, and scenario-oriented planning for light-asset and capital-intensive retail formats. The results show that a unified geospatial database improves planning interpretability by linking facility proximity, accessibility, population dynamics, and perceptual streetscape conditions. The article contributes a database-centered framework for transforming scattered urban data into a reusable commercial intelligence asset for planners, retailers, and data-driven AI applications.
