Location Intelligence: Building an Autonomous Site Selection Engine with Geospatial AI

Location Intelligence: Building an Autonomous Site Selection Engine with Geospatial AI How I Optimized Retail Expansion Using Spatial Clustering, Urban Mobility Data, and Interactive Visualization TL;DR I built an experimental autonomous engine called SiteScanner-AI to solve the classic business problem of retail site selection. By synthesizing urban demographic layers and mobility data, I created a weighted ROI scoring model that identifies optimal locations while accounting for competitor cannibalization. The project uses Python, Folium, and Scikit-learn to transform raw geospatial data into actionable, interactive investment heatmaps. Introduction From my experience working with various data science frameworks, I have often observed that the most challenging aspect of retail expansion is not just finding a "good" location, but finding the "optimal" one within a complex urban mesh. In my opinion, traditional intuition-based site selection is increasingly inadequate in a data-saturate