Urban Inference
Abstract
A machine learning model that predicts commercial and residential land-use patterns for any city in the world. Built for real estate developers, it provides a data-driven vision that compares historical and current urban data, identifies influence zones, growth corridors, and impact areas to support more accurate investment decisions.
Context / Problem
Real estate investment decisions are often driven by intuition or lagging indicators. Urban Inference addresses this gap by treating the city as a dynamic dataset: every block, parcel, and street network edge carries latent signals about where commercial activity will concentrate and where residential demand will surge.
Methodology
The model ingests OpenStreetMap features, satellite-derived building footprints, and publicly available demographic data. A graph-convolutional network encodes spatial adjacency while a temporal module captures year-over-year land-use transitions. The result is a probability map — overlaid on interactive cartography — showing predicted use shifts with confidence intervals, enabling developers to compare current baselines against 5-year projections at parcel resolution.