Dynamic Huff's Gravity Model with Covariates for Site Visitation Prediction

Learn about an enhanced version of Huff's Gravity Model in this 34-minute conference talk from the Toronto Machine Learning Series. Explore how the traditional statistical model for predicting consumer location visits can be improved by incorporating additional covariates like mobility and population behavioral data. Follow along as Winston Li, founder of Arima, explains the model formulation and demonstrates its practical application through a Canadian retailer case study. Discover how this technical enhancement to the 1963 model increases accuracy and explanability for marketing, economics, retail research, and urban planning applications.