Integration of Volunteered Geographic Information (VGI) into Commercial Gravitation Models

The digital transformation of urban environments has generated a hybrid space where physical distance and digital reputation concurrently shape consumer behavior. Traditional spatial interaction models, such as the classic Huff model, rely heavily on static socioeconomic data and geographic friction, omitting the growing influence of online user perceptions. This paper introduces a hybrid commercial gravitation framework that incorporates Volunteered Geographic Information (VGI) through natural language processing (NLP). Using a dataset of coffee shops in the Cuauhtémoc municipality, Mexico City, we computed a composite digital attraction indicator (Wj) by multiplying the text sentiment intensity extracted via pysentimiento (a RoBERTa-based architecture) by the logarithm of review volume. The model’s predictive performance was validated against real-world consumption proxies using Spearman’s rank correlation (ρ), competing against Nearest Facility (Voronoi), classic Huff, and XGBoost algorithms. The proposed hybrid framework achieved a remarkable correlation (ρ=0.8647,p<0.001), drastically outperforming the classic Huff model (ρ=0.2782) and the deterministic Voronoi approach (ρ=−0.1580). While XGBoost achieved a perfect empirical fit, it lacked spatial interpretability. These findings demonstrate that combining linguistic sentiment with physical spatial friction provides a highly predictive, explanatory, and theoretically robust framework for modern geomarketing and urban planning.