Year: 2026
Type: preprint
Abstract: This study investigates the prediction of urban crime in the São Paulo metropolitan region using an integrated spatio-temporal modeling framework. We compare Spatial Autoregressive (SAR) models with Artificial Neural Networks (ANN), including Long Short-Term Memory (LSTM) architectures, leveraging a comprehensive dataset of crime incidents, demographic indicators, and educational and economic variables at the municipal level. The SAR model captures spatial dependencies and allows for interpretable regional analysis, while LSTM networks model complex nonlinear patterns and temporal dynamics. Empirical results show that although the SAR model explains crime variation effectively, LSTM achieves substantially better predictive performance (RMSE: 22.64 vs 77.18 forSAR). This suggests the potential of deep learning techniques in improving urban crime forecasting. Our findings highlight the importance of combining spatial econometrics and machine learning to support evidence-based public safety policies and targeted social interventions.
Source: Research Square
Institution: Universidade de São Paulo
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7160844729