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An interpretable predictive model for crime forecasting using Graph Neural Network

Grant number: 22/03941-0
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): December 01, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Luis Gustavo Nonato
Grantee:Priscylla Maria da Silva Sousa
Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID
Associated scholarship(s):23/05783-5 - Investigating the disagreement problem in local explanation methods, BE.EP.DR


Urban crime is a huge problem that affects the safety and security of society, especially in big cities. So, it is crucial the creation of mechanisms that help public agents to prevent crimes occurrences and understand which factors influence crime activities. In this context, machine learning methods have been applied to crime forecasting. However, the existing studies do not explore the city's structure, like the street network, and use limited data. Graph Neural Network (GNN) is a deep learning model with a solid potential for crime forecasting. Nevertheless, just a few studies use GNNs for crime. Moreover, it is hard to explain GNN predictions. In this project, we will investigate and propose a GNN model that captures the dynamics of crime using the city's street network and relying on a set of external factors, such as meteorological and socioeconomic data, to enable a more extensive comprehensive. In addition, the model will be integrated into an explainability technique to make the prediction easy to understand.

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