Analyzing and Forecasting Urban Crime

• Grant number: 23/15805-6
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: May 01, 2024
• End date: April 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Antonio Castelo Filho
• Grantee: Victor Hugo Barella
• 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

Abstract

The analysis and prediction of crimes in urban environments pose significant challenges from a data science perspective. These challenges encompass temporal data dependencies, the inherent street graph structure, high granularity (down to street segment levels), and the sparse nature of data in both time and space. This project focuses on investigating and implementing a predictive model capable of tackling these complexities. The primary approach will involve leveraging state-of-the-art spatiotemporal neural networks on graphs. Additionally, we'll explore model interpretation techniques to address ethical considerations related to the use of these models in urban crime prevention.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SALINAS, KARELIA; BARELLA, VICTOR; VIEIRA, THALES; NONATO, LUIS GUSTAVO. A visual methodology to assess spatial graph vertex ordering algorithms. 2024 37TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES, SIBGRAPI 2024, v. N/A, p. 6-pg., 2024-01-01. (23/15805-6, 22/09091-8, 20/07012-8)