| Full text | |
| Author(s): |
Hassan, Waqar
;
Cabral, Marvin Mendes
;
Ramos, Thiago Rodrigo
;
Filho, Antonio Castelo
;
Nonato, Luis Gustavo
Total Authors: 5
|
| Document type: | Journal article |
| Source: | INTELLIGENT SYSTEMS, BRACIS 2024, PT III; v. 15414, p. 15-pg., 2025-01-01. |
| Abstract | |
Crime prediction is a critical research area for enhancing public safety and optimizing law enforcement resource allocation, and machine learning techniques have had a significant impact in this field. Traditional machine learning models have long struggled to capture complex crime patterns, primarily due to the intricate interdependence of spatial and temporal data. However, recent advancements in machine learning, particularly with Graph Neural Networks (GNNs), offer a new perspective. GNNs have demonstrated remarkable success in various applications and they can also play a significant role in crime analysis and prediction. Therefore, in this work, we explore such a potential by examining two distinct spatiotemporal GNN architectures, namely Dynamic Self-Attention Network (DySAT) and Evolving Graph Convolutional Network (EvolveGCN), assessing and comparing their effectiveness for crime prediction. Moreover, we propose a data modeling framework that integrates crime, street map graphs, and urban data, which is fundamental to properly train the GNN models. As far as we know, there is no consolidated methodology to integrate those three modalities of data, being a relevant contribution of this work. Our findings underscore the effectiveness of GNNs in crime prediction tasks, offering valuable insights for researchers and practitioners in the field of crime prevention and public safety enhancement. (AU) | |
| FAPESP's process: | 23/15618-1 - Unifying Geospatial Representations and Latent Crime Mapping from a Computational and Socio-Analytical Perspective |
| Grantee: | Thiago Rodrigo Ramos |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 22/09091-8 - Criminality, insecurity, and legitimacy: a transdisciplinary approach |
| Grantee: | Luis Gustavo Nonato |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Thematic Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 23/16334-7 - Dynamic Urban Crime Mapping: Integrating Spatio-Temporal Analysis for Predictive Modeling |
| Grantee: | Waqar Hassan |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |