Criminality, insecurity, and legitimacy: a transdisciplinary approach

• Grant number: 22/09091-8
• Support Opportunities: Research Grants - eScience and Data Science Program - Thematic Grants
• Start date: March 01, 2023
• End date: February 29, 2028
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Luis Gustavo Nonato
• Grantee: Luis Gustavo Nonato
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Pesquisadores principais: Marcos César Alvarez
• Associated researchers: Afonso Paiva Neto ; Alneu de Andrade Lopes ; Alvaro Jose Riascos Villegas ; Bruna Gisi Martins de Almeida ; Cibele Maria Russo Novelli ; Daielly Melina Nassif Mantovani ; Daniela Osvald Ramos ; Diego de Castro Rodrigues ; Frederico Castelo Branco Teixeira ; Giane Silvestre ; Jose Claudio Teixeira e Silva Junior ; Leandro Alves Neves ; Marcelo Batista Nery ; Marcos Medeiros Raimundo ; Maria Gorete Marques de Jesus ; Natasha Bachini Pereira ; Pablo Emanuel Romero Almada ; Renan Theodoro de Oliveira ; Ricardo Marcondes Marcacini ; Rodrigo Calbucci ; Rogério Galante Negri ; Sergio França Adorno de Abreu ; Thales Miranda de Almeida Vieira ; Thomas Kaue Dal Maso Peron ; Victor Hugo Barella ; Vitor Souza Lima Blotta ; Wallace Correa de Oliveira Casaca
• Associated scholarship(s): 24/23688-2 - Statistical modeling of impunity: inferential and predictive methods for crime data in the state of São Paulo, BP.IC; 24/14993-6 - The Criminal Justice Administration System: old and new institutional arrangements, BP.MS; 24/07478-8 - Exploring the Relationship between Road Network and Vehicle Theft in São Paulo, BP.MS; + associated scholarships 24/15983-4 - Data Integration and Crime Hotspot Prediction, BP.PD; 23/15618-1 - Unifying Geospatial Representations and Latent Crime Mapping from a Computational and Socio-Analytical Perspective, BP.PD; 23/07796-7 - Social Media and Political Discourse: literature review and framing democracy in social media, BP.IC; 23/04137-2 - Graph Neural Networks Application in crime prediction - Adaptation of a traffic accident model, BP.IC - associated scholarships

Abstract

Since the 1970s, Brazilian society has been undergoing a process of transition from military dictatorship to democracy. With this political transition, it was expected that conflicts would be progressively resolved, reducing violence. However, that didn't happen. Indeed, the transition was accompanied by an explosion of internal conflicts, much of which associated with urban crime. There is still no consensus among social scientists on the reasons that explain these trends in the evolution of crime and violence in Brazilian society, particularly in large cities. Among the explanations that emerge most frequently is the exhaustion of traditional models of security policies, which have become obsolete. Based on Data Science and Artificial Intelligence techniques, this project aims to develop innovative analytical methodologies to investigate complex phenomena associated with crime and the persistence of feelings of insecurity in the population. The study of the relationship between criminality, the feeling of insecurity, and the legitimacy of justice institutions is also the main focus of the project. Another important objective is to train human resources so that they are able to use Data Science and Artificial Intelligence techniques in the field of Human and Social Sciences (CHS), bringing new perspectives of approach and differentiated training for professionals and researchers. (AU)

Scientific publications

(8)

(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)

DOS SANTOS, NICOLAS ROQUE; MINATEL, DIEGO; BARIA VALEJO, ALAN DEMETRIUS; LOPES, ALNEU DE A.. Bipartite Graph Coarsening for Text Classification Using Graph Neural Networks. PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I, v. 14469, p. 16-pg., 2024-01-01. (21/06210-3, 22/03090-0, 22/09091-8, 20/09835-1)

SILVA, PRISCYLLA; SILVA, CLAUDIO; NONATO, LUIS GUSTAVO. Exploring the Relationship Between Feature Attribution Methods and Model Performance. NEURIPS WORKSHOPS, 2020, v. 257, p. 10-pg., 2024-01-01. (23/05783-5, 22/09091-8, 22/03941-0)

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)

DOS SANTOS, NICOLAS ROQUE; MINATEL, DIEGO; BARIA VALEJO, ALAN DEMETRIUS; LOPES, ALNEU DE ANDRADE. Semi-supervised Coarsening of Bipartite Graphs for Text Classification via Graph Neural Network. 2024 IEEE 11TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, DSAA 2024, v. N/A, p. 10-pg., 2024-01-01. (20/09835-1, 22/09091-8, 21/06210-3)

SANTOS, TIAGO PAULINO; SOUZA, JOAO MATHEUS SIQUEIRA; VIEIRA, THALES; NONATO, LUIS GUSTAVO. Space-Time Urban Explorer: A Visual Tool for Exploring Spatiotemporal Crime and Patrolling Data. 2024 37TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES, SIBGRAPI 2024, v. N/A, p. 6-pg., 2024-01-01. (22/09091-8)

NONATO, LUIS GUSTAVO; RUSSO, VICTOR; COSTA, BERNARDO; MORENO-VERA, FELIPE; TOLEDO, GUILHERME; DE JESUS, OSNI BRITO; VIEIRA, ROBSON; LENTINI, MARCO; POCO, JORGE. Assessing timber trade networks and supply chains in Brazil. NATURE SUSTAINABILITY, v. 8, n. 2, p. 9-pg., 2025-01-06. (22/09091-8, 21/07012-0)

MINATEL, DIEGO; DOS SANTOS, NICOLAS ROQUE; DA SILVA, VINICIUS FERREIRA; CURI, MARIANA; LOPES, ALNEU DE ANDRADE. Item Response Theory in Sample Reweighting to Build Fairer Classifiers. INFORMATION MANAGEMENT AND BIG DATA, SIMBIG 2023, v. 2142, p. 15-pg., 2024-01-01. (20/09835-1, 22/09091-8)

DE MORAES JUNIOR, MARCELO ISAIAS; MARCACINI, RICARDO MARCONDES; IEEE. On the Use of Aggregation Functions for Semi-Supervised Network Embedding. 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, v. N/A, p. 8-pg., 2023-01-01. (22/09091-8, 19/25010-5, 19/07665-4)