Graph signal processing and deep learning for crime prediction in São Paulo City
Data-driven intelligence for urban crime analysis and perception
Methodological studies aimed at research on legitimacy, youth, violence and cities...
Grant number: | 19/10560-0 |
Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
Start date until: | August 01, 2019 |
End date until: | July 13, 2020 |
Field of knowledge: | Applied Social Sciences - Demography - Components of Demographic Dynamics |
Principal Investigator: | Sergio França Adorno de Abreu |
Grantee: | Erick Mauricio Gómez Nieto |
Host Institution: | Faculdade de Filosofia, Letras e Ciências Humanas (FFLCH). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
Associated research grant: | 13/07923-7 - Center of the Study of Violence - NEV/USP, AP.CEPID |
Abstract São Paulo is the largest city in South America, as well as one of most diverse in types of crime. During last decades, its criminality rates have down considerably, probably by a combination of factors as a decline in the number of young people, a decrease of the unemployment rate, changes in law enforcement practices and strict control on firearms. However, the reasons to explain such a phenomenon are not completely clear. Looking for an understanding of these causal and consequence relationships is crucial to support government making-decision and ensure urban safety.This work proposes the development of a methodology for analyzing data from multiple contexts (e.g. socio-economic, crime records, environmental infrastructure) produced by São Paulo city with the purpose of identifying relationships/patterns/trends and including them into a predictive model that allows us to anticipate crime behavior. The partnership of the Centro de Ciências Matemáticas Aplicadas à Industria (CEMEAI-USP) and Núcleo de Estudos da Violência (NEV-USP), both at Universidade de Sao Paulo is the fundamental key for this study due to the interaction with experts in mathematical and computational sciences in one side, and in sociology and urban geography in the other, bring us the opportunity to assess our results in both fields of study.The effectiveness and usefulness of the proposed methodology will be demonstrated in case studies involving real data and validated by domain experts and by the capability to identify phenomena described in the literature. | |
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