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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Crime prediction through urban metrics and statistical learning

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Author(s):
Alves, Luiz G. A. [1] ; Ribeiro, V, Haroldo ; Rodrigues, Francisco A. [1]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 505, p. 435-443, SEP 1 2018.
Web of Science Citations: 6
Abstract

Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multicollinearity in urban indicators, it is common to find controversial conclusions about the influence of some urban indicators on crime. Machine learning ensemble-based algorithms can handle well such problems. Here, we use a random forest regressor to predict crime and quantify the influence of urban indicators on homicides. Our approach can have up to 97% of accuracy on crime prediction, and the importance of urban indicators is ranked and clustered in groups of equal influence, which are robust under slightly changes in the data sample analyzed. Our results determine the rank of importance of urban indicators to predict crime, unveiling that unemployment and illiteracy are the most important variables for describing homicides in Brazilian cities. We further believe that our approach helps in producing more robust conclusions regarding the effects of urban indicators on crime, having potential applications for guiding public policies for crime control. (C) 2018 Elsevier B.V. All rights reserved. (AU)

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: 16/16987-7 - A complex system approach to urban planning and development
Grantee:Luiz Gustavo de Andrade Alves
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 16/25682-5 - Information spreading in complex networks
Grantee:Francisco Aparecido Rodrigues
Support Opportunities: Regular Research Grants