An Initial Experiment on Associations between Crimes in Brazil

Authors

DOI:

https://doi.org/10.33448/rsd-v9i11.10078

Keywords:

Criminal analysis; Crime; Data science; Data mining; Python and R together.

Abstract

Context: Crime has been a problem around the world, causing damage to societies. Education, poverty, employment and climate are some of the factors that affect the crime rate, leading authorities to spend millions annually on actions to combat violence and strategic plans to prevent and reduce crime. Objective: Applying Data Science concepts to analyze government data related to crimes in Brazil. Method: Use of data mining techniques of association rules (AR), in a controlled experiment, to detect patterns between the types of crimes, as well as the relationship between the types of crime and the months of the year. Results: In the context of associations between crimes, the states with the most interesting rules were: Bahia, with 15 associations, São Paulo, with 12 associations, Goiás, with 11, and Paraná, with 9. Highlight for the association “Robbery Resulting in Death  Cargo Roberry”, found for the State of Bahia, which reached 99% confidence (0.99). In the scope of associations between crimes and months of the year, Paraíba had 2 associations, Maranhão, Rondônia and São Paulo, with 1 association each. Highlight for rule “December  Vehicle Steal”, found for the State of São Paulo, which reached a confidence of 84% (0.84). Conclusion: The results exposed in this research assist criminal analysts in the decision-making process to prevent and reduce crime in the country, since they can allow priority in inhibiting crimes that are antecedents of other occurrences within the same state or crimes that occur in the same period.

Author Biographies

Wesckley Faria Gomes, Universidade Federal de Sergipe

Aluno especial de Mestrado pela Universidade Federal de Sergipe (UFS). Pós-graduado em Especialização em Desenvolvimento de Soluções Web pela Faculdade de Administração e Negócios de Sergipe (FANESE/SE). Graduado em Ciência da Computação pela UFS/SE. Atualmente, Analista de Sistema de TI do Tribunal de Contas do Estado de Sergipe (SE). Lattes: http://lattes.cnpq.br/0390577313618340 .

Methanias Colaço Júnior, Universidade Federal de Sergipe

Professor da Universidade Federal de Sergipe. Doutor (UFBA) e Mestre (UFCG) em Informática, coordenou projetos de Educação e Inteligência aplicada à Segurança Pública em parceria com o Ministério Público e Grupos de Combate ao Crime Organizado. Como consultor, atuou pioneiramente na concepção dos primeiros projetos de Business Intelligence de órgãos públicos e privados, tais como no Ministério Público Federal e em Tribunais de Contas. Foi professor de pós-graduação em Gestão Estratégica para Delegados de Polícia, Comandantes da PM e Bombeiros Militares, bem como foi professor de Informática Aplicada ao Direito, na Universidade Tiradentes. Atualmente, é Professor Convidado da Pós-graduação em Políticas Públicas para Cidades Inteligentes da USP e Professor Permanente do Mestrado em Computação da UFS. Universidade Federal de Sergipe. Lattes: http://lattes.cnpq.br/5575237386798588.

Kleber Henrique de Jesus Prado, Universidade Federal de Sergipe

Mestrando em Engenharia de Software pela Universidade Federal de Sergipe (UFS). Pós-graduado em Sistema de Informação para Web pela Universidade Tiradentes (UNIT/SE). Pós-graduado em Engenharia de Software pela Universidade Estácio de Sá. Graduado em Análise e Desenvolvimento de Sistemas pela Universidade Estácio de Sá. Graduado em Ciência da Computação pela UNIT/SE. Atualmente, Analista de Sistema de TI do Tribunal de Contas do Estado de Sergipe (SE). Lattes: http://lattes.cnpq.br/1809819558863469.

References

AtlasBR. (2020). AtlasBR. Recuperado de http://www.atlasbrasil.org.br

Barros, P., Baggio, I., Stege, A., & Hilgemberg, C. (2019). Economic development and crime in Brazil: a multivariate and spatial analysis.

Basili, V. R., & Weiss, D. M. (1984). A methodology for collecting valid software engineering data. IEEE Transactions on software engineering, (6), 728-738.

Basili, V., Trendowicz, A., Kowalczyk, M., Heidrich, J., Seaman, C., Münch, J., & Rombach, D. (2014). Aligning Organizations Through Measurement: The GQM+ Strategies Approach. Springer.

Belesiotis, A., Papadakis, G., & Skoutas, D. (2018). Analyzing and Predicting Spatial Crime Distribution Using Crowdsourced and Open Data. ACM Trans. Spatial Algorithms Syst.

Campos, O. S. (2018). Data analytics transparente para descoberta de padrões e anomalias na realização de convênios e contratos de repasse federais.

Catlett, C., Cesario, E., Talia, D., & Vinci, A. (2018). A Data-Driven Approach for Spatio-Temporal Crime Predictions in Smart Cities. 2018 IEEE International Conference on Smart Computing (SMARTCOMP), (pp. 17-24). Taormina.

Chapetta, W. A. (2006). Uma Infra-estrutura para Planejamento, Execução e Empacotamento de Estudos Experimentais em Engenharia de Software (Doctoral dissertation, Dissertação de Mestrado, Programa de Engenharia de Sistemas e Computação, COPPE/UFRJ, Universidade Federal do Rio de Janeiro. Rio de Janeiro, RJ, Brasil).

Chaves, M. S. R. S., & Shimizu, I. S. (2018). Síndrome de burnout e qualidade do sono de policiais militares do Piauí. Revista Brasileira de Medicina do Trabalho, 16(4), 436-441.

Gillmeister, P., & Cazella, S. (2007). Uma análise comparativa de algoritmos de regras de associação: minerando dados da indústria automotiva. Escola Regional de Banco de Dados (ERBD). Caxias do Sul, RS.

Guimarães, F. F., Joaquim, S. F., Manzi, M. P., Silva, R. C. D., Bruder-Nascimento, A. C. M. D. O., Costa, E. O., & Langoni, H. (2016). Comparison phenotypic and genotypic identification of Staphylococcus species isolated from bovine mastitis. Pesquisa Veterinária Brasileira, 36(12), 1160-1164.

Gupta, M., Chandra, B., & Gupta, M. (2014). A framework of intelligent decision support system for Indian police. J. Enterp. Inf. Manag., 27, 512-540.

Huang, S.-M. (2013). A Study of the Application of Data Mining on the Spatial Landscape Allocation of Crime Hot Spots. Communications in Computer and Information Science, 398, 274-286.

IBGE. (2020). Instituto Brasileiro de Geografia e Estatística. Recuperado de https://www.ibge.gov.br

Lallich, S., Teytaud, O., & Prudhomme, E. (2007). Association Rule Interestingness: Measure and Statistical Validation. Studies in Computational Intelligence, 43, 251-275.

Marzan, C. S., Baculo, M. J. C., de Dios Bulos, R., & Ruiz Jr, C. (2017). Time series analysis and crime pattern forecasting of city crime data. International conference on algorithms, computing and systems, pp. 113-118.

Melo, M., Teixeira, J., & Campos, G. (2012). A prediction model for criminal levels using socio-criminal data. International Journal of Electronic Security and Digital Forensics, 4, 201-214.

MSJP. (2020). Portal Brasileiro de Dados Abertos. Recuperado de http://dados.gov.br

Nogueira de Oliveira, R., & Colaço Júnior, M. (2018). Experimental Analysis of Stemming on Jurisprudential Documents Retrieval. Information, 9, 28.

Paranhos, R., Figueiredo Filho, D. B., da Rocha, E. C., da Silva Júnior, J. A., Neves, J. A. B., & Santos, M. L. W. D. (2014). Desvendando os mistérios do coeficiente de correlação de Pearson: o retorno. Leviathan (São Paulo), (8), 66-95.

Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia da pesquisa científica.

Pereira, B., & Brandão, W. (2014). ARCA : MINING CRIME PATTERNS USING ASSOCIATION RULES.

PostgreSQL. (2020). PostgreSQL. Recuperado de https://www.postgresql.org.

Prado, K. H. de J., Souza, L. S., de Jesus Junior, I. D., & Colaço Júnior, M. (2020). Applied Intelligent Data Analysis to Government Data Related to Criminal Incident: A Systematic Review. Journal of Applied Security Research, 1-35.

Procaci da Silva, A. (2004). Regras de associação quantitativas em intervalos não contínuos.

Santos, B. S., Júnior, M. C., & de Souza, J. G. (2018). A initial experimental evaluation of the neuromessenger: a collaborative tool to improve the empathy of text interactions. In Information Technology-New Generations (pp. 411-419). Springer, Cham.

Sevri, M., Karacan, H., & Akcayol, M. (2017). Crime Analysis Based on Association Rules Using Apriori Algorithm. International Journal of Information and Electronics Engineering, 7, 99-102.

Singh, N., Kaverappa, C. B., & Joshi, J. D. (2018). Data mining for prevention of crimes. In International Conference on Human Interface and the Management of Information (pp. 705-717). Springer, Cham.

Sumithra, R., & Paul, S. (2010). Using distributed apriori association rule and classical apriori mining algorithms for grid based knowledge discovery. 2010 Second International conference on Computing, Communication and Networking Technologies, (pp. 1-5).

Travassos, G., Gurov, D., & Amaral, E. (2002). Introdução à engenharia de software experimental.

Varde, A., Takahashi, M., Rundensteiner, E., Ward, M., Maniruzzaman, M., & Jr., R. (2004). Apriori Algorithm and Game-of-Life for Predictive Analysis in Materials Science. International Journal of Knowledge-Based and Intelligent Engineering Systems, 8, 213-228.

Wu, J., He, Z., Gu, F., Liu, X., Zhou, J., & Yang, C. (2016). Computing exact permutation p-values for association rules. Information Sciences, 346, 146-162.

Yadav, S., Timbadia, M., Yadav, A., Vishwakarma, R., & Yadav, N. (2017). Crime pattern detection, analysis & prediction. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) (Vol. 1, pp. 225-230). IEEE.

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Published

19/11/2020

How to Cite

GOMES, W. F.; COLAÇO JÚNIOR, M.; PRADO, K. H. de J. An Initial Experiment on Associations between Crimes in Brazil. Research, Society and Development, [S. l.], v. 9, n. 11, p. e41791110078, 2020. DOI: 10.33448/rsd-v9i11.10078. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/10078. Acesso em: 15 jan. 2025.

Issue

Vol. 9 No. 11

Section

Exact and Earth Sciences

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Copyright (c) 2020 Wesckley Faria Gomes; Methanias Colaço Júnior; Kleber Henrique de Jesus Prado

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