Ferramenta para análises descritivas e preditivas em dados criminais: Um estudo de caso em Minas Gerais
Resumo
This article examines the underutilization of detailed criminal data, collaborating with the Military Police of Minas Gerais, Brazil. We propose a new methodology, materialized in a tool, that is able to transform raw data into strategic information for public security decision-making. The tool evaluation unfolds in three phases: characterizing the data, a descriptive analysis of a real case study, and a predictive analysis. This work highlights the untapped potential in detailed criminal data, emphasizing the pivotal role of precise analysis in deciphering complex dynamics. Collaborating with law enforcement aims to bridge the gap between data abundance and actionable insights for effective public security strategies.
Palavras-chave:
Modelos Descritivos e Preditivos de Crimes
Referências
Yan Andrade, Matheus Pimenta, Gabriel Amarante, Antônio Hot Faria, Marcelo Vilas-Boas, João Paulo da Silva, Felipe Rocha, Jamicel da Silva, Wagner Meira Jr., George Teodoro, Leonardo Rocha, and Renato Ferreira. 2023. A descriptive and predictive analysis tool for criminal data: A case study from Brazil. In Computational Science and Its Applications - ICCSA 2024 (Lecture Notes in Computer Science). Springer.
Fateha Khanam Bappee, Amílcar Soares Júnior, and Stan Matwin. 2018. Predicting crime using spatial features. In Canadian Conference on Artificial Intelligence. Springer.
Charlie Catlett, Eugenio Cesario, Domenico Talia, and Andrea Vinci. 2018. A data-driven approach for spatio-temporal crime predictions in smart cities. In 2018 IEEE International Conference on Smart Computing.
Eugenio Cesario, Charlie Catlett, and Domenico Talia. 2016. Forecasting crimes using autoregressive models. In 14th Intl Conf on Dependable, Autonomic and Secure Computing. IEEE.
Charlie S Marzan, Maria Jeseca C Baculo, Remedios de Dios Bulos, and Conrado Ruiz Jr. 2017. Time series analysis and crime pattern forecasting of city crime data. In Proceedings of the 1st International Conference on Algorithms, Computing and Systems. 113–118.
Colleen McCue. 2014. Data mining and predictive analysis: Intelligence gathering and crime analysis. Butterworth-Heinemann.
Rafael Prieto Curiel. 2023. Weekly crime concentration. Journal of quantitative criminology 39, 1 (2023), 97–124.
Rebecca J Walter, Marie Skubak Tillyer, and Arthur Acolin. 2023. Spatiotemporal crime patterns across six US cities: Analyzing stability and change in clusters and outliers. Journal of Quantitative Criminology 39, 4 (2023), 951–974.
G Peter Zhang. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (2003), 159–175.
Zhengyi Zhou and David S. Matteson. 2015. Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach. In Proceedings of the 21th ACM SIGKDD. DOI: 10.1145/2783258.2788570
Fateha Khanam Bappee, Amílcar Soares Júnior, and Stan Matwin. 2018. Predicting crime using spatial features. In Canadian Conference on Artificial Intelligence. Springer.
Charlie Catlett, Eugenio Cesario, Domenico Talia, and Andrea Vinci. 2018. A data-driven approach for spatio-temporal crime predictions in smart cities. In 2018 IEEE International Conference on Smart Computing.
Eugenio Cesario, Charlie Catlett, and Domenico Talia. 2016. Forecasting crimes using autoregressive models. In 14th Intl Conf on Dependable, Autonomic and Secure Computing. IEEE.
Charlie S Marzan, Maria Jeseca C Baculo, Remedios de Dios Bulos, and Conrado Ruiz Jr. 2017. Time series analysis and crime pattern forecasting of city crime data. In Proceedings of the 1st International Conference on Algorithms, Computing and Systems. 113–118.
Colleen McCue. 2014. Data mining and predictive analysis: Intelligence gathering and crime analysis. Butterworth-Heinemann.
Rafael Prieto Curiel. 2023. Weekly crime concentration. Journal of quantitative criminology 39, 1 (2023), 97–124.
Rebecca J Walter, Marie Skubak Tillyer, and Arthur Acolin. 2023. Spatiotemporal crime patterns across six US cities: Analyzing stability and change in clusters and outliers. Journal of Quantitative Criminology 39, 4 (2023), 951–974.
G Peter Zhang. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (2003), 159–175.
Zhengyi Zhou and David S. Matteson. 2015. Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach. In Proceedings of the 21th ACM SIGKDD. DOI: 10.1145/2783258.2788570
Publicado
14/10/2024
Como Citar
ANDRADE, Yan; AMARANTE, Gabrielle; PIMENTA, Matheus; MEIRA JR., Wagner; TEODORO, George; ROCHA, Leonardo; FERREIRA, Renato.
Ferramenta para análises descritivas e preditivas em dados criminais: Um estudo de caso em Minas Gerais. In: WORKSHOP DE FERRAMENTAS E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 30. , 2024, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 83-86.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2024.242364.
