Data Science applied to criminal analysis based on Minas Gerais open government data
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/10044 |
Resumo: | Context: Crime is a common and complex social problem that affects a nation's quality of life, economic growth and reputation. Governments and society in general have had enormous problems caused by this phenomenon. Each year, governments spend millions of dollars fighting violence and, consequently, crime prevention and control are issues of great concern to public security agencies. Objective: To apply fundamentals of Data Science and provide an automated model, constantly updated, to analyze open government data related to crimes occurred in Minas Gerais. Method: We have performed an experiment to discover associations between municipalities, Integrated Public Security Regions (IPSRs), crimes, robbery targets, and theft targets. Additionally, we have developed rankings with the most dangerous municipalities. Results: From a general point of view, with scores for crimes, Belo Horizonte, Confins and Contagem were always among the five most dangerous. In addition, it became evident that there are dependencies between: crimes and municipalities, crimes and IPSRs, robbery targets and municipalities, and robbery targets and IPSRs. Conclusion: Data Science enables the execution of more accurate and faster diagnoses, helping strategic planning and decision making in Public Security. With some peculiarities and going beyond homicides, Minas Gerais partially follows the national trend of having lower crime rates in areas around regions with greater economic development. |
id |
UNIFEI_2e17b787a63295fe5017d0248ff18b3c |
---|---|
oai_identifier_str |
oai:ojs.pkp.sfu.ca:article/10044 |
network_acronym_str |
UNIFEI |
network_name_str |
Research, Society and Development |
repository_id_str |
|
spelling |
Data Science applied to criminal analysis based on Minas Gerais open government dataCiencia de Datos aplicada al análisis criminal basado en datos abiertos del gobierno de Minas GeraisData Science aplicada à análise criminal baseada nos dados abertos governamentais de Minas GeraisAnálisis CriminalCiencia de DatosDatos abiertos gubernamentales.Análise CriminalCiência de DadosDados abertos governamentais.Criminal AnalysisData ScienceOpen government data.Context: Crime is a common and complex social problem that affects a nation's quality of life, economic growth and reputation. Governments and society in general have had enormous problems caused by this phenomenon. Each year, governments spend millions of dollars fighting violence and, consequently, crime prevention and control are issues of great concern to public security agencies. Objective: To apply fundamentals of Data Science and provide an automated model, constantly updated, to analyze open government data related to crimes occurred in Minas Gerais. Method: We have performed an experiment to discover associations between municipalities, Integrated Public Security Regions (IPSRs), crimes, robbery targets, and theft targets. Additionally, we have developed rankings with the most dangerous municipalities. Results: From a general point of view, with scores for crimes, Belo Horizonte, Confins and Contagem were always among the five most dangerous. In addition, it became evident that there are dependencies between: crimes and municipalities, crimes and IPSRs, robbery targets and municipalities, and robbery targets and IPSRs. Conclusion: Data Science enables the execution of more accurate and faster diagnoses, helping strategic planning and decision making in Public Security. With some peculiarities and going beyond homicides, Minas Gerais partially follows the national trend of having lower crime rates in areas around regions with greater economic development.Contexto: El crimen es un problema social común y complejo que afecta la calidad de vida, el crecimiento económico y la reputación de una nación. Los gobiernos y la sociedad en general han tenido enormes problemas provocados por este fenómeno. Cada año, los gobiernos gastan millones de dólares en la lucha contra la violencia y, en consecuencia, la prevención y el control del delito son temas de gran preocupación para las agencias de seguridad pública. Objetivo: Aplicar los fundamentos de la ciencia de datos y proporcionar un modelo automatizado, constantemente actualizado, para analizar datos gubernamentales abiertos relacionados con delitos ocurridos en Minas Gerais. Método: Se llevó a cabo un experimento para descubrir asociaciones entre municipios, Regiones de Seguridad Pública Integrada (RISPs), delitos y robos y objetivos de hurto. Además, se desarrollaron rankings con los municipios más peligrosos. Resultados: Desde el punto de vista general, considerando los delitos, los municipios de Belo Horizonte, Confins y Contagem estuvieron constantemente entre los cinco más peligrosos. Además, se hizo evidente que existen dependencias entre: delitos y municipios, delitos y RISPs, objetivos de robo y municipios, y objetivos de robo y RISPs. Conclusión: La ciencia de datos permite la ejecución de diagnósticos más precisos y rápidos, lo que ayuda a la planificación estratégica y la toma de decisiones en seguridad pública. Con algunas peculiaridades y más allá de los homicidios, Minas Gerais sigue parcialmente la tendencia nacional de tener menores índices de criminalidad en áreas alrededor de las regiones con mayor desarrollo económico.Contexto: Crime é um problema social comum e complexo, que afeta a qualidade de vida, o crescimento econômico e a reputação de uma nação. Governantes e a sociedade em geral têm tido enormes problemas causados por esse fenômeno. A cada ano, os governos gastam milhões de dólares combatendo a violência e, consequentemente, a prevenção e o controle do crime são questões de grande preocupação para as agências de segurança pública. Objetivo: Aplicar fundamentos de Data Science e fornecer um modelo automatizado, constantemente atualizado, para analisar dados abertos governamentais relacionados aos crimes ocorridos em Minas Gerais. Método: Um experimento foi executado para descoberta de associações entre os municípios, Regiões Integradas de Segurança Pública (RISPs), crimes e alvos de roubo e furto. Adicionalmente, foram desenvolvidos rankings com os municípios mais perigosos. Resultados: Do ponto de vista geral, com ponderações para os crimes, os munícipios de Belo Horizonte, Confins e Contagem estiveram, constantemente, entre os cinco mais perigosos. Além disso, ficou evidenciado que existem dependências entre: crimes e municípios, crimes e RISPs, alvos de roubo e municípios, e alvos de roubo e RISPs. Conclusão: A Ciência de Dados possibilita a execução de diagnósticos mais precisos e mais céleres, auxiliando o planejamento estratégico e a tomada de decisão em Segurança Pública. Com algumas particularidades e indo além dos homicídios, Minas Gerais segue parcialmente a tendência nacional de ter índices de criminalidade mais baixos em áreas ao redor de regiões com maior desenvolvimento econômico.Research, Society and Development2020-11-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1004410.33448/rsd-v9i11.10044Research, Society and Development; Vol. 9 No. 11; e36391110044Research, Society and Development; Vol. 9 Núm. 11; e36391110044Research, Society and Development; v. 9 n. 11; e363911100442525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/10044/9542Copyright (c) 2020 Kleber Henrique de Jesus Prado; Methanias Colaço Júniorhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessPrado, Kleber Henrique de JesusColaço Júnior, Methanias2020-12-10T23:37:57Zoai:ojs.pkp.sfu.ca:article/10044Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:32:11.353479Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Data Science applied to criminal analysis based on Minas Gerais open government data Ciencia de Datos aplicada al análisis criminal basado en datos abiertos del gobierno de Minas Gerais Data Science aplicada à análise criminal baseada nos dados abertos governamentais de Minas Gerais |
title |
Data Science applied to criminal analysis based on Minas Gerais open government data |
spellingShingle |
Data Science applied to criminal analysis based on Minas Gerais open government data Prado, Kleber Henrique de Jesus Análisis Criminal Ciencia de Datos Datos abiertos gubernamentales. Análise Criminal Ciência de Dados Dados abertos governamentais. Criminal Analysis Data Science Open government data. |
title_short |
Data Science applied to criminal analysis based on Minas Gerais open government data |
title_full |
Data Science applied to criminal analysis based on Minas Gerais open government data |
title_fullStr |
Data Science applied to criminal analysis based on Minas Gerais open government data |
title_full_unstemmed |
Data Science applied to criminal analysis based on Minas Gerais open government data |
title_sort |
Data Science applied to criminal analysis based on Minas Gerais open government data |
author |
Prado, Kleber Henrique de Jesus |
author_facet |
Prado, Kleber Henrique de Jesus Colaço Júnior, Methanias |
author_role |
author |
author2 |
Colaço Júnior, Methanias |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Prado, Kleber Henrique de Jesus Colaço Júnior, Methanias |
dc.subject.por.fl_str_mv |
Análisis Criminal Ciencia de Datos Datos abiertos gubernamentales. Análise Criminal Ciência de Dados Dados abertos governamentais. Criminal Analysis Data Science Open government data. |
topic |
Análisis Criminal Ciencia de Datos Datos abiertos gubernamentales. Análise Criminal Ciência de Dados Dados abertos governamentais. Criminal Analysis Data Science Open government data. |
description |
Context: Crime is a common and complex social problem that affects a nation's quality of life, economic growth and reputation. Governments and society in general have had enormous problems caused by this phenomenon. Each year, governments spend millions of dollars fighting violence and, consequently, crime prevention and control are issues of great concern to public security agencies. Objective: To apply fundamentals of Data Science and provide an automated model, constantly updated, to analyze open government data related to crimes occurred in Minas Gerais. Method: We have performed an experiment to discover associations between municipalities, Integrated Public Security Regions (IPSRs), crimes, robbery targets, and theft targets. Additionally, we have developed rankings with the most dangerous municipalities. Results: From a general point of view, with scores for crimes, Belo Horizonte, Confins and Contagem were always among the five most dangerous. In addition, it became evident that there are dependencies between: crimes and municipalities, crimes and IPSRs, robbery targets and municipalities, and robbery targets and IPSRs. Conclusion: Data Science enables the execution of more accurate and faster diagnoses, helping strategic planning and decision making in Public Security. With some peculiarities and going beyond homicides, Minas Gerais partially follows the national trend of having lower crime rates in areas around regions with greater economic development. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-17 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/10044 10.33448/rsd-v9i11.10044 |
url |
https://rsdjournal.org/index.php/rsd/article/view/10044 |
identifier_str_mv |
10.33448/rsd-v9i11.10044 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/10044/9542 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Kleber Henrique de Jesus Prado; Methanias Colaço Júnior https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Kleber Henrique de Jesus Prado; Methanias Colaço Júnior https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 9 No. 11; e36391110044 Research, Society and Development; Vol. 9 Núm. 11; e36391110044 Research, Society and Development; v. 9 n. 11; e36391110044 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
_version_ |
1797052663752294400 |