Application of spatio-temporal scan statistics in cases of intentional homicides in northern Brazil
Conteúdo do artigo principal
Resumo
The crime of intentional homicide in Brazil is worrying. In the northern region, this type of crime has been growing since 2020. In this sense, we decided to apply Kuldorff’s prospective space-time scan statistics in order to identify emerging clusters. We conclude that there are two emerging clusters of high relative risk (Amazonas and Pará) that require rapid intervention and two clusters of low relative risk (Acre, Roraima, Amazonas, Amapá, and Pará) that do not require urgent intervention. Some characteristics of these two clusters are presented: radius, population, relative risk, likelihood ratio.
Detalhes do artigo
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Referências
Coelho, D. S. C., Cunha, A. d. S., Alves, H. & Guedes, E. P. Metodologia para a pré-seleção dos municípios participantes do Programa Nacional de Enfrentamento de Homicídios e Roubos (2021). http://dx.doi.org/10.38116/ntdiest55
Costa, M. A., Assunçao, R. M. & Kulldorff, M. Constrained spanning tree algorithms for irregularly-shaped spatial clustering. Computational Statistics & Data Analysis 56, 1771–1783 (2012). http://dx.doi.org/10.1016/j.csda.2011.11.001
Dim, E. E. Experiences of physical and psychological violence against male victims in Canada: A qualitative study. International journal of offender therapy and comparative criminology 65, 1029–1054 (2021). https://doi.org/10.1177/0306624x20911898
Duczmal, L., Kulldorff, M. & Huang, L. Evaluation of spatial scan statistics for irregularly shaped clusters. Journal of Computational and Graphical Statistics 15, 428–442 (2006). http://dx.doi.org/10.1198/106186006X112396
Kulldorff, M. SaTScanTM user guide for version 9.6. 2018. Department of Medicine, Harvard Medical School: Boston, MA 2120 (2018).
Kulldorff, M. Prospective time periodic geographical disease surveillance using a scan statistic. Journal of the Royal Statistical Society: Series A (Statistics in Society) 164, 61–72 (2001). https://doi.org/10.1111/1467-985X.00186
Kulldorff, M., Athas, W. F., Feurer, E. J., Miller, B. A. & Key, C. R. Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. American journal of public health 88, 1377–1380 (1998).
Kulldorff, M. & Nagarwalla, N. Spatial disease clusters: detection and inference. Statistics in medicine 14, 799–810 (1995). https://doi.org/10.1002/sim.4780140809
Leite, F. M. C., Mascarello, K. C., Almeida, A. P. S. C., Fávero, J. L., Santos, A. S. d., Silva, I. C. M. d. &Wehrmeister, F. C. Analysis of the female mortality trend due to assault in Brazil, States and Regions. Ciência & Saúde Coletiva 22, 2971–2978 (2017). https://doi.org/10.1590/1413-81232017229.25702016
Liang, S., Han, D. & Yang, Y. Cluster validity index for irregular clustering results. Applied Soft Computing 95, 106–583 (2020). https://doi.org/10.1016/j.asoc.2020.106583
Mohammadi, A. Spatiotemporal patterns of homicide rates in Tehran metropolitan area, Iran. Spatial Information Research, 1–9 (2023). https://doi.org/10.1007/s41324-023-00506-4
Qian, H. et al. Detecting spatial-temporal cluster of hand foot and mouth disease in Beijing, China, 2009-2014. BMC infectious diseases 16, 1–13 (2016). https://doi.org/10.1186/s12879-016-1547-6
Silva, C. C. d., Souza, K. O. C. d., Paz, W. S. d., Santos, A. P. S., Melo, L. R. S. d., Sousa, Á. F. L. d., Araújo, D. d. C. & Santos, A. D. d. Modelagem espacial da mortalidade por homicídios na Região Nordeste do Brasil. Revista Brasileira de Enfermagem 76, e20220182 (2023). https://doi.org/10.1590/0034-7167-2022-0182pt
Steelesmith, D. L. & Lindstrom, M. R. e. a. Spatiotemporal Patterns of Deaths of Despair Across the US, 2000-2019. American journal of preventive medicine (2023). https://doi.org/10.1016/j.amepre.2023.02.020
Tavares, R., Catalan, V. D. B., Romano, P. M. d. M. & Melo, E. M. Homicídios e vulnerabilidade social. Ciência & Saúde Coletiva 21, 923–934 (2016). https://doi.org/10.1590/1413-81232015213.12362015
Uittenbogaard, A. & Ceccato, V. Space-time clusters of crime in Stockholm, Sweden. Rev. Eur. Stud. (2012). http://dx.doi.org/10.5539/res.v4n5p148
Vieira, C. P., Oliveira, A. M., Rodas, L. A. C., Dibo, M. R., Guirado, M. M. & Chiaravalloti Neto, F. Temporal, spatial and spatiotemporal analysis of the occurrence of visceral leishmaniasis in humans in the City of Birigui, State of São Paulo, from 1999 to 2012. Revista da Sociedade Brasileira de Medicina Tropical 47, 350–358 (2014). https://doi.org/10.1590/0037-8682-0047-2014
Xia, J. et al. Spatial, temporal, and spatiotemporal analysis of malaria in Hubei Province, China from 2004–2011. Malaria journal 14, 1–10 (2015). https://doi.org/10.1186/s12936-015-0650-2
Yiannakoulias, N., Rosychuk, R. J. & Hodgson, J. Adaptations for finding irregularly shaped disease clusters. International Journal of Health Geographics 6, 1–16 (2007). https://doi.org/10.1186/1476-072X-6-28