Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | preprint |
Idioma: | por |
Título da fonte: | SciELO Preprints |
Texto Completo: | https://preprints.scielo.org/index.php/scielo/preprint/view/6550 |
Resumo: | The study of the spatial distribution of Brazilian prisons is one of the ways to understand the mass incarceration phenomenon. By using the Penitentiary Census of the Ministry of Justice, 2014, we analyse punitive patterns around the country. The results show the existence of five punitive spatial clusters: two of which are prominent, in the Southeast region and the other centred in the Northeast region (between the states of Pernambuco and Ceará). Methodologically, we structured models for point patterns considering the geographic location (latitude and longitude), in addition, we introduced into the models the prisons’ characteristics, such as occupancy rate and prison capacity. As a result, it is possible to observe a statistical regularity that shows a differential punitive pattern in the São Paulo state. There are many units with large capacities, higher than other states' prisons. The statistical inference was made under the Bayesian paradigm, which allows characterizing the uncertainties of the models in a probabilistic way and circumventing the problems of scientific decision-making based on the p-value. The present paper is the first one to identify punitive groups in Brazil and to statistically verify the existence of different spatial patterns in São Paulo’s prisons, contributing to the debate on punitive policies in the country. |
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Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonaldCompreendendo o padrão espacial punitivo no Brasil e o McDonald prisional de São Paulopuniçãoviolênciametodologiaprisãoestatística espacialpunishmentviolencemethodologyprisonspatial patternThe study of the spatial distribution of Brazilian prisons is one of the ways to understand the mass incarceration phenomenon. By using the Penitentiary Census of the Ministry of Justice, 2014, we analyse punitive patterns around the country. The results show the existence of five punitive spatial clusters: two of which are prominent, in the Southeast region and the other centred in the Northeast region (between the states of Pernambuco and Ceará). Methodologically, we structured models for point patterns considering the geographic location (latitude and longitude), in addition, we introduced into the models the prisons’ characteristics, such as occupancy rate and prison capacity. As a result, it is possible to observe a statistical regularity that shows a differential punitive pattern in the São Paulo state. There are many units with large capacities, higher than other states' prisons. The statistical inference was made under the Bayesian paradigm, which allows characterizing the uncertainties of the models in a probabilistic way and circumventing the problems of scientific decision-making based on the p-value. The present paper is the first one to identify punitive groups in Brazil and to statistically verify the existence of different spatial patterns in São Paulo’s prisons, contributing to the debate on punitive policies in the country.Uma das maneiras de compreender o fenômeno do encarceramento em massa no Brasil é através do estudo da distribuição das unidades prisionais em seu território. A partir do Censo Penitenciário realizado pelo Ministério da Justiça, em 2014, foi feita uma análise de estatística espacial para identificar os padrões punitivos no país. Os resultados mostram a existência de cinco agrupamentos punitivos, sendo dois principais: um na região sudeste, centrado no estado de São Paulo, e outro no Nordeste, com centro entre os estados de Pernambuco e Ceará. Metodologicamente, modelos estruturados para padrões de pontos foram expandidos introduzindo características do fenômeno, além das geográficas (latitude e longitude), tais como: taxa de ocupação e capacidade dos presídios. Como resultado, foi possível observar uma regularidade estatística que mostra um padrão punitivo diferenciado em São Paulo. Observa-se grande quantidade de unidades com capacidade superior à de seus pares na região. A inferência estatística foi feita sob o paradigma bayesiano que permite caracterizar as incertezas dos modelos de forma probabilística e contornar os problemas de tomada de decisão científica baseada no p-valor. Este trabalho é o primeiro a identificar os agrupamentos punitivos no país e verificar estatisticamente a existência de padrões diferenciados nas unidades prisionais de São Paulo, contribuindo para o debate sobre o punitivismo no Brasil.SciELO PreprintsSciELO PreprintsSciELO Preprints2023-10-10info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/655010.1590/SciELOPreprints.6550porhttps://preprints.scielo.org/index.php/scielo/article/view/6550/12541Copyright (c) 2023 Vinicius Pinheiro Israel, Marina Silva Paez, Rebecca de Oliveira Souzahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessIsrael, Vinicius PinheiroPaez, Marina SilvaSouza, Rebecca de Oliveirareponame:SciELO Preprintsinstname:Scientific Electronic Library Online (SCIELO)instacron:SCI2023-09-15T17:23:08Zoai:ops.preprints.scielo.org:preprint/6550Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2023-09-15T17:23:08SciELO Preprints - Scientific Electronic Library Online (SCIELO)false |
dc.title.none.fl_str_mv |
Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald Compreendendo o padrão espacial punitivo no Brasil e o McDonald prisional de São Paulo |
title |
Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald |
spellingShingle |
Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald Israel, Vinicius Pinheiro punição violência metodologia prisão estatística espacial punishment violence methodology prison spatial pattern |
title_short |
Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald |
title_full |
Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald |
title_fullStr |
Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald |
title_full_unstemmed |
Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald |
title_sort |
Understanding the punitive spatial pattern in Brazil and São Paulo's prison McDonald |
author |
Israel, Vinicius Pinheiro |
author_facet |
Israel, Vinicius Pinheiro Paez, Marina Silva Souza, Rebecca de Oliveira |
author_role |
author |
author2 |
Paez, Marina Silva Souza, Rebecca de Oliveira |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Israel, Vinicius Pinheiro Paez, Marina Silva Souza, Rebecca de Oliveira |
dc.subject.por.fl_str_mv |
punição violência metodologia prisão estatística espacial punishment violence methodology prison spatial pattern |
topic |
punição violência metodologia prisão estatística espacial punishment violence methodology prison spatial pattern |
description |
The study of the spatial distribution of Brazilian prisons is one of the ways to understand the mass incarceration phenomenon. By using the Penitentiary Census of the Ministry of Justice, 2014, we analyse punitive patterns around the country. The results show the existence of five punitive spatial clusters: two of which are prominent, in the Southeast region and the other centred in the Northeast region (between the states of Pernambuco and Ceará). Methodologically, we structured models for point patterns considering the geographic location (latitude and longitude), in addition, we introduced into the models the prisons’ characteristics, such as occupancy rate and prison capacity. As a result, it is possible to observe a statistical regularity that shows a differential punitive pattern in the São Paulo state. There are many units with large capacities, higher than other states' prisons. The statistical inference was made under the Bayesian paradigm, which allows characterizing the uncertainties of the models in a probabilistic way and circumventing the problems of scientific decision-making based on the p-value. The present paper is the first one to identify punitive groups in Brazil and to statistically verify the existence of different spatial patterns in São Paulo’s prisons, contributing to the debate on punitive policies in the country. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-10 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/preprint info:eu-repo/semantics/publishedVersion |
format |
preprint |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/preprint/view/6550 10.1590/SciELOPreprints.6550 |
url |
https://preprints.scielo.org/index.php/scielo/preprint/view/6550 |
identifier_str_mv |
10.1590/SciELOPreprints.6550 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/article/view/6550/12541 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Vinicius Pinheiro Israel, Marina Silva Paez, Rebecca de Oliveira Souza https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Vinicius Pinheiro Israel, Marina Silva Paez, Rebecca de Oliveira Souza 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 |
SciELO Preprints SciELO Preprints SciELO Preprints |
publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
dc.source.none.fl_str_mv |
reponame:SciELO Preprints instname:Scientific Electronic Library Online (SCIELO) instacron:SCI |
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Scientific Electronic Library Online (SCIELO) |
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SCI |
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SCI |
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SciELO Preprints |
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SciELO Preprints |
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SciELO Preprints - Scientific Electronic Library Online (SCIELO) |
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1797047812384358400 |