Mirante: a visualization tool for analyzing urban crimes

Detalhes bibliográficos
Autor(a) principal: Garcia-Zanabria, Germain
Data de Publicação: 2020
Outros Autores: Gomez-Nieto, Erick, Silveira, Jaqueline, Poco, Jorge, Nery, Marcelo Batista, Adorno, Sergio, Nonato, Luis Gustavo
Tipo de documento: Relatório
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/31627
Resumo: Visualization assisted crime analysis tools used by public security agencies are usually designed to explore large urban areas, relying on grid-based heatmaps to reveal spatial crime distribution in whole districts, regions, and neighborhoods. Therefore, those tools can hardly identify micro-scale patterns closely related to crime opportunity, whose understanding is fundamental to the planning of preventive actions. Enabling a combined analysis of spatial patterns and their evolution over time is another challenge faced by most crime analysis tools. In this paper, we present Mirante, a crime mapping visualization system that allows spatiotemporal analysis of crime patterns in a street-level scale. In contrast to conventional tools, Mirante builds upon street-level heatmaps and other visualization resources that enable spatial and temporal pattern analysis, uncovering fine-scale crime hotspots, seasonality, and dynamics over time. Mirante has been developed in close collaboration with domain experts, following rigid requirements as scalability and versatile to be implemented in large and medium-sized cities. We demonstrate the usefulness of Mirante throughout case studies run by domain experts using real data sets from cities with different characteristics. With the help of Mirante, the experts were capable of diagnosing how crime evolves in specific regions of the cities while still being able to raise hypotheses about why certain types of crime show up.
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spelling Garcia-Zanabria, GermainGomez-Nieto, ErickSilveira, JaquelinePoco, JorgeNery, Marcelo BatistaAdorno, SergioNonato, Luis GustavoEscolas::EMApDemais unidades::RPCA2022-02-21T21:36:05Z2022-02-21T21:36:05Z2020https://hdl.handle.net/10438/31627Visualization assisted crime analysis tools used by public security agencies are usually designed to explore large urban areas, relying on grid-based heatmaps to reveal spatial crime distribution in whole districts, regions, and neighborhoods. Therefore, those tools can hardly identify micro-scale patterns closely related to crime opportunity, whose understanding is fundamental to the planning of preventive actions. Enabling a combined analysis of spatial patterns and their evolution over time is another challenge faced by most crime analysis tools. In this paper, we present Mirante, a crime mapping visualization system that allows spatiotemporal analysis of crime patterns in a street-level scale. In contrast to conventional tools, Mirante builds upon street-level heatmaps and other visualization resources that enable spatial and temporal pattern analysis, uncovering fine-scale crime hotspots, seasonality, and dynamics over time. Mirante has been developed in close collaboration with domain experts, following rigid requirements as scalability and versatile to be implemented in large and medium-sized cities. We demonstrate the usefulness of Mirante throughout case studies run by domain experts using real data sets from cities with different characteristics. With the help of Mirante, the experts were capable of diagnosing how crime evolves in specific regions of the cities while still being able to raise hypotheses about why certain types of crime show up.engViolênciaCrimes urbanosMatemáticaTecnologiaVisualização da informaçãoCriminalidade urbanaViolênciaMirante: a visualization tool for analyzing urban crimesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/reportreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessModelos matemáticos e computacionais de otimização de estratégias de redução dos níveis de violência no BrasilModelos matemáticos e computacionais de otimização de estratégias de redução dos níveis de violência no BrasilProjetos de Pesquisa AplicadaORIGINALMirante.pdfMirante.pdfPDFapplication/pdf7911878https://repositorio.fgv.br/bitstreams/473d3461-922e-4fce-a420-7b37d28673b3/download34fdb5ea7dc386efbd82c17c88905b53MD51TEXTMirante.pdf.txtMirante.pdf.txtExtracted texttext/plain46369https://repositorio.fgv.br/bitstreams/9b03568a-4d0e-4f5a-b314-49f3130cd366/downloaddb26737f31700f3a467a18e6c95e271fMD54THUMBNAILMirante.pdf.jpgMirante.pdf.jpgGenerated Thumbnailimage/jpeg6297https://repositorio.fgv.br/bitstreams/575c19f1-1dd1-4bc9-a865-e7d7b709532c/download5570b44b1214bf0c984f45b0d9b9b2e3MD5510438/316272023-11-25 10:17:01.658open.accessoai:repositorio.fgv.br:10438/31627https://repositorio.fgv.brRepositório InstitucionalPRIhttp://bibliotecadigital.fgv.br/dspace-oai/requestopendoar:39742023-11-25T10:17:01Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV)false
dc.title.eng.fl_str_mv Mirante: a visualization tool for analyzing urban crimes
title Mirante: a visualization tool for analyzing urban crimes
spellingShingle Mirante: a visualization tool for analyzing urban crimes
Garcia-Zanabria, Germain
Violência
Crimes urbanos
Matemática
Tecnologia
Visualização da informação
Criminalidade urbana
Violência
title_short Mirante: a visualization tool for analyzing urban crimes
title_full Mirante: a visualization tool for analyzing urban crimes
title_fullStr Mirante: a visualization tool for analyzing urban crimes
title_full_unstemmed Mirante: a visualization tool for analyzing urban crimes
title_sort Mirante: a visualization tool for analyzing urban crimes
author Garcia-Zanabria, Germain
author_facet Garcia-Zanabria, Germain
Gomez-Nieto, Erick
Silveira, Jaqueline
Poco, Jorge
Nery, Marcelo Batista
Adorno, Sergio
Nonato, Luis Gustavo
author_role author
author2 Gomez-Nieto, Erick
Silveira, Jaqueline
Poco, Jorge
Nery, Marcelo Batista
Adorno, Sergio
Nonato, Luis Gustavo
author2_role author
author
author
author
author
author
dc.contributor.unidadefgv.none.fl_str_mv Escolas::EMAp
Demais unidades::RPCA
dc.contributor.author.fl_str_mv Garcia-Zanabria, Germain
Gomez-Nieto, Erick
Silveira, Jaqueline
Poco, Jorge
Nery, Marcelo Batista
Adorno, Sergio
Nonato, Luis Gustavo
dc.subject.por.fl_str_mv Violência
Crimes urbanos
topic Violência
Crimes urbanos
Matemática
Tecnologia
Visualização da informação
Criminalidade urbana
Violência
dc.subject.area.por.fl_str_mv Matemática
Tecnologia
dc.subject.bibliodata.por.fl_str_mv Visualização da informação
Criminalidade urbana
Violência
description Visualization assisted crime analysis tools used by public security agencies are usually designed to explore large urban areas, relying on grid-based heatmaps to reveal spatial crime distribution in whole districts, regions, and neighborhoods. Therefore, those tools can hardly identify micro-scale patterns closely related to crime opportunity, whose understanding is fundamental to the planning of preventive actions. Enabling a combined analysis of spatial patterns and their evolution over time is another challenge faced by most crime analysis tools. In this paper, we present Mirante, a crime mapping visualization system that allows spatiotemporal analysis of crime patterns in a street-level scale. In contrast to conventional tools, Mirante builds upon street-level heatmaps and other visualization resources that enable spatial and temporal pattern analysis, uncovering fine-scale crime hotspots, seasonality, and dynamics over time. Mirante has been developed in close collaboration with domain experts, following rigid requirements as scalability and versatile to be implemented in large and medium-sized cities. We demonstrate the usefulness of Mirante throughout case studies run by domain experts using real data sets from cities with different characteristics. With the help of Mirante, the experts were capable of diagnosing how crime evolves in specific regions of the cities while still being able to raise hypotheses about why certain types of crime show up.
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