Mirante: a visualization tool for analyzing urban crimes
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
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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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. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020 |
dc.date.accessioned.fl_str_mv |
2022-02-21T21:36:05Z |
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2022-02-21T21:36:05Z |
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eng |
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