Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes

Detalhes bibliográficos
Ano de defesa: 2020
Autor(a) principal: Queiroz Neto, José Florencio de
Orientador(a): Santos, Emanuele Marques dos
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/53759
Resumo: In recent years, violence has increased considerably in the world. In Ceará, a Brazilian state member, the homicide rate went from 16 per 100,000 inhabitants in 2000 to 37 per 100,000 inhabitants in 2016. With the popularization of spatial databases and Geographic Information Systems (GIS), police departments worldwide started to create various types of crime maps, generated with different techniques, to analyze and fight crime. One of the types of crime maps is the hotspot map, which helps decision-makers to identify high-risk areas and allocate resources more efficiently. The analysis of crime data is usually a complex operation, and a target of Visual Analytics (VA) systems, which are systems that aim to increase human analytical reasoning through visual and interactive interfaces. The need for interactivity requires that VA systems should include high-performance as one of the main conditions. In this thesis, we propose MSKDE - Marching Squares Kernel Density Estimation, a technique to generate fast and accurate hotspot maps. We describe the method and demonstrate its superior qualities through careful comparison with the Kernel Density Estimation (KDE), widely used to generate density maps. Another contribution of this thesis aims to help police departments in their planning activities. Professionals and researchers agree that tracking crime over time and identifying its geographic patterns is vital information for efficient resource planning. To help to perform these activities, frequently, police departments have access to systems that are too complicated and overly technical, leading to modest use at last. We collaborated with domain experts from police departments in Brazil and the United States to recognize and characterize five domain tasks inherent to the activity of tracking crime and resource allocation planning. All domain tasks are related to hotspot analysis and policing, one of the prominent approaches to fight crime. To facilitate the performing of the domain tasks, we proposed SHOC, The One-Shot Comparison Tool, a technique that allows immediate spatial comparison of crime density surfaces. We included SHOC into a VA system, CrimeWatcher, which allows users to perform filtering operations and visualize maps and data smoothly. CrimeWatcher strives for simplicity and will enable users, even without technical knowledge, to perform all tasks, annotate, save, and share analyzes. We also demonstrated that CrimeWatcher and SHOC effectively support the completion of domain tasks in two different real-world case studies.
id UFC-7_633860d5ab07609bc8b7867640298d90
oai_identifier_str oai:repositorio.ufc.br:riufc/53759
network_acronym_str UFC-7
network_name_str Repositório Institucional da Universidade Federal do Ceará (UFC)
repository_id_str
spelling Queiroz Neto, José Florencio deVidal, Creto AugustoSantos, Emanuele Marques dos2020-09-01T18:26:47Z2020-09-01T18:26:47Z2020QUEIROZ NETO, José Florencio de. A visual analytics approach for geocoded crime data. 2020. 100 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal do Ceará, Fortaleza, 2020.http://www.repositorio.ufc.br/handle/riufc/53759In recent years, violence has increased considerably in the world. In Ceará, a Brazilian state member, the homicide rate went from 16 per 100,000 inhabitants in 2000 to 37 per 100,000 inhabitants in 2016. With the popularization of spatial databases and Geographic Information Systems (GIS), police departments worldwide started to create various types of crime maps, generated with different techniques, to analyze and fight crime. One of the types of crime maps is the hotspot map, which helps decision-makers to identify high-risk areas and allocate resources more efficiently. The analysis of crime data is usually a complex operation, and a target of Visual Analytics (VA) systems, which are systems that aim to increase human analytical reasoning through visual and interactive interfaces. The need for interactivity requires that VA systems should include high-performance as one of the main conditions. In this thesis, we propose MSKDE - Marching Squares Kernel Density Estimation, a technique to generate fast and accurate hotspot maps. We describe the method and demonstrate its superior qualities through careful comparison with the Kernel Density Estimation (KDE), widely used to generate density maps. Another contribution of this thesis aims to help police departments in their planning activities. Professionals and researchers agree that tracking crime over time and identifying its geographic patterns is vital information for efficient resource planning. To help to perform these activities, frequently, police departments have access to systems that are too complicated and overly technical, leading to modest use at last. We collaborated with domain experts from police departments in Brazil and the United States to recognize and characterize five domain tasks inherent to the activity of tracking crime and resource allocation planning. All domain tasks are related to hotspot analysis and policing, one of the prominent approaches to fight crime. To facilitate the performing of the domain tasks, we proposed SHOC, The One-Shot Comparison Tool, a technique that allows immediate spatial comparison of crime density surfaces. We included SHOC into a VA system, CrimeWatcher, which allows users to perform filtering operations and visualize maps and data smoothly. CrimeWatcher strives for simplicity and will enable users, even without technical knowledge, to perform all tasks, annotate, save, and share analyzes. We also demonstrated that CrimeWatcher and SHOC effectively support the completion of domain tasks in two different real-world case studies.Nos últimos anos, a violência aumentou consideravelmente no mundo. No Ceará, a taxa de homicídios passou de 16 por 100.000 habitantes em 2000 para 37 por 100.000 habitantes em 2016. Com a popularização de bancos de dados espaciais e Sistemas de Informação Geográfica (SIG), os departamentos de polícia começaram a criar vários tipos de mapas de crimes, gerados com diferentes técnicas, para analisar e combater o crime. Um dos tipos de mapas de crimes é o mapa de hotspots, que ajuda na identificação de áreas de alto risco e na alocação de recursos com mais eficiência. A análise de dados de crimes geralmente é uma operação complexa e indicada para os sistemas Visual Analytics (VA), que são sistemas que visam aumentar o raciocínio analítico humano por meio de interfaces visuais e interativas. A necessidade de interatividade exige que os sistemas VA incluam alto desempenho como uma das principais condições. Nesta tese, propomos o MSKDE - Marching Squares Kernel Density Estimation, uma técnica para gerar mapas de hotspot rápidos e precisos. Descrevemos o método e demonstramos suas qualidades superiores por meio de uma comparação cuidadosa com a Estimativa de Densidade de Kernel (KDE), amplamente usada para gerar mapas de densidade. Outra contribuição desta tese visa ajudar os departamentos de polícia em suas atividades de planejamento. Profissionais e pesquisadores concordam que rastrear o crime ao longo do tempo e identificar seus padrões geográficos são vitais para o planejamento eficiente dos recursos. Para ajudar a realizar essas atividades, freqüentemente, os departamentos de polícia têm acesso a sistemas muito complicados e excessivamente técnicos. Colaboramos com especialistas em departamentos de polícia do Brasil e dos Estados Unidos para identificar e caracterizar cinco tarefas inerentes à atividade de rastreamento de crimes e planejamento de alocação de recursos. Todas as tarefas estão relacionadas à análise de hotspots, um dos métodos mais importantes para combater o crime. Para facilitar a execução das tarefas, propusemos o SHOC, The One-Shot Comparison Tool, uma técnica que permite a comparação espacial imediata das superfícies de densidade de crimes. Incluímos o SHOC em um sistema VA, o CrimeWatcher, que permite aos usuários realizar operações de filtragem e visualizar mapas e dados. O CrimeWatcher se destaca pela simplicidade e permite que usuários, mesmo sem conhecimento técnico, executem as tarefas, anotem, salvem e compartilhem análises. Também demonstramos que o CrimeWatcher e o SHOC suportam efetivamente a conclusão de tarefas em dois estudos de caso reais.Visualização analíticaVisualização de dadosComputação gráficaUma Abordagem em Visualização Analítica para Dados Geocodificados de CrimesA visual analytics approach for geocoded crime datainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisengreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFCinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.ufc.br/bitstream/riufc/53759/4/license.txt8a4605be74aa9ea9d79846c1fba20a33MD54ORIGINAL2020_tese_jfqueirozneto.pdf2020_tese_jfqueirozneto.pdfapplication/pdf23165620http://repositorio.ufc.br/bitstream/riufc/53759/3/2020_tese_jfqueirozneto.pdf8703c691b195fa1dd971753ebecdc937MD53riufc/537592020-09-01 15:26:47.413oai:repositorio.ufc.br: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Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2020-09-01T18:26:47Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false
dc.title.pt_BR.fl_str_mv Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes
dc.title.en.pt_BR.fl_str_mv A visual analytics approach for geocoded crime data
title Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes
spellingShingle Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes
Queiroz Neto, José Florencio de
Visualização analítica
Visualização de dados
Computação gráfica
title_short Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes
title_full Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes
title_fullStr Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes
title_full_unstemmed Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes
title_sort Uma Abordagem em Visualização Analítica para Dados Geocodificados de Crimes
author Queiroz Neto, José Florencio de
author_facet Queiroz Neto, José Florencio de
author_role author
dc.contributor.co-advisor.none.fl_str_mv Vidal, Creto Augusto
dc.contributor.author.fl_str_mv Queiroz Neto, José Florencio de
dc.contributor.advisor1.fl_str_mv Santos, Emanuele Marques dos
contributor_str_mv Santos, Emanuele Marques dos
dc.subject.por.fl_str_mv Visualização analítica
Visualização de dados
Computação gráfica
topic Visualização analítica
Visualização de dados
Computação gráfica
description In recent years, violence has increased considerably in the world. In Ceará, a Brazilian state member, the homicide rate went from 16 per 100,000 inhabitants in 2000 to 37 per 100,000 inhabitants in 2016. With the popularization of spatial databases and Geographic Information Systems (GIS), police departments worldwide started to create various types of crime maps, generated with different techniques, to analyze and fight crime. One of the types of crime maps is the hotspot map, which helps decision-makers to identify high-risk areas and allocate resources more efficiently. The analysis of crime data is usually a complex operation, and a target of Visual Analytics (VA) systems, which are systems that aim to increase human analytical reasoning through visual and interactive interfaces. The need for interactivity requires that VA systems should include high-performance as one of the main conditions. In this thesis, we propose MSKDE - Marching Squares Kernel Density Estimation, a technique to generate fast and accurate hotspot maps. We describe the method and demonstrate its superior qualities through careful comparison with the Kernel Density Estimation (KDE), widely used to generate density maps. Another contribution of this thesis aims to help police departments in their planning activities. Professionals and researchers agree that tracking crime over time and identifying its geographic patterns is vital information for efficient resource planning. To help to perform these activities, frequently, police departments have access to systems that are too complicated and overly technical, leading to modest use at last. We collaborated with domain experts from police departments in Brazil and the United States to recognize and characterize five domain tasks inherent to the activity of tracking crime and resource allocation planning. All domain tasks are related to hotspot analysis and policing, one of the prominent approaches to fight crime. To facilitate the performing of the domain tasks, we proposed SHOC, The One-Shot Comparison Tool, a technique that allows immediate spatial comparison of crime density surfaces. We included SHOC into a VA system, CrimeWatcher, which allows users to perform filtering operations and visualize maps and data smoothly. CrimeWatcher strives for simplicity and will enable users, even without technical knowledge, to perform all tasks, annotate, save, and share analyzes. We also demonstrated that CrimeWatcher and SHOC effectively support the completion of domain tasks in two different real-world case studies.
publishDate 2020
dc.date.accessioned.fl_str_mv 2020-09-01T18:26:47Z
dc.date.available.fl_str_mv 2020-09-01T18:26:47Z
dc.date.issued.fl_str_mv 2020
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv QUEIROZ NETO, José Florencio de. A visual analytics approach for geocoded crime data. 2020. 100 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal do Ceará, Fortaleza, 2020.
dc.identifier.uri.fl_str_mv http://www.repositorio.ufc.br/handle/riufc/53759
identifier_str_mv QUEIROZ NETO, José Florencio de. A visual analytics approach for geocoded crime data. 2020. 100 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal do Ceará, Fortaleza, 2020.
url http://www.repositorio.ufc.br/handle/riufc/53759
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Federal do Ceará (UFC)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Repositório Institucional da Universidade Federal do Ceará (UFC)
collection Repositório Institucional da Universidade Federal do Ceará (UFC)
bitstream.url.fl_str_mv http://repositorio.ufc.br/bitstream/riufc/53759/4/license.txt
http://repositorio.ufc.br/bitstream/riufc/53759/3/2020_tese_jfqueirozneto.pdf
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
8703c691b195fa1dd971753ebecdc937
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv bu@ufc.br || repositorio@ufc.br
_version_ 1847793072536027136