CrimAnalyzer: Understanding Crime Patterns in São Paulo

García, Germain; Silveira, Jaqueline; Poco, Jorge; Paiva, Afonso; Nery, Marcelo Batista; Silva, Cláudio T.; Adorno, Sérgio; Nonato, Luís Gustavo

doi:10.1109/tvcg.2019.2947515

Cited by 24 publications

(18 citation statements)

References 49 publications

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“…Visual analytics also supports in-depth later analysis for public security, in areas such as crime analysis [96,[106][107][108], police resource allocation [32,109,110], fire station selection [48], crisis management [111], disease analysis [95,106,112,113], and surveillance video inspection [114]. For example, given historic crime data, crime hotspots with frequent occurrences of crime can be visually determined [106][107][108], and urban experts can take informed action such as appropriate policing.…”

Section: Human-created Eventsmentioning

confidence: 99%

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A survey of urban visual analytics: Advances and future directions

et al. 2022

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Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.

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Section: Human-created Eventsmentioning

confidence: 99%

“…[106] for (C2), c IEEE 2010; Ref. [108] for (C3), c IEEE 2019. provide spatial visualizations. Such visualizations enable urban analysis to be performed in a geographic context, e.g., by presenting data distributions and anomalies in geographic space.…”

Section: Spatial Visualizationmentioning

confidence: 99%

A survey of urban visual analytics: Advances and future directions

et al. 2022

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“…O uso de identificac ¸ão de hotspots é uma técnica bastante utilizada para identificar padrões de criminalidade. Uma Non-negative matrix factorization -NMF foi utilizada por [Garcıa et al 2019] que construíram uma ferramenta de análise visual com o objetivo de identificar hotspots associados ao comportamento dos crimes ao longo do tempo na Cidade de São Paulo. Com o objetivo detalhar a concentrac ¸ão de ocorrências criminais e analisar os seus padrões espacias e temporais em uma escala no nível de rua, foi implementado no trabalho de [Garcia-Zanabria et al 2020] a ferramenta Mirante, que fornece um conjunto de componentes visuais interativos permitindo ao usuário explorar heatmaps e séries temporais.…”

Section: Ferramentas Para Analisar E Explorar Padrões De Crimesunclassified

“…Ferramentas computacionais, sobretudo as que usam modelo de aprendizado baseado em dados, podem direcionar esforc ¸os para onde realmente é necessário. Muitas ferramentas e técnicas foram propostas nesta área, apresentando hotspots de crimes, evoluc ¸ão do crime no tempo, visuais interativos e modelos espaciais preditivos, [Garcıa et al 2019], [Garcia-Zanabria et al 2020], [ToppiReddy et al 2018]. Porém, há pouca ênfase em monitoramento de mudanc ¸as de padrão de crime, o que seria um elemento fundamental para a inteligência policial.…”

Section: Introduc ¸ãOunclassified

CityGuardian: Uma ferramenta para monitorar mudanças em padrões de criminalidade nas Cidades Inteligentes

Leal

Gomes-Jr

2022

Anais Estendidos Do XVIII Simpósio Brasileiro De Sistemas De Informação (SBSI Estendido 2022)

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Para combater os altos índices de criminalidade, Cidades Inteligentes recorrem a ferramentas de monitoramento e suporte para subsidiar ações de prevenção. Analisar onde e quando um crime ocorreu é o primeiro passo para entender os padrões de criminalidade. Mudanças repentinas como a pandemia da covid-19, mostram a importância de entender um fato ocorrido para se tomar uma decisão assertiva. Dessa forma, apresentamos a CityGuardian, uma ferramenta web, que monitora as ocorrências criminais e alerta ao identificar alterações de padrões no espaço-tempo. Além disso permitindo explorar em sua interface gráfica os dados que causaram essas alterações.

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“…Regarding multifaceted time-varying data, works range from focus + context [ 26 ] weather analysis [ 27 , 28 , 29 ] to multiplayer game analysis [ 30 ]. In the context of time-varying data defined on graphs, visualization tools to analyze mobility [ 31 ], dynamic graphs [ 32 ], and crime data [ 33 ] have been proposed. The methods above are only examples of visualization methods for time-varying data and a more comprehensive overview can be found in a number of surveys [ 21 , 34 , 35 ]…”

Section: Related Workmentioning

confidence: 99%

NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks

Contreras

Parnandi

Coelho

et al. 2021

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A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.

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CrimAnalyzer: Understanding Crime Patterns in São Paulo

Cited by 24 publications

References 49 publications

A survey of urban visual analytics: Advances and future directions

A survey of urban visual analytics: Advances and future directions

CityGuardian: Uma ferramenta para monitorar mudanças em padrões de criminalidade nas Cidades Inteligentes

NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks

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