Crime pattern detection, analysis &amp; prediction

Yadav, Sunil; Timbadia, Meet; Yadav, Ajit Kumar; Vishwakarma, Rohit; Yadav, Nikhilesh

doi:10.1109/iceca.2017.8203676

Cited by 86 publications

(22 citation statements)

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“…Using data mining and statistical techniques, new algorithms and systems have been developed along with new types of data. For instance, classification and statistical models are applied for mining of crime patterns and crime prediction [17], [18], where transfer learning has been employed to exploit spatio-temporal patterns in New York city [19]. Wu et al [20] developed a system to automatically collect crime-logged data for mining of crime patterns, in order to achieve more effective crime prevention in/around a university campus.…”

Section: B Crime Data Mining Visualization and Trends Forecastingmentioning

confidence: 99%

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

et al. 2019

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Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-theart data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process. INDEX TERMS Big data analytics (BDA), data mining, data visualization, neural network, time series forecasting.

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Section: B Crime Data Mining Visualization and Trends Forecastingmentioning

confidence: 99%

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

et al. 2019

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“…Genetic Algorithm [12]: Genetic algorithm is used for understanding obliged and unconstrained modifications. In every iteration, it gives points and is based on biological choice process.…”

Section: Technique Used For Detection Of Various Crimesmentioning

confidence: 99%

Theoretical and Empirical Analysis of Crime Data

Mudgal

Punj

Pillai

2021

JWE

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Crime is one of the biggest and dominating problems in today’s world and it is not only harmful to the person involved but also to the community and government. Due to escalation in crime frequency, there is a need for a system that can detect and predict crimes. This paper describes the summary of the different methods and techniques used to identify, analyze and predict upcoming and present crimes. This paper shows, how data mining techniques can be used to detect and predict crime using association mining rule, k-means clustering, decision tree, artificial neural networks and deep learning methods are also explained. Most of the researches are currently working on forecasting the occurrence of future crime. There is a need for approaches that can work on real-time crime prediction at high speed and accuracy. In this paper, a model has been proposed that can work on real-time crime prediction by recognizing human actions.

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“…O trabalho apresentado em (Yadav, Timbadia, Yadav, Vishwakarma, & Yadav, 2017) utilizou dados abertos retirados do portal online da Índia, listando vários tipos de crimes, tais como assassinatos, sequestros, roubos e estupros, entre outros. Nessa pesquisa, foram aplicados diferentes algoritmos de análise de dados que inferiram as conexões entre os crimes e seus padrões, entre os quais estavam Apriori, K-means, Naive Bayes, por meio das ferramentas Weka e linguagem de programação R. O algoritmo Apriori mostrou associação entre pessoas presas e soltas no mesmo ano, indicando que quanto maior o número de pessoas presas, mais pessoas são soltas e, portanto, mais pessoas acabam sendo inocentadas.…”

Section: Trabalhos Relacionadosunclassified

Um Experimento Inicial sobre Associações entre os Crimes Ocorridos no Brasil

Gomes

Júnior

Prado

2020

RSD

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Contexto: A criminalidade tem sido um problema ao redor do mundo, causando danos às sociedades. Educação, pobreza, emprego e clima são alguns fatores que afetam a taxa de criminalidade, levando as autoridades a gastar, anualmente, milhões com ações de combate à violência e planos estratégicos de prevenção e redução da criminalidade. Objetivo: Aplicar conceitos de Data Science para análise de dados governamentais relacionados a crimes no Brasil. Método: Uso de mineração de dados, especificamente regras de associação (RA), em um experimento controlado, para detecção de padrões entre os tipos de crimes, como também entre os tipos de crime e meses do ano. Resultados: No contexto das associações entre crimes, os estados com regras mais interessantes foram: Bahia, com 15 associações, São Paulo, com 12, Goiás, 11, e Paraná, com 9. Destaque para a associação “Latrocínio Roubo de Carga”, encontrada para o Estado da Bahia, a qual atingiu uma confiança de 99% (0.99). Já no âmbito das associações entre crimes e meses do ano, avultaram-se Paraíba, com 2 associações, Maranhão, Rondônia e São Paulo, com 1 associação cada. Destaque para regra “Dezembro Roubo de Veículo”, encontrada para o Estado de São Paulo, que alcançou uma confiança de 84% (0,84). Conclusão: Os resultados expostos nesta pesquisa auxiliam analistas criminais no processo de tomada de decisão para prevenção e redução da criminalidade no país, uma vez que podem permitir a inibição de crimes que são antecedentes de outras ocorrências dentro do mesmo estado ou de crimes que ocorrem num mesmo período.

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Crime pattern detection, analysis & prediction

Cited by 86 publications

References 1 publication

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Theoretical and Empirical Analysis of Crime Data

Um Experimento Inicial sobre Associações entre os Crimes Ocorridos no Brasil

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