Analysis and prediction of crime patterns using big data

Kumar, Ritesh; Nagpal, Bharti

doi:10.1007/s41870-018-0260-7

Cited by 20 publications

(10 citation statements)

References 16 publications

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“…Structured data defining an explicitly specified cluster of data play an important role in data analysis at all times due to the convenience of their classification (Kumar & Nagpal, 2019). Most of the biometric verifications used in definitions in the field of security are based on structured data.…”

Section: Data Mining Methods and Modelsmentioning

confidence: 99%

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Güzel¹

2021

JPLC

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Young adults engage in a disproportionately high rate of problematic behaviors such as risky sexual activities, academic dishonesty, and substance abuse. In order to understand why this occurs, two lesser-known constructs related to risktaking behavior were investigated in this study: differential identification, which has yet to be empirically studied in the context of emerging adults, and criminogenic thinking, which has only been evaluated in this context to a minimal degree. To bridge the gap between these two constructs, motivation was hypothesized to be a moderator in each of these respective relationships. Data from 309 emerging adults were recruited via Amazon's Mechanical Turk (MTurk) to complete the survey materials online. The analysis consisted of a hierarchical multiple linear regression. Results indicated that motivation did not moderate either relationship (i.e., differential identification and risk-taking behavior and criminogenic thinking and risk-taking behavior). However, a main effect was found between one aspect of differential identification and risk-taking behavior. No other main effects were found in this study. Potential explanations for the results of this study are discussed, as well as implications and directions for future research.

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Section: Data Mining Methods and Modelsmentioning

confidence: 99%

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Güzel¹

2021

JPLC

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“…Finally, Naïve Bayes model was constructed, but did not provide accurate prediction of crimes. 6 While another study showed that machine learning techniques can predict the behavior of criminals based on their criminal records. For this purpose, the criminal records as a time-series data set were used to construct the deep neural network (DNN).…”

Section: Related Workmentioning

confidence: 99%

“…Data cleaning was performed to remove irrelevant and redundant data. Finally, Naïve Bayes model was constructed, but did not provide accurate prediction of crimes 6 . While another study showed that machine learning techniques can predict the behavior of criminals based on their criminal records.…”

Section: Related Workmentioning

confidence: 99%

A model for predicting crimes using big data and neural‐fuzzy networks

Jaber

Sheibani

Shakeri

2022

Concurrency and Computation

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Big data analytics is important for identifying and analyzing different patterns, relations, and trends within a large volume of data such as crime prediction. In this paper, we propose a new model for predicting crimes using neural‐fuzzy model. For this purpose, time of occurrence and longitude and latitude of the crime scene are used. We use k‐means algorithms to cluster crime records based on their locations. The crime pattern of each cluster was learnt by its own neural‐fuzzy network, in addition using shuffled frog leaping algorithm to determine the optimal cluster radius. The accuracy and Mean Absolute Error and Root Mean Squared Error of this method were compared with: Gaussian Process Regression, Classification and Regression Tree, and Artificial Neural Network. The experiment results showed that the proposed model was the most accurate model and had the least error to predict the occurrence of crimes so can be used as an effective tool in real‐world applications.

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“…In terms of prediction content, the prediction of crimes is roughly subcategorized as follows: re-offending prediction [11,12], victim prediction [13], offender prediction [14,15] crime pattern prediction [16,17] and crime hot spots prediction [18]. Crime hot spots prediction can be classified into temporal prediction [19], spatial prediction [20] and spatial-temporal prediction [21,22].…”

Section: Related Workmentioning

confidence: 99%

Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN

Han

et al. 2020

IEEE Access

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Urbanization has been speeding up social and economic transformations in urban communities, the smallest social units in a city. However, urbanization brings challenges to urban management and security. Therefore, a system of risk prediction of crimes may be essential to crime prevention and control in urban communities and its system improvement. To tackle crime-related problems in urban communities, this paper proposes a model of daily crime prediction by combining Long Short-Term Memory Network (LSTM) and Spatial-Temporal Graph Convolutional Network (ST-GCN) to automatically and effectively detect the high-risk areas in a city. Topological maps of urban communities carry the dataset in the model, which mainly includes two modules-spatial-temporal features extraction module and temporal feature extraction module-to extract the factors of theft crimes collectively. We have performed the experimental evaluation of the existing crime data from Chicago, America. The results show that the integrated model demonstrates positive performance in predicting the number of crimes within the sliding time range.

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Analysis and prediction of crime patterns using big data

Cited by 20 publications

References 16 publications

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

A model for predicting crimes using big data and neural‐fuzzy networks

Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN

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