Crime rate prediction in the urban environment using social factors

Ingilevich, Varvara; Ivanov, Sergey V.

doi:10.1016/j.procs.2018.08.261

Cited by 39 publications

(23 citation statements)

References 18 publications

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“…With the advancement in technology, criminal behavior is becoming more and more channeled and complex [4]. For crime control, the nature of crime must be understood [18]. The spatial analysis helps to decode the spatial behavior of criminal activities [18] and assist law enforcement in making predictions about future crimes that may occur [1].…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Analysis of Web News Archives for Crime Prediction

et al. 2020

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In today’s world, security is the most prominent aspect which has been given higher priority. Despite the rapid growth and usage of digital devices, lucrative measurement of crimes in under-developing countries is still challenging. In this work, unstructural crime data (900 records) from the news archives of the previous eight years were extracted to predict the behavior of criminals’ networks and transform it into useful information using natural language processing (NLP). To estimate the next move of criminals in Pakistan, we performed hotspot-based spatial analysis. Later, this information is fed to two different classifiers for possible identification and prediction. We achieved the maximum accuracy of 92% using K-Nearest Neighbor (KNN) and 62% using the Random Forest algorithm. In terms of crimes, the results showed that the most prevalent crime events are robberies. Thus, the usage of digital information archives, spatial analysis, and machine learning techniques can open new ways of handling a peaceful and sustainable society in eradicating crimes for countries having paucity of financial resources.

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Section: Related Workmentioning

confidence: 99%

Spatiotemporal Analysis of Web News Archives for Crime Prediction

et al. 2020

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“…Xiangyu et al utilized spatiotemporal patterns in urban data in one borough in a city, and then leveraged the transfer learning techniques to reinforce the crime prediction of other boroughs. A novel transfer learning framework is used to integrate these features and model patio-temporal patterns for crime prediction [15]. The Indian numerical plates [14] were introduced using fuzzy based approach and 95% accuracy was achieved.…”

Section: Literature Summerymentioning

confidence: 99%

An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach

et al. 2021

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Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem in India. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB. The SVM algorithm is applied to achieve domain-specific configurations compared with another machine learning model J48, SMO Naïve byes bagging and, the Random Forest. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved 99.5% classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence. The results also proved that any empirical data on crime, is compatible with criminological theories. The proposed approach also found to be useful for predicting possible crime predictions. And suggest that the prediction accuracy of the stacking ensemble model is higher than that of the individual classifier.

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“…The research results can help the police find the best locations for the police station in the city and identify strategies for reducing the crime rate. [15] Deepika K.K 1, *, Smitha Vinod 2 : This paper suggests a strategy for the identification of crime in India using data mining methods. The method consists of Pre-processing, clustering, classification and visualization of the data.…”

Section: IImentioning

confidence: 99%

A Review on Resolving Crime with Prediction

Yadav¹

2021

IJRASET

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Crime data analysts can assist law enforcement officers in accelerating the crime prediction process with the rising advent of computerized systems. This survey paper, therefore, presents a description of the tools and strategies used in the study and forecast of crime data. Crime forecasts are a way to exploit and reduce future crime by anticipating when future crime will happen. Crime prediction relates historical data and predicts the next crime, based on place, time, day, season, and year after analyzing data.This paper provides a fair review of numerous discoveries and predictions of potential crime

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Crime rate prediction in the urban environment using social factors

Cited by 39 publications

References 18 publications

Spatiotemporal Analysis of Web News Archives for Crime Prediction

Spatiotemporal Analysis of Web News Archives for Crime Prediction

An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach

A Review on Resolving Crime with Prediction

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