Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments

Catlett, Charlie; Cesario, Eugenio; Talia, Domenico; Vinci, Andrea

doi:10.1016/j.pmcj.2019.01.003

Cited by 83 publications

(48 citation statements)

References 10 publications

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“…2 and calibrations are performed until the required literature is returned. It was revealed during the study selection process that some studies are duplicated; they were first included in the conference proceedings [105] and then were published by a journal as extended versions [112]. Furthermore, quality assessment criteria III-F are defined by following the guidelines provided in [25], [26].…”

Section: ) Validation Of Systematic Literature Reviewmentioning

confidence: 99%

“…The majority of the literature does not have a dataset description and not even any link to access and compare results, so it is hard to conclude the prominent approaches they have mentioned. A few papers use [5], [71], [105], [112] same publically available crime data and compare their results in a particular setting. However, researchers such as Charlie et al [104] and Shakila [100] use different datasets to check the effectiveness of their proposed approach.…”

Section: ) Superior Spatio-temporal Crime Hotspot Detection Approachmentioning

confidence: 99%

“…Spatial and temporal information addition in datasets found to be more effective for crime prediction. Charlie et al [112] pointed out the DBSCAN approach to be superior among all the crime detection approaches as they compared with state-of-the-art techniques. They used publically available Chicago crime dataset that is used in many crime hotspot detection papers.…”

Section: ) Superior Spatio-temporal Crime Hotspot Detection Approachmentioning

confidence: 99%

“…Charlie et al [112] DBSCAN Accuracy, RMSE used in 45% of the studies. Support and confidence measure is used in numerous pattern matching techniques and reported around 20% of the studies.…”

Section: Accuracy Rmsementioning

confidence: 99%

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Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review

et al. 2020

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The primary objective of this study is to accumulate, summarize, and evaluate the state-ofthe-art for spatio-temporal crime hotspot detection and prediction techniques by conducting a systematic literature review (SLR). The authors were unable to find a comprehensive study on crime hotspot detection and prediction while conducting this SLR. Therefore, to the best of author's knowledge, this study is the premier attempt to critically analyze the existing literature along with presenting potential challenges faced by current crime hotspot detection and prediction systems. The SLR is conducted by thoroughly consulting top five scientific databases (such as IEEE, Science Direct, Springer, Scopus, and ACM), and synthesized 49 different studies on crime hotspot detection and prediction after critical review. This study unfolds the following major aspects: 1) the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; 2) the utility of time series analysis techniques and deep learning techniques in crime trend prediction; 3) the inclusion of spatial and temporal information in crime datasets making the crime prediction systems more accurate and reliable; 4) the potential challenges faced by the state-of-the-art techniques and the future research directions. Moreover, the SLR aims to provide a core foundation for the research on spatio-temporal crime prediction applications while highlighting several challenges related to the accuracy of crime hotspot detection and prediction applications.

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Section: ) Validation Of Systematic Literature Reviewmentioning

confidence: 99%

Section: ) Superior Spatio-temporal Crime Hotspot Detection Approachmentioning

confidence: 99%

Section: ) Superior Spatio-temporal Crime Hotspot Detection Approachmentioning

confidence: 99%

Section: Accuracy Rmsementioning

confidence: 99%

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Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review

et al. 2020

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“…This paper [3] proposes a spatio temporal predictive model which finds crime dense regions using k-means and DB scan clustering algorithms, extracts crime predictors using Seasonal Auto Regressive Moving Average model(SARIMA).The results and its implications are presented using appropriate graphs.…”

Section: Spatio-temporal Crime Prediction In Ruralmentioning

confidence: 99%

Descriptive Analytics on Crime in India using Clustering Techniques

2020

IJITEE

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Understanding the perspectives of crime happenings by exploiting crime data helps early detection and prevention of crimes. Ruling Government and Policing systems are aware the importance of realising the changing aspects of crime. As technology is advanced, there are many ways to comprehend the trends and the patterns of crime activities. The paper presents a hybrid model using AGNES and K-means clustering algorithms to focus on different views and representation of crime in India and aims to recognize the crime types that cluster the regions of India. Accuracy of model is measured using log loss and the patterns and trends in crime are presented.

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