A Data-Driven Approach for Spatio-Temporal Crime Predictions in Smart Cities

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

doi:10.1109/smartcomp.2018.00069

Cited by 42 publications

(22 citation statements)

References 15 publications

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“…A spatio-temporal approach has been considered by [11] and [12]. [11] used a regression model (polynomial regression, support vector regression, and auto-regressive regression) for predicting crime activity in the city of Chicago using social information sources from network analytic techniques.…”

Section: Discussionmentioning

confidence: 99%

“…By comparing the models, the support vector regression provided better performances in terms of RMSE. [12] presented the design and implementation of an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas, and to reliably forecast crime trends in each region. The results (in terms of MAE) showed that crimes decrease for smaller areas.…”

Section: Discussionmentioning

confidence: 99%

“…With the growth of technology, the local authorities and police departments had to deal with large amounts of data in order to understand criminal patterns. In this context, [12] presented a spatio-temporal crime prediction approach based on spatial auto-regressive models to automatically detect high-risk crime regions in urban areas, and to reliably forecast crime trends in each region. They used crime data in Chicago over the period 2014-2016, and obtained a maximum mean absolute prediction error (MAPE) between 8.7% and 11.9%.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

et al. 2020

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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%

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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“…Ferreira et al (2012) combined cluster analysis and GWR to identify areas with the highest crime rates. Catlett et al (2018) defined an approach based on spatial analysis and auto-regressive models for automatically detect high-risk crime regions in urban areas. Walker et al (2014) studied the effect that natural disasters have on the space-time behaviour of crime patterns using local-level data and highlighted aspects of change in patterns of crime as a response to Hurricane Wilma in Miami, Florida, 2005.…”

Section: Introductionmentioning

confidence: 99%

Investigating Temporal and Spatial Effects of Urban Planning Variables on Crime Rate: A GWR and Ols Based Approach

Irandegani

Mohammadi

Taleai

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 ABSTRACT:Spatial, temporal, environmental and urban planning variables are some factors effected crime occurrence as a social undesirable phenomenon. In this paper, the effect of urban planning variables (including land use diversity and police station area) and temporal parameters (including daily and weekly time windows) on crime incidence were investigated by doing some spatial-temporal analysis. To tackle this, at first, in order to determine crime clusters, DBSCAN algorithm is utilized. Then by using the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) the global effect of urban planning variables on crime spatial clusters are modelled and compared. Finally, the OLS method is used to investigate the effects of temporal variables on crime spatial clusters. The results demonstrate that there is a negative correlation between crime clusters and the police station coverage, and a positive relationship between crime clusters and land-use diversity. Furthermore, increasing the number of police stations in districts with high land-use diversity can result in reducing the crime rate. Also, the result of our temporal analysis shows that the high crime rate was happened at noon and on weekdays. Therefore, increasing the number of police officers at noon and on weekdays in the districts with high land-use diversity is recommended.

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A Data-Driven Approach for Spatio-Temporal Crime Predictions in Smart Cities

Cited by 42 publications

References 15 publications

Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

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

Investigating Temporal and Spatial Effects of Urban Planning Variables on Crime Rate: A GWR and Ols Based Approach

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