Crime event prediction with dynamic features

Rumi, Shakila Khan; Deng, Ke; Salim, Flora D.

doi:10.1140/epjds/s13688-018-0171-7

Cited by 52 publications

(30 citation statements)

References 43 publications

(97 reference statements)

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“…(5) will approach Eqn. (3) where the algorithm of Gauss-Newton is applied. As an alternative, if μ has values that are above zero, the steepest descent method is applied by approximating Eqn.…”

Section: E Nonlinear Autoregressive With Exogenous (External) Input mentioning

confidence: 99%

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Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

et al. 2020

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In the criminology area, to detain the serial criminal, the forthcoming serial crime time, distance, and criminal's biography are essential keys. The main concern of this study is on the upcoming serial crime distance, time, and suspect biographies such as age and nationality. In conjunction with having time delays, the dynamic classifier, like Time Delay Neural Network (TDNN) utilized to perform nonlinear techniques-based predictions. The TDNN classifier system, like Back Propagation Through Time (BPTT) and Nonlinear Autoregressive with Exogenous Input (NARX) are two prominent examples. However, BPTT and NARX techniques are unable to identify the dynamic system by using single-activation functions due to producing lower accuracy. Hence, during the training phase, the direct minimization of the TDNN error can further enhance the single activation function. Thus, this work introduces an enhanced NARX (eNARX) model based on the proposed activation functions of SiRBF via fusion of two functions of the hyperbolic tangent (Tansig) and Radial Basis Function (RBF), in the same hidden layer. If a fusion of activation functions can affect the TDNN error minimization, then fusing of the Tansig and RBF functions can produce a precise prediction for crime spatiotemporal. To evaluate the proposed technique and compared it with existing NARX and BPTT, we utilized five time-series datasets, namely, Dow Jones Index, Monthly River flow in cubic meters per second, Daily temperature, and UKM-PDRM datasets namely, "Suspect & Capture" and "Crime Plotting." The analysis of the results demonstrated that the proposed eNARX produce higher accuracy in comparison to other techniques of NARX and BPTT. Consequently, the proposed technique provides more effective results for the prediction of commercial serial crime.

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Section: E Nonlinear Autoregressive With Exogenous (External) Input mentioning

confidence: 99%

“…One of the main factors to prevent serial criminals in the criminology area is forecasting the next serial crime incident. The significant aspect of crime prediction is location or scene [3]. Location is a de facto of the criminal incidence, which is related to regulation, offender, and target [4].…”

Section: Introductionmentioning

confidence: 99%

Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

et al. 2020

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“…In contrast, locations with crimes are not well forecasted. Some authors try to ameliorate the negative-positive ratio between crime and no crime cells, by adjusting the weight of hotspots and cold spots (Yu et al 2011), or change the training set, while the test set keeps its original, real data (Rumi et al 2018). Another dependency is the different kinds of aggregation that take place during modelling by time, space, or crime types attributes.…”

Section: Dependencies and Limitationsmentioning

confidence: 99%

A systematic review on spatial crime forecasting

et al. 2020

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Background: Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects. Methods: We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics. Results: The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1-Score. Finally, the most common validation approach was the train-test split while other approaches include the cross-validation, the leave one out, and the rolling horizon. Limitations: Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems. Conclusions: There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real-time prediction. Implications: Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66). To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study's key data items.

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“…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%

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

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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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Crime event prediction with dynamic features

Cited by 52 publications

References 43 publications

Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

A systematic review on spatial crime forecasting

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

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