Spatio-temporal prediction of crimes using network analytic approach

Dash, Saroj; Safro, Ilya; Srinivasamurthy, Ravisutha Sakrepatna

doi:10.1109/bigdata.2018.8622041

Cited by 20 publications

(7 citation statements)

References 22 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. By comparing the models, the support vector regression provided better performances in terms of RMSE.…”

Section: Discussionmentioning

confidence: 99%

“…Different optimization algorithms (such as Adagrad, RMSProp, SGDNesterov optimizers, AdaDelta and Adam) were compared, and Adam resulted with the smaller loss function. In [11] the city of Chicago is divided into 77 communities, where for each community they have social information such as the number of police stations in a sector, number of schools, bookstores, type of crime and calls to 311. This information was then used for running three regression models: a polynomial regression, a self-regressive model, and the support vector regression (SVR).…”

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: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…However, the research methodology had some limitations as the dataset was a snapshot of a specific timestamp. Therefore, the dataset was not fully representative of the real-world criminal networks, which evolve over time [19], [20].…”

Section: Related Workmentioning

confidence: 99%

Situation-Aware Deep Reinforcement Learning Link Prediction Model for Evolving Criminal Networks

et al. 2020

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Evidently, criminal network activities have shown an increasing trend in terms of complexity and frequency, particularly with the advent of social media and modern telecommunication systems. In these circumstances, law enforcement agencies have to be armed with advance criminal network analysis (CNA) tools capable of uncovering with speed, probable key hidden relationships (links/edges) and players (nodes) in order to anticipate, undermine and cripple organised crime syndicates and activities. The development of link prediction models for network orientated domains is based on Social Network Analysis (SNA) methods and models. The key objective of this research is to develop a link prediction model that incorporates a fusion of metadata (i.e. environment data sources such as arrest warrants, judicial judgement, wiretap records and police station proximity) with a time-evolving criminal dataset in order to be aware of real-world situations to improve the quality of link prediction. Based on the review of related work, most of the models are constructed by leveraging on classical machine learning (ML) techniques such as support vector machine (SVM) without metadata fusion. The problem with the use of classical ML techniques is the lack of available domain dataset which is sufficiently large for training purpose. Compared to sociaI network, criminal network dataset by nature tends to relatively much smaller. In view of this, deep reinforcement learning (DRL) technique which could improve the training of models with the self-generated dataset is leveraged upon to construct the model. In this research, a purely time-evolving DRL model (TDRL-CNA) without metadata fusion is designed as a baseline for comparison with the metadata fusion model (FDRL-CNA). The experimental results show that the predictive accuracy of new and recurrent links by the FDRL-CNA model is higher than the baseline TDRL-CNA model that does not factor data fusion from different data sources. INDEX TERMS Data fusion, time-evolving network, criminal network analysis, deep reinforcement learning, node similarity.

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“…Distinguishing features of this work include: It is noteworthy that several prior studies listed in Table 1 analyze both the spatial and temporal dimensions of crime [18][19][20][21][22][23]. This is achieved either by applying autocorrelation and autoregression analysis of crime event time series, or by adding clock and calendar attributes (such as the hour or hour interval, day of the week, and month) to model inputs.…”

Section: Related Workmentioning

confidence: 99%

Urban Crime Risk Prediction Using Point of Interest Data

Cichosz

2020

IJGI

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Geographical information systems have found successful applications to prediction and decision-making in several areas of vital importance to contemporary society. This article demonstrates how they can be combined with machine learning algorithms to create crime prediction models for urban areas. Selected point of interest (POI) layers from OpenStreetMap are used to derive attributes describing micro-areas, which are assigned crime risk classes based on police crime records. POI attributes then serve as input attributes for learning crime risk prediction models with classification learning algorithms. The experimental results obtained for four UK urban areas suggest that POI attributes have high predictive utility. Classification models using these attributes, without any form of location identification, exhibit good predictive performance when applied to new, previously unseen micro-areas. This makes them capable of crime risk prediction for newly developed or dynamically changing neighborhoods. The high dimensionality of the model input space can be considerably reduced without predictive performance loss by attribute selection or principal component analysis. Models trained on data from one area achieve a good level of prediction quality when applied to another area, which makes it possible to transfer or combine crime risk prediction models across different urban areas.

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Spatio-temporal prediction of crimes using network analytic approach

Cited by 20 publications

References 22 publications

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

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

Situation-Aware Deep Reinforcement Learning Link Prediction Model for Evolving Criminal Networks

Urban Crime Risk Prediction Using Point of Interest Data

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