Spatio-temporal modeling of criminal incidents using geographic, demographic, and twitter-derived information

Wang, Xiaofeng; Brown, Donald E.; Gerber, Matthew S.

doi:10.1109/isi.2012.6284088

Cited by 64 publications

(43 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Liu and Brown [20] and Wang et al [32] have considered demographic, spatial, temporal and social-media dependent models. Particularly Liu and Brown [20] propose a transition density model that takes into account demographic, economic, social, victim and spatial attributes of criminal activity.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression

et al. 2018

View full text Add to dashboard Cite

Objectives: This paper describes the use of machine learning techniques to implement a Bayesian approach to modelling the dependency between offence data and environmental factors such as demographic characteristics and spatial location. The main goal of this paper is to provide a fully probabilistic approach to modelling crime which reflects all uncertainties in the prediction of offences as well as the uncertainties surrounding model parameters. Methods:The proposed method is based on a Bayesian framework, with a Gaussian Process prior and MCMC, allowing uncertainties in prediction and inference to be quantified via the posterior distributions of interest. By using Bayesian updating, these predictions and inferences are dynamic in the sense that they change as new information becomes available. Results:We applied the proposed methodology to particular offence data, such as domestic violence-related assaults, burglary and motor vehicle theft, in the state of New South Wales (NSW), Australia. Our results demonstrate the strength of the technique by validating the factors that are associated with high and low criminal activity, including bounds on the degree of the relation. Conclusions:We argue that this fully probabilistic approach will improve prediction, in the sense that the uncertainties are more accurately quantified, with attendant benefits to policymakers and policing organisations seeking to deploy limited criminal justice resources to prevent and control crime. While limitations and areas for potential improvement are identified, the success of the Bayesian approach, implemented using machine learning techniques, in a criminological context represents an exciting development.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Our approach is mainly inspired by the work of Flaxman [20], Liu and Brown [13] and Liu and Brown [32]. We derive a general probabilistic model that can capture generic features across space and that can consider spatial correlations using a non-parametric component.…”

Section: Related Workmentioning

confidence: 99%

Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In [13], Gerber utilized a logistic regression model for spatiotemporal events forecasting using topic-related tweet volumes as features. Wang et al [30] developed a spatiotemporal generalized additive model to characterize and predict spatio-temporal criminal incidents, but their model requires the demographic data. Ramakrishnan et al [22] built separate LASSO models for different locations to predict the occurrence of civil unrest events.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Learning for Spatio-Temporal Event Forecasting

Zhao

Sun

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

117

View full text Add to dashboard Cite

Spatial event forecasting from social media is an important problem but encounters critical challenges, such as dynamic patterns of features (keywords) and geographic heterogeneity (e.g., spatial correlations, imbalanced samples, and different populations in different locations). Most existing approaches (e.g., LASSO regression, dynamic query expansion, and burst detection) are designed to address some of these challenges, but not all of them. This paper proposes a novel multi-task learning framework which aims to concurrently address all the challenges. Specifically, given a collection of locations (e.g., cities), we propose to build forecasting models for all locations simultaneously by extracting and utilizing appropriate shared information that effectively increases the sample size for each location, thus improving the forecasting performance. We combine both static features derived from a predefined vocabulary by domain experts and dynamic features generated from dynamic query expansion in a multi-task feature learning framework; we investigate different strategies to balance homogeneity and diversity between static and dynamic terms. Efficient algorithms based on Iterative Group Hard Thresholding are developed to achieve efficient and effective model training and prediction. Extensive experimental evaluations on Twitter data from four different countries in Latin America demonstrated the effectiveness of our proposed approach.

show abstract

“…As yet, there have been few reports of work specifically on spatiotemporal event forecasting. Gerber [6] proposed a predictor for spatiotemporal events by utilizing historical event counts and topics, but do not consider temporal evolution and dependencies, while Wang et al [20] developed a model to characterize and predict spatio-temporal criminal incidents, but their model requires the availability of demographic information.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Event Forecasting in Social Media

Zhao¹,

Chen²,

Lu³

et al. 2015

Proceedings of the 2015 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

Event forecasting in Twitter is an important and challenging problem. Most existing approaches focus on forecasting temporal events (such as elections and sports) and do not consider spatial features and their underlying correlations. In this paper, we propose a generative model for spatiotemporal event forecasting in Twitter. Our model characterizes the underlying development of future events by jointly modeling the structural contexts and spatiotemporal burstiness. An effective inference algorithm is developed to train the model parameters. Utilizing the trained model, the alignment likelihood of tweet sequences is calculated by dynamic programming. Extensive experimental evaluations on two different domains demonstrated the effectiveness of our proposed approach.

show abstract

Spatio-temporal modeling of criminal incidents using geographic, demographic, and twitter-derived information

Cited by 64 publications

References 12 publications

Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression

Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression

Multi-Task Learning for Spatio-Temporal Event Forecasting

Spatiotemporal Event Forecasting in Social Media

Contact Info

Product

Resources

About