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

The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.

[1]  Gang Wang,et al.  Crime data mining: a general framework and some examples , 2004, Computer.

[2]  Peng Chen,et al.  Forecasting Crime Using the ARIMA Model , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[3]  Wei Ding,et al.  Crime Hot Spot Forecasting: A Recurrent Model with Spatial and Temporal Information , 2017, 2017 IEEE International Conference on Big Knowledge (ICBK).

[4]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[5]  Tong Wang,et al.  Learning to Detect Patterns of Crime , 2013, ECML/PKDD.

[6]  Gang Wang,et al.  Automatically detecting deceptive criminal identities , 2004, CACM.

[7]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[8]  Shyam Varan Nath,et al.  Crime Pattern Detection Using Data Mining , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops.

[9]  B. Chandra,et al.  A multivariate time series clustering approach for crime trends prediction , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[10]  Charles E. Catlett,et al.  Forecasting Crimes Using Autoregressive Models , 2016, 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

[11]  Franco Cicirelli,et al.  An edge-based platform for dynamic Smart City applications , 2017, Future Gener. Comput. Syst..

[12]  Martin Ester,et al.  CRIMETRACER: Activity space based crime location prediction , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[13]  Daniel Kifer,et al.  Crime Rate Inference with Big Data , 2016, KDD.

[14]  Charles E. Catlett,et al.  Plenario: An Open Data Discovery and Exploration Platform for Urban Science , 2014, IEEE Data Eng. Bull..

[15]  Jakarta Lima Lahore Calcutta Dhaka.  Cities: The century of the city , 2010, Nature.

[16]  Wei Ding,et al.  Crime Forecasting Using Data Mining Techniques , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[17]  Donald E. Brown,et al.  Data association methods with applications to law enforcement , 2003, Decis. Support Syst..

[18]  Walter A. Kosters,et al.  Data Mining Approaches to Criminal Career Analysis , 2006, Sixth International Conference on Data Mining (ICDM'06).

[19]  Wilpen L. Gorr,et al.  Short-term forecasting of crime , 2003 .