Spatio-temporal Probabilistic Modeling Based on Gaussian Mixture Models and Neural Gas Theory for Prediction of Criminal Activity

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Francisco Jaramillo Vanessa Quintero Aramis Pérez Marcos Orchard

Abstract

Criminal risk models are used to assist security forces both in the identification of zones with high of criminal activity for better resource allocation and prediction of future criminal events for the prevention of new crimes. In this sense, spatio-temporal models are widely employed by their capacity of characterizing the criminal risk inside of a zone of interest and updating the model to new crime data. This paper improves an existing method based on spatio-temporal probabilistic risk functions. The spatial probabilistic characterization uses geo-referenced information of criminal incidents related to public services to approximate a risk function based on a Gaussian Mixture Model (GMM). The temporal characterization is supported by Importance Sampling methods and Neural Gas Theory to incorporate the information from new measurements, in a recursive manner, updating the spatial probabilistic risk function. Finally, we propose a prediction scheme for criminal activity that also uses Neural Gas Theory, in conjunction with hypothetical future criminal events sampled from a GMM that characterizes the spatial distribution associated with recent criminal activity. The time index related to each hypothetical future crime event is probabilistically characterized using an exponential distribution. Results using real data and the defined performance indexes show an improvement both in the temporal updating as well as the proposed prediction approach.

How to Cite

Jaramillo, F., Quintero, V., Pérez, A., & Orchard, M. (2017). Spatio-temporal Probabilistic Modeling Based on Gaussian Mixture Models and Neural Gas Theory for Prediction of Criminal Activity. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2391
Abstract 162 | PDF Downloads 42

##plugins.themes.bootstrap3.article.details##

Keywords

criminal risk characterization, Gaussian Mixture Model, Neural Gas Theory

References
Ancona, F., Rovetta, S., & Zunino, R. (1997, Jun). Hardware implementation of the neural gas. In Neural networks,1997., international conference on (Vol. 2, p. 991-994 vol.2).
Caplan, J. M., & Kennedy, L. W. (2011). Risk terrain modeling compendium. Rutgers Center on Public Security, Newark.
De-Alarcon, P. A., Pascual-Montano, A. P., Gupta, A., & Carazo, J. M. (2002). Modeling shape and topology of 3d images of biological specimens. In Object recognition supported by user interaction for service robots (Vol. 1, p. 79-82 vol.1).
Eck, J., Chainey, S., Cameron, J., &Wilson, R. (2005). Mapping crime: Understanding hotspots.
Fawcett, T. (2006). An introduction to roc analysis. Pattern recognition letters, 27(8), 861–874.
Flores, P., Vergara, M., Fuentes, P., Jaramillo, F., Acuna, D., Perez, A., & Orchard, M. (2015). Modeling and prediction of criminal activity based on spatio-temporal probabilistic risk functions. , 6.
Ivaha, C., Al-Madfai, H., Higgs, G., & Ware, J. (2007). The Dynamic Spatial Disaggregation Approach: A Spatio-Temporal Modelling of Crime. In World congress on engineering (pp. 961–966).
Martinetz, T. M., Berkovich, S. G., & Schulten, K. J. (1993, Jul). ‘neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, 4(4), 558-569.
Orts-Escolano, S., Garcia-Rodriguez, J., Serra-Perez, J. A., Jimeno-Morenilla, A., Garcia-Garcia, A., Morell, V., & Cazorla, M. (2015). 3d model reconstruction using neural gas accelerated on fGPUg. Applied Soft Computing, 32, 87 - 100.
Peterson, L. E., Ather, S., Divakaran, V., Deswal, A., Bozkurt, B., & Mann, D. L. (2009, June). Improved propensity matching for heart failure using neural gas and self-organizing maps. In 2009 international joint conference on neural networks (p. 2517-2524).
Smith, M. A., & Brown, D. E. (2007). Discrete choice analysis of spatial attack sites. Information Systems and e-Business Management, 5(3), 255–274.
Wang, X., & Brown, D. (2012). The spatio-temporal modeling for criminal incidents. Security Informatics, 1(1), 1–17.
Xue, Y., & Brown, D. (2006, March). Spatial analysis with preference specification of latent decision makers for criminal event prediction. Decision Support Systems, 41(3), 560–573.
Section
Technical Papers

Most read articles by the same author(s)

1 2 > >>