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Personalized crime location prediction

Published online by Cambridge University Press:  28 April 2016

MOHAMMAD A. TAYEBI ,
UWE GLÄSSER ,
MARTIN ESTER  and
PATRICIA L. BRANTINGHAM
MOHAMMAD A. TAYEBI
Affiliation:
School of Computing Science, Simon Fraser University, B.C., Canada email: tayebi@cs.sfu.ca, glaesser@cs.sfu.ca, ester@cs.sfu.ca
UWE GLÄSSER
Affiliation:
School of Computing Science, Simon Fraser University, B.C., Canada email: tayebi@cs.sfu.ca, glaesser@cs.sfu.ca, ester@cs.sfu.ca
MARTIN ESTER
Affiliation:
School of Computing Science, Simon Fraser University, B.C., Canada email: tayebi@cs.sfu.ca, glaesser@cs.sfu.ca, ester@cs.sfu.ca
PATRICIA L. BRANTINGHAM
Affiliation:
School of Criminology, Simon Fraser University, B.C., Canada email: pbrantin@sfu.ca
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Abstract

Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here.

Keywords

Spatial crime analysisRandom walk modelActivity spaceCrime occurrence spaceCo-offending networks
Type
Papers
Information
European Journal of Applied Mathematics , Volume 27 , Issue 3: Mathematical Modelling of Crime and Security , June 2016 , pp. 422 - 450
Copyright
Copyright © Cambridge University Press 2016 

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References

[1]Backstrom, L. & Leskovec, J. (2011) Supervised random walks: Predicting and recommending links in social networks. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM'11), Hong Kong, China, pp. 635–644.Google Scholar
[2]Bernasco, W. (2006) Co-offending and the choice of target areas in burglary. J. Investigative Psychol. Offender Profiling 3 (3), 139–155.Google Scholar
[3]Bernasco, W. (2008) Them again? Same-offender involvement in repeat and near repeat burglaries. Eur. J. Criminology 5 (4), 411–431.CrossRefGoogle Scholar
[4]Bernasco, W. (2010) Modeling micro-level crime location choice: Application of the discrete choice framework to crime at places. J. Quant. Criminology 26 (1), 113–138.Google Scholar
[5]Bernasco, W. & Block, R. (2009) Where offenders choose to attack: A discrete choice model of robberies in chicago. Criminology 47 (1), 93–130.Google Scholar
[6]Bernasco, W. & Nieuwbeerta, P. (2005) How do residential burglars select target areas? A new approach to the analysis of criminal location choice. Br. J. Criminology 45 (3), 296–315.Google Scholar
[7]Braga, A. A. (2001) The effects of hot spots policing on crime. Ann. Am. Acad. Political Soc. Sci. 578 (1), 104–125.Google Scholar
[8]Brantingham, P. J. & Brantingham, P. L. (1981) Environmental Criminology, Sage Publications, Beverly Hills, CA, USA.Google Scholar
[9]Brantingham, P. L. & Brantingham, P. J. (1993) Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. J. Environ. Psychol. 13 (1), 3–28.CrossRefGoogle Scholar
[10]Brantingham, P. L. & Brantingham, P. J. (1995) Criminality of place. Eur. J. Criminal Policy Res. 3 (3), 5–26.Google Scholar
[11]Brantingham, P. L., Ester, M., Frank, R., Glässer, U. & Tayebi, M. A. (2011) Co-offending network mining. In: Wiil, U. K. (editor), Counterterrorism and Open Source Intelligence, Springer, Vienna, pp. 73–102.CrossRefGoogle Scholar
[12]Brockmann, D., Hufnagel, L. & Geisel, T. (2006) The scaling laws of human travel. Nature 439 (7075), 462–465.Google Scholar
[13]Canter, D. V. & Gregory, A. (1994) Identifying the residential location of rapists. J. Forensic Sci. Soc. 34 (3), 169–175.CrossRefGoogle ScholarPubMed
[14]Chrastil, E. R. (2013) Neural evidence supports a novel framework for spatial navigation. Psychonomic Bull. Rev. 20 (2), 208–227.Google Scholar
[15]Davies, T. P. & Bishop, S. R. (2013) Modelling patterns of burglary on street networks. Crime Sci. 2 (1), Article 10.CrossRefGoogle Scholar
[16]Frank, R., Andresen, M. A. & Brantingham, P. L. (2012) Criminal directionality and the structure of urban form. J. Environ. Psychol. 32 (1), 37–42.CrossRefGoogle Scholar
[17]Frank, R., Andresen, M. A. & Brantingham, P. L. (2013) Visualizing the directional bias in property crime incidents for five canadian municipalities. Can. Geogr./Le Géographe Can. 57 (1), 31–42.Google Scholar
[18]Frank, R., Andresen, M. A., Cheng, C. & Brantingham, P. L. (2011) Finding criminal attractors based on offenders' directionality of crimes. In: Proceedings of the 2011 European Intelligence and Security Informatics Conference (EISIC'11), Athens, Greece, pp. 86–93.CrossRefGoogle Scholar
[19]Frank, R. & Kinney, B. (2012) How many ways do offenders travel – evaluating the activity paths of offenders. In: Proceedings of the 2012 European Intelligence and Security Informatics Conference (EISIC'12), Odense, Denmark, pp. 99–106.Google Scholar
[20]Glässer, U., Taybei, M. A., Brantingham, P. L. & Brantingham, P. J. (2012) Estimating possible criminal organizations from co-offending data. Public Safety Canada, Ottawa, Canada, http://publications.gc.ca/collections/collection_2012/sp-ps/PS14-7-2012-eng.pdf.Google Scholar
[21]Golledge, R. G. (1981) Misconceptions, misinterpretations, and misrepresentations of behavioral approaches in human geography. Environ. Plan. A 13 (11), 1325–44.Google Scholar
[22]Gonzalez, M. C., Hidalgo, C. A. & Barabasi, A. (2008) Understanding individual human mobility patterns. Nature 453 (7196), 779–782.Google Scholar
[23]Gorr, W. & Harries, R. (2003) Introduction to crime forecasting. Int. J. Forecast. 19 (4), 551–555.Google Scholar
[24]Grimmett, G. & Stirzaker, D. (1989) Probability and Random Processes, Oxford University Press, Oxford, UK.Google Scholar
[25]Harries, K. (1999) Mapping crime principle and practice. U.S. Department of Justice, Office of Justice Programs, National Institute of Justice, Washington, DC, USA, https://www.ncjrs.gov/pdffiles1/nij/178919.pdf.Google Scholar
[26]Johnson, S. D. (2008) Repeat burglary victimisation: A tale of two theories. J. Exp. Criminology 4 (3), 215–240.Google Scholar
[27]Johnson, S. D. (2010) A brief history of the analysis of crime concentration. Eur. J. Appl. Math. 21 (4–5), 349–370.CrossRefGoogle Scholar
[28]Johnson, S. D., Bernasco, W., Bowers, K. J., Elffers, H., Ratcliffe, J., Rengert, G. & Townsley, M. (2007) Space–time patterns of risk: A cross national assessment of residential burglary victimization. J. Quant. Criminology 23 (3), 201–219.Google Scholar
[29]Kolokolnikov, T., Ward, M. & Wei, J. (2014) The stability of steady-state hot-spot patterns for a reaction-diffusion model of urban crime. Discrete Continuous Dyn. Syst. 19 (5), 1373–1410.Google Scholar
[30]Liu, B., Fu, Y., Yao, Z. & Xiong, H. (2013) Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'13), Chicago, Illinois, USA, pp. 1043–1051.CrossRefGoogle Scholar
[31]Liu, H. & Brown, D. E. (2003) Criminal incident prediction using a point-pattern-based density model. Int. J. Forecast. 19 (4), 603–622.Google Scholar
[32]Miller, B. N., Konstan, J. A. & Riedl, J. (2004) Pocketlens: Toward a personal recommender system. ACM Trans. Inform. Syst. (TOIS) 22 (3), 437–476.Google Scholar
[33]Quetelet, L. A. J. (1842) A Treatise on Man and the Development of His Faculties, Edinburgh: W. and R. Chambers, Edinburgh, UK.Google Scholar
[34]Reiss, A. J. Jr. (1988) Co-offending and criminal careers. Crime Justice 10, 117–170.Google Scholar
[35]Rodriguez, N. & Bertozzi, A. (2010) Local existence and uniqueness of solutions to a pde model for criminal behavior. Math. Models Methods Appl. Sci. 20 (supp01), 1425–1457.Google Scholar
[36]Rossmo, D. K. (2000) Geographic Profiling, CRC Press.Google Scholar
[37]Schaefer, D. R. (2012) Youth co-offending networks: An investigation of social and spatial effects. Soc. Netw. 34 (1), 141–149.Google Scholar
[38]Sherman, L. W., Gartin, P. R. & Buerger, M. E. (1989) Hot spots of predatory crime: Routine activities and the criminology of place. Criminology 27 (1), 27–56.Google Scholar
[39]Short, M. B., Bertozzi, A. L. & Brantingham, P. J. (2010) Nonlinear patterns in urban crime: Hotspots, bifurcations, and suppression. SIAM J. Appl. Dyn. Syst. 9 (2), 462–483.Google Scholar
[40]Short, M. B., Brantingham, P. J., Bertozzi, A. L. & Tita, G. E. (2010) Dissipation and displacement of hotspots in reaction-diffusion models of crime. Proc. Nat. Acad. Sci. 107 (9), 3961–3965.Google Scholar
[41]Short, M. B., D'orsogna, M. R., Pasour, V. B., Tita, G. E., Brantingham, P. J., Bertozzi, A. L. & Chayes, L. B. (2008) A statistical model of criminal behavior. Math. Models Methods Appl. Sci. 18 (supp01), 1249–1267.Google Scholar
[42]Song, J., Spicer, V., Brantingham, P. L. & Frank, R. (2013) Crime ridges: Exploring the relationship between crime attractors and offender movement. In: Proceedings of the 2013 European Intelligence And Security Informatics Conference (EISIC'13), IEEE, Uppsala, Sweden, pp. 75–82.Google Scholar
[43]Tayebi, M. A., Bakker, L., Glässer, U. & Dabbaghian, V. (2011) Locating central actors in co-offending networks. In: Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM'11), Kaohsiung, Taiwan, pp. 171–179.Google Scholar
[44]Tayebi, M. A., Ester, M., Glässer, U. & Brantingham, P. L. (2014) CrimeTracer: Activity space based crime location prediction. In: Proceedings of the 2014 International Conference on Advances in Social Networks Analysis and Mining (ASONAM'14), Beijing, China, pp. 472–480.Google Scholar
[45]Tayebi, M. A., Ester, M., Glässer, U. & Brantingham, P. L. (2014) Spatially embedded co-offence prediction using supervised learning. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), New York, New York, USA, pp. 1789–1798.Google Scholar
[46]Tayebi, M. A., Frank, R. & Glässer, U. (2012) Understanding the link between social and spatial distance in the crime world. In: Proceedings of the 20nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS'12), Redondo Beach, California, USA, pp. 550–553.Google Scholar
[47]Tayebi, M. A., Jamali, M., Ester, M., Glässer, U. & Frank, R. (2011) CrimeWalker: a recommendation model for suspect investigation. In: Proceedings of the 5th ACM Conference on Recommender Systems (RecSys'11), Chicago, Illinois, USA, pp. 173–180.Google Scholar
[48]Tayebi, M. A. & Glässer, U. (2012) Investigating organized crime groups: A social network analysis perspective. In: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM'12), Istanbul, Turkey, pp. 565–572.Google Scholar
[49]Tong, H., Faloutsos, C. & Pan, J. (2006) Fast random walk with restart and its applications. In Proceedings of the 6th International Conference on Data Mining (ICDM'06), Hong Kong, China, pp. 613–622.Google Scholar
[50]Townsley, M. & Sidebottom, A. (2010) All offenders are equal but some are more equal than others: Variation in journeys to crime between offenders. Criminology 48 (3), 897–917.CrossRefGoogle Scholar
[51]Wang, H., Terrovitis, M. & Mamoulis, N. (2013) Location recommendation in location-based social networks using user check-in data. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS'13), San Francisco, California, USA, pp. 374–383.Google Scholar
[52]Weisburd, D. L., Groff, E. R. & Yang, S. (2012) The Criminology of Place: Street Segments and Our Understanding of the Crime Problem, Oxford University Press.Google Scholar
[53]Wilson, J. Q. & Kelling, G. L. (1982) Broken windows and police and neighborhood safety. Atlantic Monthly 24 (9), 29–38.Google Scholar
[54]Yan, L., Dodier, R. H., Mozer, M. & Wolniewicz, R. H. (2003) Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic. In: Proceedings of the 20th International Conference on Machine Learning (ICML'03), Washington, DC, USA, pp. 848–855.Google Scholar
[55]Ye, M., Yin, P. & Lee, W. (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS'10), San Jose, California, USA, pp. 458–461.CrossRefGoogle Scholar