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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 21

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

A simple crime hotspot forecasting algorithm

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DOI: http://dx.doi.org/10.15439/2020F5

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 23–26 ()

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Abstract. Crime hotspot forecasting is an important part of crime prevention and reducing the delay between a 911 call and the physical intervention. Current developments in the field focus on enriching the historical data and sophisticated point process analysis methods with a fixed grid. In the paper we present a simple spatio-temporal point process allowing one to perform exhaustive (literal) grid searches. We then show that this approach can compete with more complex methods, as evidenced by the results on data collected by the Portland Bureau of Police. Finally, we discuss the advantages and potential implications of the new method.

References

  1. W. Gorr, A. Olligschlaeger, and Y. Thompson, “Short-term forecasting of crime,” International Journal of Forecasting, vol. 19, pp. 579–594, 2003.
  2. J. Cohen, W. L. Gorr, and A. M. Olligschlaeger, “Leading indicators and spatial interactions: A crime-forecasting model for proactive police deployment,” Geographical Analysis, vol. 39, pp. 105–127, 2007.
  3. L. W. Kennedy, J. M. Caplan, and E. Piza, “Risk clusters, hotspots, and spatial intelligence: Risk terrain modeling as an algorithm for police resource allocation strategies,” Journal of Quantitative Criminology, vol. 27, pp. 339–362, 2011.
  4. X. Wang, M. S. Gerber, and D. E. Brown, “Automatic crime prediction using events extracted from twitter posts,” in Social Computing Behavioral - Cultural Modeling and Prediction. Springer Berlin Heidelberg, 2012, pp. 231–238.
  5. K. J. Bowers, S. D. Johnson, and K. Pease, “Prospective hot-spotting: The future of crime mapping?” The British Journal of Criminology, vol. 44, pp. 641–658, 2004.
  6. S. Chainey, L. Tompson, and S. Uhlig, “The utility of hotspot mapping for predicting spatial patterns of crime,” Security Journal, vol. 21, pp. 4–28, 2008.
  7. M. Fielding and V. Jones, “Disrupting the optimal forager: Predictive risk mapping and domestic burglary reduction in trafford, greater manchester,” International Journal of Police Science & Management, vol. 14, pp. 30–41, 2012.
  8. W. L. Gorr and Y. Lee, “Early warning system for temporary crime hot spots,” Journal of Quantitative Criminology, vol. 31, pp. 25–47, 2015.
  9. M. D. Porter and B. J. Reich, “Evaluating temporally weighted kernel density methods for predicting the next event location in a series,” Annals of GIS, vol. 18, pp. 225–240, 2012.
  10. M. A. Boni and M. S. Gerber, “Automatic optimization of localized kernel density estimation for hotspot policing,” in Proc. 15th IEEE Int. Conf. Machine Learning and Applications (ICMLA), Dec. 2016, pp. 32–38.
  11. H. Liu and D. E. Brown, “Criminal incident prediction using a point-pattern-based density model,” International Journal of Forecasting, vol. 19, pp. 603–622, 2003.
  12. M. A. Taddy, “Autoregressive mixture models for dynamic spatial poisson processes: Application to tracking intensity of violent crime,” Journal of the American Statistical Association, vol. 105, pp. 1403–1417, 2010.
  13. G. Rosser and T. Cheng, “Improving the robustness and accuracy of crime prediction with the self-exciting point process through isotropic triggering,” Applied Spatial Analysis and Policy, pp. 1–21, 2016.
  14. G. O. Mohler, M. B. Short, P. J. Brantingham, F. P. Schoenberg, and G. E. Tita, “Self-exciting point process modeling of crime,” Journal of the American Statistical Association, vol. 106, pp. 100–108, 2011.
  15. G. Mohler, “Marked point process hotspot maps for homicide and gun crime prediction in chicago,” International Journal of Forecasting, vol. 30, pp. 491–497, 2014.
  16. G. O. Mohler, M. B. Short, S. Malinowski, M. Johnson, G. E. Tita, A. L. Bertozzi, and P. J. Brantingham, “Randomized controlled field trials of predictive policing,” Journal of the American Statistical Association, vol. 110, pp. 1399–1411, 2015.
  17. C. Loeffler and S. Flaxman, “Is gun violence contagious? A spatiotemporal test,” Journal of Quantitative Criminology, 2017.
  18. W. Perry, B. McInnis, C. Price, S. Smith, and J. Hollywood, “Predictive policing: The role of crime forecasting in law enforcement operations,” RAND Corporation, Santa Monica, Tech. Rep., 2013.
  19. National Institute of Justice, “Real-Time Crime Forecasting Challenge,” https://www.nij.gov/funding/Pages/fy16-crime-forecasting-challenge.aspx, 2017
  20. L. A. Rastrigin, “About convergence of random search method in extremal control of multi-parameter systems,” Automation and Remote Control, vol. 24, pp. 1467–1473, 1963.
  21. J. M. Hunt, “Do crime hot spots move? exploring the effects of the modifiable areal unit problem and modifiable temporal unit problem on crime hot spot stability,” Ph.D. dissertation, 2016.
  22. G. Mohler and M. D. Porter, “Rotational grid, PAI-maximizing crime forecasts,” 2018, to appear in Statistical Analysis and Data Mining.
  23. S. Flaxman, M. Chirico, P. Pereira, and C. Loeffler, “Scalable highresolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the nij "Real-Time Crime Forecasting Challenge",” 2018.