A simple crime hotspot forecasting algorithm

• Autorzy: Bogucki Robert , Milczek Jan Kanty , Miziuła Patryk

Identyfikatory

DOI

10.15439/2020F5

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

computer science; law enforcement; emergency services; delays; forecasting; information systems

PL

informatyka; egzekwowanie prawa; służby ratunkowe; opóźnienia; prognozowanie; systemy informacyjne

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 23--26
• Opis fizyczny: Bibliogr. 23 poz., wz., rys.

Twórcy

autor

Bogucki Robert

• University of Warsaw ul. Banacha 2, 02-097 Warsaw, Poland
• deepsense.ai, Al. Jerozolimskie 162A, 02-342 Warsaw, Poland

autor

Milczek Jan Kanty

• deepsense.ai, Al. Jerozolimskie 162A, 02-342 Warsaw, Poland

autor

Miziuła Patryk

• deepsense.ai, Al. Jerozolimskie 162A, 02-342 Warsaw, Poland

Bibliografia

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• 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.
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• 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.
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• 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.

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).