Abstract
Prediction can play a very important role in many types of domains, including the criminal justice system. Even a little information can be gained from proper police assignments, which can increase the efficiency of the crime patrolling system. Citizens can also be aware and alert for possible future criminal incidents. This was identified previously, but the proposed solutions use many complex features, which are difficult to collect, especially for developing and underdeveloped countries, and the maximum accuracy obtained to date using simple features is around 66%. Few of these countries have even started collecting such criminal records in digital format. Thus, there is a need to use simple and minimal required features for prediction and to improve prediction accuracy. In the proposed work, a spatiotemporal ordinary kriging model is used. This method uses not only minimal features such as location, time and crime type, but also their correlation to predict future crime locations, which helps to increase accuracy. Past crime hot spot locations are used to predict future possible crime locations. To address this, the Philadelphia dataset is used to extract features such as latitude, longitude, crime type and time of incident, and prediction can be given for every 0.36 square km per day. The city area is divided into grids of 600 \(\times \) 600 m. According to the evaluation results, the average sensitivity and specificity obtained for these experiments is 90.52 and 88.63%, respectively.
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Chainey, Spencer, Lisa Tompson, and Sebastian Uhlig. 2008. The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal - Palgrave Macmillan 21: 1–2.
IBM Business Analytic Software Services. 2012. IBM SPSS Crime Prediction Analytics Solution (CPAS) Service. IBM Corporation.
Kang, Qiao, Wei-keng Liao, Ankit Agrawal, and Alok Choudhary. 2016. A filtering-based clustering algorithm for improving spatio-temporal kriging interpolation accuracy. In Proceedings of the 25th ACM international on conference on information and knowledge management, 2209–2214.
Liu, Hua, and Donald E. Brown. 2003. Criminal incident prediction using a point-pattern-based density model. International Journal of Forecasting - Elsevier 19: 603–622.
Mateu, Jorge, and others. 2015. Spatial and spatio-temporal geostatistical modeling and kriging, vol. 998, 162–267. New York: Wiley.
Padoan, Simone and Moreno Bevilacqua. 2015. Composite-Likelihood Based Analysis of Random Fields. https://cran.r-project.org/web/packages/CompRandFld/CompRandFld.pdf
Parvez, Md Rizwan, Turash Mosharraf, and Mohammed Eunus Ali. 2016. A Novel approach to identify spatio-temporal crime pattern in Dhaka city. In Proceedings of the eighth international conference on information and communication technologies and development. ACM.
PredPol. 2015. How PredPol Works: We Provide Guidance on Where and When to Patrol. http://www.predpol.com/
Ritter, Nancy. 2013. Predicting recidivism risk: New tool in Philadelphia shows great promise. National Institute of Justice Journal 271: 4–13.
Vakil-Baghmisheh, Mohammad-Taghi, and Alireza Navarbaf. 2008. A modified very fast simulated annealing algorithm. In IEEE international symposium on telecommunications, IST 2008, 61–66.
Varin, Cristiano, Nancy Reid, and David Firth. 2011. An overview of composite likelihood methods. In Statistica Sinica - JSTOR, 5–42.
Zheng, Xifan, Yang Cao, and Zhiyu Ma. 2011. A mathematical modeling approach for geographical profiling and crime prediction. In 2011 IEEE 2nd international conference on software engineering and service science (ICSESS), 500–503.
Zhou, Zhengyi, and David S. Matteson. 2015. Predicting ambulance demand: A spatio-temporal kernel approach. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 2297–2303.
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Deshmukh, S.S., Annappa, B. (2019). Prediction of Crime Hot Spots Using Spatiotemporal Ordinary Kriging. In: Krishna, A., Srikantaiah, K., Naveena, C. (eds) Integrated Intelligent Computing, Communication and Security. Studies in Computational Intelligence, vol 771. Springer, Singapore. https://doi.org/10.1007/978-981-10-8797-4_70
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DOI: https://doi.org/10.1007/978-981-10-8797-4_70
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