Applying Crime Prediction Techniques to Japan: A Comparison Between Risk Terrain Modeling and Other Methods

Adepeju, M., Rosser, G., & Cheng, T. (2016). Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions-a crime case study. International Journal of Geographical Information Science, 30(11), 1–22. Anselin, L., Cohen, J., Cook, D., Gorr, W., & Tita, G. (2000). Spatial analyses of crime. Criminal Justice, 4(2), 213–262. Block, C. (1995). STAC hot-spot areas: A statistical tool for law enforcement decisions. In Crime analysis through computer mapping (pp. 15–32). Washington, DC: Police Executive Research Forum. Block, R., & Block, C. (1995). Space, place and crime: Hot spot areas and hot places of liquor-related crime. In J. Eck & D. Weisburd (Eds.), Crime and place (pp. 145–183). Monsey: Criminal Justice Press. Bowers, K., Johnson, S., & Pease, K. (2004). Prospective hot-spotting: the future of crime mapping? British Journal of Criminology, 44(5), 641–658. Brantingham, P., & Brantingham, P. (1995). Criminality of place: Crime generators and crime attractors. European Journal on Criminal Policy and Research, 3(3), 1–26. Cabinet Secretariat (2014). Action Plan on Promoting Utilization of Geospatial Information: Summary by Measures (G-Space Action Plan), Geospatial Information Utilization Promotion Meeting. http://www.cas.go.jp/jp/seisaku/sokuitiri/260617/gaiyou03.pdf. Accessed 16 February 2017. (In Japanese). Caplan, J., & Kennedy, L. (2016). Risk terrain modeling: Crime prediction and risk reduction. Oakland: University of California Press. Caplan, J., Kennedy, L., & Miller, J. (2011). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28(2), 360–381. Caplan, J., Kennedy, L., & Piza, E. (2013). Risk terrain modeling diagnostics utility (version 1.0). Newark: Rutgers Center on Public Security. Chainey, S. (2005). Methods and techniques for understanding crime hot spots. In Mapping crime: Understanding hot spots (pp. 15–34). US Department of Justice. Chainey, S., & Ratcliffe, J. (2013). GIS and crime mapping. Chichester: Wiley. Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1), 4–28. Cheng, T., & Adepeju, M. (2013). Detecting emerging space-time crime patterns by prospective STSS. In Proceedings of the 12th international conference on geocomputation. http://www.geocomputation.org/2013/papers/77.pdf. Accessed 22 August 2017. CIVITAS. (2012). Comparisons of Crime in OECD Countries. http://www.civitas.org.uk/content/files/crime_stats_oecdjan2012.pdf. Accessed 22 August 2017. Dugato, M. (2013). Assessing the validity of risk terrain modeling in a European city: Preventing robberies in the city of Milan. Crime Mapping, 5(1), 63–89. Drawve, G. (2014). A metric comparison of predictive hot spot techniques and RTM. Justice Quarterly, 33(3), 369–397. Eck, J., Chainey, S., Cameron, J., & Wilson, R. (2005). Mapping crime: Understanding hotspots. Washington, D.C.: National Institute of Justice. Fox, J., & Brown, D. (2012). Using temporal indicator functions with generalized linear models for spatial-temporal event prediction. Procedia Computer Science, 8, 106–111. Gorr, W., & Harries, R. (2003). Introduction to crime forecasting. International Journal of Forecasting, 19(4), 551–555. Groff, E., & La Vigne, N. (2002). Forecasting the future of predictive crime mapping. Crime Prevention Studies, 13, 29–57. Haberman, C. (2017). Overlapping Hot Spots? Criminology & Public Policy, 16(2), 633–660. Hart, T., & Zandbergen, P. (2012). Effects of data quality on predictive hotspot mapping: Final technical report (doc. #239861). Washington, D.C.: National Institute of Justice. Johnson, S., Birks, D., McLaughlin, L., Bowers, K., & Pease, K. (2007). Prospective crime mapping in operational context, final report. London: Home Office. Johnson, S., Bowers, K., Birks, D., & Pease, K. (2009). Predictive mapping of crime by ProMap: Accuracy, units of analysis, and the environmental backcloth. In D. Weisburd, W. Bernasco, & G. J. N. Bruinsma (Eds.), Putting crime in its place (pp. 171–198). New York: Springer. Johnson, S., Summers, L., & Pease, K. (2006). Vehicle crime: Communicating spatial and temporal patterns, final report. London: Home Office. Kennedy, L., Caplan, J., & Piza, E. (2011). Risk clusters, hotspots, and spatial intelligence: Risk terrain modeling as an algorithm for police resource allocation strategies. Journal of Quantitative Criminology, 27(3), 339–362. Kikuchi, G., Amemiya, M., Shimada, T., Saito, T., & Harada, Y. (2010). An analysis of near repeat victimization patterns across crime types―an application of Spatio-temporal K function―. Theory and Applications of GIS, 18(2), 129–138 (In Japanese). Kulldorff, M., Athas, W., Feurer, E., Miller, B., & Key, C. (1998). Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico. American Journal of Public Health, 88(9), 1377–1380. Kulldorff, M., Heffernan, R., Hartman, J., Assunção, R., & Mostashari, F. (2005). A space–time permutation scan statistic for disease outbreak detection. PLoS Medicine, 2(3), 216–224. Levine, N. (2008). The “hottest” part of a hotspot: Comments on “the utility of hotspot mapping for predicting spatial patterns of crime”. Security Journal, 21(4), 295–302. Mohler, G. (2014). Marked point process hotspot maps for homicide and gun crime prediction in Chicago. International Journal of Forecasting, 30(3), 491–497. Mohler, G. (2015). Event forecasting system. U. S. Patent 13,676,358. 3 February 2015. Mohler, G., Short, M., Brantingham, P., Schoenberg, F., & Tita, G. (2011). Self-exciting point process modeling of crime. Journal of the American Statistical Association, 106(493), 100–108. Moreto, W., & Caplan, J. M. (2010). Forecasting global maritime piracy utilizing the risk terrain modeling (rtm) approach. Rutgers center on public security brief. Moreto, W. D., Piza, E. L., & Caplan, J. M. (2014). “A plague on both your houses?”: Risks, repeats and reconsiderations of urban residential burglary. Justice Quarterly, 31(6), 1102–1126. Nagasawa, H., & Hosoe, T. (1999). A study on the factors of criminal situations of thefts from cars. Bulletin of the Faculty of Social Welfare, Iwate Prefectural University, 61–72. (In Japanese). Ohyama, T., Amemiya, M., Shimada, T., & Nakaya, T. (2017). Recent research trends on geographical crime prediction. Theory and Applications of GIS, 25(1), 33–43 (In Japanese). Onat, I., & Gul, Z. (2018). Terrorism Risk Forecasting by Ideology. European Journal on Criminal Policy and Research, 1–17. Openshaw, S. (1995). Developing automated and smart spatial pattern exploration tools for geographical information systems applications. Journal of the Royal Statistical Society. Series D (The Statistician), 44(1), 3–16. Openshaw, S., Charlton, M., Wymer, C., & Craft, A. (1987). A mark 1 geographical analysis machine for the automated analysis of point data sets. International Journal of Geographical Information System, 1(4), 335–358. Perry, W., McInnis, B., Price, C., Smith, S., & Hollywood, J. (2013). Predictive policing: The role of crime forecasting in law enforcement operations. Santa Monica: Rand Corporation. Ratcliffe J. (1999). Hotspot detective for MapInfo helpfile version 1.0. Roberts, A. (2008). The influences of incident and contextual characteristics on crime clearance of nonlethal violence: A multilevel event history analysis. Journal of Criminal Justice, 36(1), 61–71. Roberts, A., & LaFree, G. (2004). Explaining Japan’s postwar violent crime trends. Criminology, 42(1), 179–210. Rosser, G., & Cheng, T. (2016). Improving the robustness and accuracy of crime prediction with the self-exciting point process through isotropic triggering. Applied Spatial Analysis and Policy, 1–21. Rosser, G., Davies, T., Bowers, K. J., Johnson, S., & Cheng, T. (2017). Predictive crime mapping: Arbitrary grids or street networks? Journal of Quantitative Criminology, 33(3), 569–594. Sherman, L. (1995). Hot spots of crime and criminal careers of places. In J. Eck & D. Weisburd (Eds.), Crime and place: Crime prevention studies (pp. 35–52). Monsey: Criminal Justice Press. Sherman, L., Gartin, P., & Buerger, M. (1989). Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27(1), 27–56. Shiode, S. (2011). Street-level spatial scan statistic and STAC for Analysing street crime concentrations. Transactions in GIS, 15(3), 365–383. Shiode, S., & Shiode, N. (2014). Microscale prediction of near-future crime concentrations with street-level Geosurveillance. Geographical Analysis, 46(4), 435–455. Thakali, L., Kwon, T. J., & Fu, L. (2015). Identification of crash hotspots using kernel density estimation and kriging methods: A comparison. Journal of Modern Transportation, 23(2), 93–106. Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries. A test of the near repeat hypothesis. British Journal of Criminology, 43(3), 615–633. Van Patten, I., McKeldin-Coner, J., & Cox, D. (2009). A Microspatial analysis of robbery: Prospective hot spotting in a Small City. Crime Mapping: A Journal of Research and Practice, 1(1), 7–32. Wang, X., & Brown, D. (2012). The spatio-temporal modeling for criminal incidents. Security Informatics, 1(1), 1–17.