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

## Abstract

In recent years, the field of crime prediction has drawn increasing attention in Japan. However, predicting crime in Japan is especially challenging because the crime rate is considerably lower than that of other developed countries, making the development of a statistical model for crime prediction quite difficult. Risk terrain modeling (RTM) may be the most suitable method, as it depends mainly on the environmental factors associated with crime and does not require past crime data. In this study, we applied RTM to cases of theft from vehicles in Fukuoka, Japan, in 2014 and evaluated the predictive performance (hit rate and predictive accuracy index) in comparison to other crime prediction techniques, including KDE, ProMap, and SEPP, which use past crime occurrences to predict future crime. RTM was approximately twice as effective as the other techniques. Based on the results, we discuss the merits of and drawbacks to using RTM in Japan.

## Keywords

Crime prediction Crime mapping Risk terrain modeling Self-exciting point process Prospective mapping## Notes

### Acknowledgements

The data on crime was provided to us under the Fukuoka Prefectural Police’s Crime Prevention Research Advisor framework; we would like to thank the Fukuoka Prefectural Police for their support. We would also like to thank Dr. Millar for English language editing.

## Compliance with Ethical Standards

## Conflict of Interest

The authors declare that they have no conflict of interest.

## References

- 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.CrossRefGoogle Scholar - Anselin, L., Cohen, J., Cook, D., Gorr, W., & Tita, G. (2000). Spatial analyses of crime.
*Criminal Justice, 4*(2), 213–262.Google Scholar - 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.Google Scholar - 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.Google Scholar - Bowers, K., Johnson, S., & Pease, K. (2004). Prospective hot-spotting: the future of crime mapping?
*British Journal of Criminology, 44*(5), 641–658.CrossRefGoogle Scholar - Brantingham, P., & Brantingham, P. (1995). Criminality of place: Crime generators and crime attractors.
*European Journal on Criminal Policy and Research, 3*(3), 1–26.CrossRefGoogle Scholar - 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.Google Scholar - 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.CrossRefGoogle Scholar - Caplan, J., Kennedy, L., & Piza, E. (2013).
*Risk terrain modeling diagnostics utility (version 1.0)*. Newark: Rutgers Center on Public Security.Google Scholar - Chainey, S. (2005). Methods and techniques for understanding crime hot spots. In
*Mapping crime: Understanding hot spots*(pp. 15–34). US Department of Justice.Google Scholar - Chainey, S., & Ratcliffe, J. (2013).
*GIS and crime mapping*. Chichester: Wiley.Google Scholar - Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime.
*Security Journal, 21*(1), 4–28.CrossRefGoogle Scholar - 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.Google Scholar - Drawve, G. (2014). A metric comparison of predictive hot spot techniques and RTM.
*Justice Quarterly, 33*(3), 369–397.CrossRefGoogle Scholar - Eck, J., Chainey, S., Cameron, J., & Wilson, R. (2005).
*Mapping crime: Understanding hotspots*. Washington, D.C.: National Institute of Justice.Google Scholar - Fox, J., & Brown, D. (2012). Using temporal indicator functions with generalized linear models for spatial-temporal event prediction.
*Procedia Computer Science, 8*, 106–111.CrossRefGoogle Scholar - Gorr, W., & Harries, R. (2003). Introduction to crime forecasting.
*International Journal of Forecasting, 19*(4), 551–555.CrossRefGoogle Scholar - Groff, E., & La Vigne, N. (2002). Forecasting the future of predictive crime mapping.
*Crime Prevention Studies, 13*, 29–57.Google Scholar - Haberman, C. (2017). Overlapping Hot Spots?
*Criminology & Public Policy, 16*(2), 633–660.CrossRefGoogle Scholar - 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.Google Scholar - Johnson, S., Birks, D., McLaughlin, L., Bowers, K., & Pease, K. (2007).
*Prospective crime mapping in operational context, final report*. London: Home Office.Google Scholar - 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.CrossRefGoogle Scholar - Johnson, S., Summers, L., & Pease, K. (2006).
*Vehicle crime: Communicating spatial and temporal patterns, final report*. London: Home Office.Google Scholar - 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.CrossRefGoogle Scholar - 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).Google Scholar - 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.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - Mohler, G. (2014). Marked point process hotspot maps for homicide and gun crime prediction in Chicago.
*International Journal of Forecasting, 30*(3), 491–497.CrossRefGoogle Scholar - Mohler, G. (2015). Event forecasting system. U. S. Patent 13,676,358. 3 February 2015.Google Scholar
- 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.CrossRefGoogle Scholar - Moreto, W., & Caplan, J. M. (2010). Forecasting global maritime piracy utilizing the risk terrain modeling (rtm) approach.
*Rutgers center on public security brief*.Google Scholar - 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.CrossRefGoogle Scholar - 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).Google Scholar - 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).Google Scholar - Onat, I., & Gul, Z. (2018). Terrorism Risk Forecasting by Ideology.
*European Journal on Criminal Policy and Research*, 1–17.Google Scholar - 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.Google Scholar - 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.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - Ratcliffe J. (1999).
*Hotspot detective for MapInfo helpfile version 1.0.*Google Scholar - 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.CrossRefGoogle Scholar - Roberts, A., & LaFree, G. (2004). Explaining Japan’s postwar violent crime trends.
*Criminology, 42*(1), 179–210.CrossRefGoogle Scholar - 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.Google Scholar - 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.CrossRefGoogle Scholar - 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.Google Scholar - Sherman, L., Gartin, P., & Buerger, M. (1989). Hot spots of predatory crime: Routine activities and the criminology of place.
*Criminology, 27*(1), 27–56.CrossRefGoogle Scholar - Shiode, S. (2011). Street-level spatial scan statistic and STAC for Analysing street crime concentrations.
*Transactions in GIS, 15*(3), 365–383.CrossRefGoogle Scholar - Shiode, S., & Shiode, N. (2014). Microscale prediction of near-future crime concentrations with street-level Geosurveillance.
*Geographical Analysis, 46*(4), 435–455.CrossRefGoogle Scholar - 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.CrossRefGoogle Scholar - Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries. A test of the near repeat hypothesis.
*British Journal of Criminology, 43*(3), 615–633.CrossRefGoogle Scholar - 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.Google Scholar - Wang, X., & Brown, D. (2012). The spatio-temporal modeling for criminal incidents.
*Security Informatics, 1*(1), 1–17.CrossRefGoogle Scholar