Applying Crime Prediction Techniques to Japan: A Comparison Between Risk Terrain Modeling and Other Methods
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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.
KeywordsCrime prediction Crime mapping Risk terrain modeling Self-exciting point process Prospective mapping
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.
This study was funded by a grant from the Nikkoso Research Foundation for Safe Society in 2016 and JSPS Grant Number JP17H02046.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
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