- Short Contribution
- Open Access
- Published:
Near-repeat victimization of sex crimes and threat incidents against women and girls in Tokyo, Japan
Crime Science volume 9, Article number: 5 (2020)
Abstract
Near-repeat victimization (NRV) is a phenomenon in which there is a greater likelihood for a subsequent crime to occur within close spatiotemporal proximity of the last occurrence of a similar crime. This study investigated the NRV of sex crimes and threat incidents against women and girls in Tokyo. An analysis using the Knox ratio showed significant near-repeat patterns of sex crimes and two types of threat incidents, with the exception of threat incidents with physical contact against girls. Additionally, the tendency of NRV was revealed as being stronger when the victims were girls.
Introduction
Near-repeat victimization (NRV) is a phenomenon wherein it is likelier for a subsequent crime to occur within close spatiotemporal proximity of a similar crime’s last occurrence. NRV has consistently been verified since Townsley et al. (2003) first empirically confirmed it. Although earlier studies focused on the NRV of residential burglary, recent ones examined its relation to various crime types (Table 1).
NRV presumes that offender(s) repeatedly select a space–time region where more vulnerable criminal targets are concentrated or where a similar crime had been successfully perpetrated in the past, thus avoiding the risk of being arrested and obtaining the maximum benefits as an “optimal forager” (Johnson and Bowers 2004; Townsley et al. 2003).
Optimal foraging is applicable to impersonal sex-related crimes, and sex criminals tend to repeat sex crimes during short periods of time and in small areas, using rational and consistent methods to select their location (Beauregard Proulx and Rossmo 2005; Ceccato 2014; Hewitt and Beauregard 2014; Leclerc et al. 2016; Rebocho and Silva 2014; Tokyo Metropolitan Police Department [TMPD] 2017). Thus, NRV might be linked to sex-related crimes, and if so, crime prevention measures can be implemented more effectively by considering NRV, such as rapidly sharing information on sex-related crime cases with neighbors using email, SNS, or app push notifications (TMPD 2017).
However, previous studies have not verified NRV in sex-related crimes (Table 1), and this study aims to fill this gap.
Data and methods
Data
Data were obtained from the TMPD’s official records on sex crimes (2011–2016) and “threat incidents” in public spaces (2014–2016). Public space in this study includes roads, parks, parking areas, and accessible areas at shopping centers or housing complexes.
The sex crime categories used in this research comprised rape and indecent assaults between unacquainted victims and offenders, and threat incidents referred to an incident that posed a threat to women or girls, such as molestation. Although not criminally punished, threat incidents are viewed as precursors to sex crimes, and this perspective spearheaded the implementation of countermeasures in Japan (Kikuchi 2015). Eight types of threat incidents were analyzed and divided into those with and those without physical contact (Table 2).
This study used different data sources for sex crimes and threat incidents. Official penal code crime records were used to analyze sex crimes, and, as threat incidents are not covered by the penal code, data on the number of police responses for service requests were analyzed. Thus, this study analyzed sex crimes and threat incidents separately.
The variables for sex crimes and threat incidents were as follows: incident type, incident date/time, location latitude/longitude, geocoding accuracy, and victim’s sex/age. We used sex crime and threat incident data on female victims with limited geographically accurate occurrence positions at city block (called ban) or parcel (called go) levels (Table 2). The geocoding hit rates at these levels were between 94.7% (threat incident with physical contact against girls) and 97.3% (sex crimes against girls).
The frequency of sex crimes and threat incidents varied according to the victim’s age. Therefore, data were analyzed separately for women and girls. Girls in this study are those under the age of 13; this bifurcation in age is often used in Japan to distinguish between crimes against children and those against adults.
Analytical methods
“NearRepeat,” the R package provided by Steenbeek (2018), was used to analyze the NRV of sex crimes and threat incidents. NearRepeat calculates Knox ratios at specific spatiotemporal ranges from the most recent previous event and their statistical significance using a Monte Carlo simulation. The Knox ratio is an indicator used by Johnson et al. (2007) by improving the spatiotemporal clustering test (Knox test) proposed by Knox (1964). The indicator is calculated by enumerating the occurrences of pairs within a specific time difference and distance difference among the pairs and dividing the value by the expected value on the assumption that each event occurred independently in time and space. The Knox ratio has generally been used as an indicator of the strength of NRV in previous studies (see Table 1).
Regarding spatiotemporal thresholds to calculate Knox ratios, previous studies mostly used 7 or 14 days and 100 to 150 m as predetermined thresholds considering weekly periodicity of crime occurrence and the average size of city blocks. This study used 7 days as the temporal threshold, as sex-related crimes could be assumed to be periodic within a week. Regarding the spatial threshold, considering the average nearest neighbor distance of the representative point of city blocks in Tokyo’s urban area (“Urban Promotion Area” in Tokyo) to be 46.65 m, this study used 100 m, following previous studies.
Results
Figure 1 illustrates the Knox ratio in the predetermined spatiotemporal range with 999 Monte Carlo simulations. NRV can be identified from the magnitude and statistical significance of the value represented in the smallest spatiotemporal range and its decaying pattern with distance and time.
As illustrated in Fig. 1, using Ratcliffe’s (2009) criteria of NRV, a Knox ratio larger than 1.20 and p-value less than 0.05, significant NRV was present for sex crimes and two threat incident types less than 100 m and fewer than 7 days from the last crime, with the exception of threat incidents with physical contact against girls. The Knox ratios for all other sex crimes and threat incidents are maximal in an area less than 100 m and time of fewer than 7 days, and they tend to decay from the spatiotemporal range.
Comparing the different sets of data, while referring to the smallest spatiotemporal range, the Knox ratios on sex crimes and threat incidents without physical contact against girls were larger than those against women.
Discussion and conclusion
This study extended the scope of NRV hypotheses to sex crimes and threat incidents against women and girls. As a result, significant near-repeat patterns of sex crimes and two types of threat incidents were identified. This may contribute to the generalization of the NRV hypothesis. Although the NRV of threat incidents with physical contact against girls was not clear, the calculated Knox ratio could become unstable because of the low frequency of incidents.
Additionally, it was revealed that the tendency of NRV was stronger when the victims were girls. This result may be interpreted as being due to the vulnerability of girls, as girls are less likely to access crime-related information than women and have fewer possibilities for actions against crime; consequently, sufficient measures will not be taken after a crime. Therefore, girls are more likely to be repeatedly targeted than women. More generally, it may be hypothesized that NRV is stronger in more vulnerable victims. This hypothesis is indirectly supported by several studies that found that the NRV of some types of property crimes is more likely to occur in vulnerable areas (Lockwood 2012; Zhang et al. 2015; Nobles et al. 2016; Piza and Carter 2018). Future research should develop this topic to generalize the relationship.
Sex-related crimes against women and girls represent an important issue in contemporary Japan because they are not decreasing compared to other crimes (TMPD 2017). As sexual assaults cause serious negative psychological and behavioral consequences for their victims, various measures, including foot patrols by residents and email alerts from the local police, have been introduced in Japan (TMPD 2017). These measures can be evaluated positively as situational crime prevention strategies, which are known to be effective against the NRV of residential burglaries (Grove et al. 2012).
While this research aimed to describe NRV, explaining its overall mechanisms is beyond the scope of this brief report. It is necessary to identify NRV mechanisms regarding sex crimes and threat incidents to strengthen the theoretical foundation further. Additionally, one fundamental problem of sex crimes is that it contains many unreported cases. More accurate estimation of NRV combined with data sources other than police records, such as crime victimization surveys, is a future challenge.
Availability of data and materials
The data were provided by the Tokyo Metropolitan Police Department to the advisory board members of “Keishicho kodomo josei no anzentaisaku ni kannsuru yuusikisya kenkyukai” (The advisory board of crime prevention measures for women and children). The data are not available because they are confidential.
Abbreviations
- NRV:
-
Near-repeat victimization
- TMPD:
-
Tokyo Metropolitan Police Department
References
Beauregard, E., Proulx, J., & Rossmo, D. K. (2005). Spatial patterns of sex offenders: theoretical, empirical, and practical issues. Aggression and Violent Behavior,10(5), 579–603. https://doi.org/10.1016/j.avb.2004.12.003.
Block, S., & Fujita, S. (2013). Patterns of near repeat temporary and permanent motor vehicle thefts. Crime Prevention and Community Safety,15(2), 151–167. https://doi.org/10.1057/cpcs.2013.1.
Bowers, K. J., & Johnson, S. D. (2004). Who commits near repeats? a test of the boost explanation. Western Criminology Review,5(3), 12–24.
Bowers, K. J., & Johnson, S. D. (2005). Domestic burglary repeats and space-time clusters: the dimensions of risk. European Journal of Criminology,2(1), 67–92. https://doi.org/10.1177/1477370805048631.
Ceccato, V. (2014). The nature of rape places. Journal of Environmental Psychology,40, 97–107. https://doi.org/10.1016/j.jenvp.2014.05.006.
Chainey, S. P., & da Silva, B. F. A. (2016). Examining the extent of repeat and near repeat victimisation of domestic burglaries in Belo Horizonte, Brazil. Crime Science,5(1), 1. https://doi.org/10.1186/s40163-016-0049-6.
Chen, P., Yuan, H., & Li, D. (2013). Space-time analysis of burglary in Beijing. Security Journal,26(1), 1–15. https://doi.org/10.1057/sj.2011.4.
de Melo, S. N., Andresen, M. A., & Matias, L. F. (2018). Repeat and near-repeat victimization in Campinas, Brazil: new explanations from the Global South. Security Journal,31(1), 364–380. https://doi.org/10.1057/s41284-017-0105-2.
Glasner, P., Johnson, S. D., & Leitner, M. (2018). A comparative analysis to forecast apartment burglaries in Vienna, Austria, based on repeat and near repeat victimization. Crime Science,7(1), 9. https://doi.org/10.1186/s40163-018-0083-7.
Glasner, P., & Leitner, M. (2017). Evaluating the impact the weekday has on near-repeat victimization: a spatio-temporal analysis of street robberies in the city of Vienna. Austria. ISPRS International Journal of Geo-Information,6(1), 3. https://doi.org/10.3390/ijgi6010003.
Grove, L. E., Farrell, G., Farrington, D. P., & Johnson, S. D. (2012). Preventing repeat victimization: A systematic review. Brottsförebyggande rådet/The Swedish National Council for Crime Prevention.
Grubb, J. A., & Nobles, M. R. (2016). A spatiotemporal analysis of arson. Journal of Research in Crime and Delinquency,53(1), 66–92. https://doi.org/10.1177/0022427815590858.
Grubesic, T. H., & Mack, E. A. (2008). Spatio-temporal interaction of urban crime. Journal of Quantitative Criminology,24(3), 285–306. https://doi.org/10.1007/s10940-008-9047-5.
Haberman, C. P., & Ratcliffe, J. H. (2012). The predictive policing challenges of near repeat armed street robberies. Policing: A Journal of Policy and Practice, 6(2), 151–166. https://doi.org/10.1093/police/pas012.
Hewitt, A., & Beauregard, E. (2014). Sexual crime and place: the impact of the environmental context on sexual assault outcomes. Journal of Criminal Justice,42(5), 375–383. https://doi.org/10.1016/j.jcrimjus.2014.05.003.
Hino, K., & Amemiya, M. (2019). Spatiotemporal analysis of burglary in multifamily housing in Fukuoka City, Japan. Cities,90, 15–23. https://doi.org/10.1016/j.cities.2019.01.030.
Hoppe, L., & Gerell, M. (2019). Near-repeat burglary patterns in Malmö: stability and change over time. European Journal of Criminology,16(1), 3–17. https://doi.org/10.1177/1477370817751382.
Johnson, S. D., Bernasco, W., Bowers, K. J., Elffers, H., Ratcliffe, J., Rengert, G., et al. (2007). Space–time patterns of risk: a cross national assessment of residential burglary victimization. Journal of Quantitative Criminology,23(3), 201–219. https://doi.org/10.1007/s10940-007-9025-3.
Johnson, S. D., & Bowers, K. J. (2004). The burglary as clue to the future: the beginnings of prospective hot-spotting. European Journal of Criminology,1(2), 237–255. https://doi.org/10.1177/1477370804041252.
Johnson, S. D., Summers, L., & Pease, K. (2006). Vehicle crime: Communicating spatial and temporal patterns. http://discovery.ucl.ac.uk/1430754/. Accessed 05 October 2019.
Johnson, S. D., Summers, L., & Pease, K. (2009). Offender as forager? A direct test of the boost account of victimization. Journal of Quantitative Criminology,25(2), 181–200. https://doi.org/10.1007/s10940-008-9060-8.
Kikuchi, G. (2015). Precursor events of sex crimes in Japan: a spatio-temporal analysis of reports of contacts with suspicious persons by target age groups. International Journal of Criminal Justice Sciences,10(2), 122–138.
Knox, G. (1964). Epidemiology of childhood leukaemia in Northumberland and Durham. British Journal of Preventive & Social Medicine,18(1), 17–24. https://doi.org/10.1136/jech.18.1.17.
Leclerc, B., Chiu, Y. N., Cale, J., & Cook, A. (2016). Sexual violence against women through the lens of environmental criminology: toward the accumulation of evidence-based knowledge and crime prevention. European Journal on Criminal Policy and Research,22(4), 593–617. https://doi.org/10.1007/s10610-015-9300-z.
Lockwood, B. (2012). The presence and nature of a near-repeat pattern of motor vehicle theft. Security Journal,25(1), 38–56. https://doi.org/10.1057/sj.2011.5.
Marchione, E., & Johnson, S. D. (2013). Spatial, temporal and spatio-temporal patterns of maritime piracy. Journal of Research in Crime and Delinquency,50(4), 504–524. https://doi.org/10.1177/0022427812469113.
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. https://doi.org/10.1080/07418825.2012.754921.
Nobles, M. R., Ward, J. T., & Tillyer, R. (2016). The impact of neighborhood context on spatiotemporal patterns of burglary. Journal of Research in Crime and Delinquency,53(5), 711–740. https://doi.org/10.1177/0022427816647991.
Piza, E. L., & Carter, J. G. (2018). Predicting initiator and near repeat events in spatiotemporal crime patterns: an analysis of residential burglary and motor vehicle theft. Justice Quarterly,35(5), 842–870. https://doi.org/10.1080/07418825.2017.1342854.
Ratcliffe, J. H. (2009). Near repeat calculator: Program manual for version 1.3. https://liberalarts.temple.edu/sites/liberalarts/files/NearRepeatsManual.pdf.
Ratcliffe, J. H., & Rengert, G. F. (2008). Near-repeat patterns in Philadelphia shootings. Security Journal,21(1–2), 58–76. https://doi.org/10.1057/palgrave.sj.8350068.
Rebocho, M. F., & Silva, P. (2014). Target selection in rapists: the role of environmental and contextual factors. Aggression and Violent Behavior,19(1), 42–49. https://doi.org/10.1016/j.avb.2013.12.003.
Steenbeek, W. (2018). Near Repeat, R package version 0.1.0. https://github.com/wsteenbeek/NearRepeat.
Sturup, J., Rostami, A., Gerell, M., & Sandholm, A. (2018). Near-repeat shootings in contemporary Sweden 2011 to 2015. Security Journal,31(1), 73–92. https://doi.org/10.1057/s41284-017-0089-y.
Tokyo Metropolitan Police Department. (2017). Keishicho kodomo josei no anzentaisaku ni kannsuru yuusikisya kennkyukai teigensyo (Recommendation for children and women’s safety by advisory panel). Tokyo Metropolitan Police Department. https://www.keishicho.metro.tokyo.jp/kurashi/anzen/anshin/kodomo_josei_anzen.html. Accessed 05 October 2019.
Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries. A test of the near repeat hypothesis. British Journal of Criminology, 43(3), 615–633. https://doi.org/10.1093/bjc/43.3.615.
Turchan, B., Grubb, J. A., Pizarro, J. M., & McGarrell, E. F. (2019). Arson in an urban setting: A multi-event near repeat chain analysis in Flint, Michigan. Security Journal,32(3), 179–197. https://doi.org/10.1057/s41284-018-0155-0.
Wang, Z., & Liu, X. (2017). Analysis of burglary hot spots and near-repeat victimization in a large Chinese city. ISPRS International Journal of Geo-Information,6(5), 148. https://doi.org/10.3390/ijgi6050148.
Wells, W., Wu, L., & Ye, X. (2012). Patterns of near-repeat gun assaults in Houston. Journal of Research in Crime and Delinquency,49(2), 186–212. https://doi.org/10.1177/0022427810397946.
Wu, L., Xu, X., Ye, X., & Zhu, X. (2015). Repeat and near-repeat burglaries and offender involvement in a large Chinese city. Cartography and Geographic Information Science,42(2), 178–189. https://doi.org/10.1080/15230406.2014.991426.
Ye, X., Xu, X., Lee, J., Zhu, X., & Wu, L. (2015). Space-time interaction of residential burglaries in Wuhan, China. Applied Geography,60, 210–216. https://doi.org/10.1016/j.apgeog.2014.11.022.
Youstin, T. J., Nobles, M. R., Ward, J. T., & Cook, C. L. (2011). Assessing the generalizability of the near repeat phenomenon. Criminal Justice and Behavior,38(10), 1042–1063. https://doi.org/10.1177/0093854811417551.
Zhang, Y., Zhao, J., Ren, L., & Hoover, L. (2015). Space-time clustering of crime events and neighborhood characteristics in Houston. Criminal Justice Review,40(3), 340–360. https://doi.org/10.1177/0734016815573309.
Acknowledgements
Not applicable.
Funding
This work was supported by MEXT KAKENHI Grant Number 17H02046.
Author information
Authors and Affiliations
Contributions
MA analyzed and interpreted the crime data and wrote the first draft of the manuscript. TN and TS advised on the analysis method and provided a criminological theoretical basis for interpretation. TN and TS also contributed to the manuscript revision. All authors read and approved the final manuscript.
Authors’ information
MA is an Associate Professor in the Division of Policy and Planning Sciences, Faculty of Engineering, Information and Systems, University of Tsukuba. TN is a Professor in the Department of Frontier Science for Advanced Environment, Graduate School of Environmental Studies, Tohoku University. TS is the Head of the Crime Prevention Section of the Department of Criminology and Behavioral Sciences, National Research Institute of Police Science.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Amemiya, M., Nakaya, T. & Shimada, T. Near-repeat victimization of sex crimes and threat incidents against women and girls in Tokyo, Japan. Crime Sci 9, 5 (2020). https://doi.org/10.1186/s40163-020-00114-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s40163-020-00114-9
Keywords
- Near-repeat victimization
- Sex crime
- Threat incident
- Women
- Girls
- Tokyo