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http://dx.doi.org/10.11108/kagis.2019.22.4.072

Probabilistic Prediction of the Risk of Sexual Crimes Using Weight of Evidence  

KIM, Bo-Eun (Chungcheongbuk-do Disaster Safety Research Institute)
KIM, Young-Hoon (Department of Geography Education, Korea National University of Education)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.22, no.4, 2019 , pp. 72-85 More about this Journal
Abstract
The goal of this study is to predict sexual violence crimes, which is an routine risk. The study used to the Weight of Evidence on sexual violence crimes that occurred in partly Cheongju-si for five years from 2011 to 2015. The results are as follows. First, application and analysis of the Weight of Evidence that considers the weight of evidence characteristics showed 8 out of total 26 evidences that are used for a sexual violence crimes risk prediction. The evidences were residential area, date of use permission for building, individual housing price, floor area ratio, number of basement floor, lot area, security light and recreational facility; which satisfied credibility in the process of calculating weight. Second, The weight calculated 8 evidences were combined to create the prediction map in the end. The map showed that 16.5% of sexual violence crimes probability occurs in 0.3㎢, which is 3.3% of the map. The area of probability of 34.5% is 1.8㎢, which is 19.0% of the map and the area of probability of 75.5% is 2.0㎢, which is 20.7% of the map. This study derived the probability of occurrence of sexual violence crime risk and environmental factors or conditions that could reduce it. Such results could be used as basic data for devising preemptive measures to minimize sexual violence, such as police activities to prevent crimes.
Keywords
Sexual Violence Crimes; Prediction; Risk; Weight of Evidence;
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1 Roh, S.H. 2015. Testing the predictability of crime forecasting models using spatio-temporal analysis and risk terrainmodeling. Korean Criminological Review. 26(3):239-266
2 Roncek, D.W. and P.A. Maier. 1991. Bar, blocks and crimes revisited: Linking the theory of routine activities to the empiricism of hot spots. Criminology. 29(4):725-753.   DOI
3 Shimada, T. 2004. Spatial diffusion of residential burglaries in Tokyo: Using exploratory spatial data analysis. Behaviormetrika. 31(2):169-181.   DOI
4 Tak, H.S., J.H. Park, J.S. Choeng and J.W. Yoon. 2015. Building crime prevention system utilizing big data(II). Korean Institute of Criminology. Seoul, Korea pp. 183-200
5 Yerxa, M. 2013. Evaluating the temporal parameters of risk terrain modeling with residential burglary. Crime Mapping. 5(1):7-38.
6 Yoon, H.S., H.W. Chun, C.S. Yang, B.S. Kim and K.B. Kim. 2014. Building Crime Prevention System Utilizing Big Data( I ). Korean Institute of Criminology. Seoul, Korea. pp.170-188
7 Yoo, Y.W. and T.K. Baek. 2017. Construction of urban crime prediction model based on census using GWR. Journal of the korean association of Geographic information studies, 20(4):65-76   DOI
8 Anderson, M.A. 2006. A spatial analysis of crime in Vancuouver, British Columbia: A synthesis of social disorganization and routine activity theory. the canadian geographer/Le G'eographe canadien. 50(4):487-502.   DOI
9 Anselin, L., 2002. Under the hood: Issues in the specification and interpretation of spatial regression models. Agricultural Economics. 27:247-267.   DOI
10 Baek, S.G. and D.H. Jang. 2013. Potential mapping of mountainous wetlands using Weights of Evidence model in Yeongnam Area, Korea. Journal of the Korean geomorphological association. 20(1):21-33
11 Bonham-Carter, G.F., F.P. Agterberg and D.F. Wright. 1988. Integration of geological datasets for gold exploration in Nova Scotia. Photogrammetic Engineering and Remote Sensing. 54(11):1585-1592.
12 Bonham-Carter, G.F. 1994. Geographic Information Systems for geoscientist: modelling with GIS. Pergamon Press, Oxford, UK. pp.267-337.
13 Caplan, J.M., L.W. Kennedy and J. Mille. 2011. Risk terrain modeling: brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly. 28(2):360-381.   DOI
14 Cheong, J.S. 2013. Spatial analyses on structural causal factors of serious crime. The Journal of Police Science. 13(4):53-78
15 Cheong, J.S. and E.G. Hwang. 2010. A macro-level study on the cause of homicide rate: Nationwide analysis using spatial regression model. Korean Criminological Review. 22(1):157-184   DOI
16 Cheong, J.S., S.M. Jeong and B.H. Lee. 2015. A study on the ecological factors of sex crime: Focusing on the structural characteristics of Eup-Myon-Dong Districts. The Journal of Police Science. 15(1):3-31
17 Cho, I.H. and G.H. Kwon. 2011. Analysis of effectiveness of sex crimes prevention policy in Seoul: Focusing on panel data regression model considering Heteroscedasticity. The korea Local Administration Review. 25(2):439-468
18 Choi, M.J. and K.S. Noh. 2016. Exploratory study on crime prevention based on bigdata convergence: Through case studies of Seongnam City. Journal of Digital Convergence. 14(11):125-133   DOI
19 Dolan, P. and T. Peasgood. 2007. Estimating the economic and social costs of the fear of crime. British Journal of Criminology. 47(1):121-132.   DOI
20 Drawve, G. 2016. A metric comparison of predictive hot spot techniques and RTM. Justice Quarterly, 33(3):369-397.   DOI
21 Dugato, M. 2013. Assessing the validity of risk terrain modeling in a European city: Preventing robberies in the city of Milan. Crime Mapping: A Journal of Research and Practice. 5(1):63-89.
22 Huh, K.M. 2004. A study on the characteristics of elderly crime victims. Korean Associatin of Public Safety and Criminal Justice Review. 17(17):423-452
23 Gabor, T. and T.K. Griffith. 1980. The assessment of community vulnerability to acute hazardous materials incidents. Journal of Hazardous Materials. 3(4):323-333.   DOI
24 Harris, J.R., L. Wilkinson, K. Heather and S. Fumerton. 2001. Application of GIS processing techniques for producing mineral prospectivity Maps-A case Study: Mesothernal Au in the Swayze Greenstone Belt, Ontario, Canada. Natural Resources Research. 10(2):91-124.   DOI
25 Heo, S.Y. and T.H. Moon. 2013. Analysis of urban environmental impact factors in crime hotsopt. Journal of Korea Planners Association. 48(6):223-234
26 Hwang, J.T. 2010. Estimating the amounts of some major hidden crime 2008 in Korea. Korean Criminological Review. 21(3):7-51
27 Kim, S.E. 2011. Fear of crime and forting up of the residential community. Korean Criminological Review. 22(4):315-346
28 Hwang, S.Y. and C.S. Hwang. 2003. The spatial pattern analysis of urban crimes using GIS: The case of residential burglary. Journal of Korea Planners Association. 38(1):53-66
29 Jeong, K.S., T.H. Moon, J.H. Jeong and S.Y. Heo. 2009. Analysis of spatiotemporal pattern of urban crime and its influencing factors. Journal of the korean association of Geographic information studies. 12(1):12-25
30 Jung, S.W. and K.H. Lee. 2015. An analysis of environmental factors affecting the sexual assault in metropolitan cities. Journal of the Architectural Institute of Korea-Planning & Design. 31(11):179-186   DOI
31 Lee, G.J. and Y.S. Jeon. 1994. A study on crime and criminal victimization of the old. Korean Institute of Criminology. Seoul, Korea pp.114-124
32 Kim, T.M. 2011. Realities of sexual violence and it's countermeasures. Korean Criminological Review. 22(3):5-44
33 Korean National Police Agency. 2017. Korean Police Crime Statistics in 2017. Korean National Plice Agency. Seoul, Korea. pp.32-37
34 Lee, G.H., C.W. Jin, J.W. Kim and W.H. Kim. 2016. A study on the characteristics of the spatial distribution of sex crimes: Spatial analysis based on environmental criminology. Journal of the korean Geographical Society. 51(6):853-871
35 Lee, K.H. 2002. A study on policing for crime prevention: Centering on its evaluation in the U.S. Korean Criminological Review. 14(2):149-183
36 Lee, S.C. 2015. An exploratory study on the relationship between facility circumstance and violent crime in regions. The Korean Association of Police Science Review. 17(2):127-155
37 Murray, T.A., I. McGuffog, S.J. Western and P. Mullins. 2001. Exploratory spatial data analysis techniques for examining urban crime. British Journal of Criminology. 41(2):309-329.   DOI
38 Lee, S.R., H.J. Oh and K.D. Min. 2006. Sedimentary type non-metallic mineral potential analysis using GIS and Weight of Evidence model in the Gangreung Area. Journal of Korea Spatial Information Society. 14(1):129-150
39 Lee, S.W. 2004. Implication of the urban plan on the crime occurrence. Seoul Development Institute. Seoul, Korea. pp. 11-21
40 Messner, S.F. and L. Anselin. 2004. Spatial analyses of homicide with areal data. In: M. Goodchild and D. Janelle(Ed.). Spatially Integrated Social Science. Oxfords University Press, New York, USA. pp.127-141.
41 Noe, C.S. and D.H. Kim. 2012. A crime occurrence risk probability map generation model based on the Markov Chain. Journal of Korean Institute Technology. 10(10):89-98
42 Park, K.R. 2013. A study of the probability of prediction to crime according to time Status change. Journal of the Korea Society of Computer and Information. 18(5):147-156   DOI
43 Park, K.R., S.D. Kim, S.R. Choi and J.H. Lee. 2010. Law and economics of crime and criminal justice policy in Korea(II): Estimates of the social cost of crime in Korea. Korean Institute of Criminology. Seoul, Korea. pp.239-307
44 Park, S.H., S.S. Kim and M.W. Kang. 2002. Regression Analysis. Korea National Open University Press, Seoul, Korea. pp.224-229
45 Police Science Institute. 2019. Public Security Prospect 2019. Police Science Institute. Chungcheongnam-do, Korea. pp.66-77
46 Porwal, A. 2001. Extended Weights-of-Evidence modelling for predictive mapping of base metal deposit potential in Aravalli Province, Western India. Exploration and Mining Geology. 10(4):273-287.   DOI