A Study on Model for Social Return for the Prevention of Recidivism of Sexual Violence Criminals Based on Big Data

doi:10.15683/kosdi.2021.9.30.535

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2021 Vol.17, Issue 3 Preview Page Next Page

Original Article

A Study on Model for Social Return for the Prevention of Recidivism of Sexual Violence Criminals Based on Big Data 빅데이터 기반 성폭력범죄자 재범방지를 위한 사회지원모델에 관한 연구: 오세연*
Sei Youen Oh*; Associate professor, Department of Police Administration, Semyung University, Jecheon, Republic of Korea

30 September 2021. pp. 535-542

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Abstract

Purpose: The purpose of this study is to prevent recidivism by recognizing the seriousness of recidivism against sexual offenders under the age of 13 and providing customized social adaptation services based on risk. Method: The study evaluate the efficiency of existing models and proposed model systems, and compare and review what features and operational differences exist from existing models. Result: The proposed model will collect data from related agencies on sexual violence offenders with a high risk of recidivism and classify them into three risk groups through risk algorithms to provide social adaptation services for each risk group. In addition, by monitoring primary social support matching data, storing and re-analyzing the results data to rematch social support services, the model differs from the existing model in preventing recidivism of sexual violence offenders from a long-term perspective. Conclusion: The proposed model of this study is meaningful in that it proposed the basic data of a response system to prevent recidivism from a long-term perspective of sexual offenders with the highest risk of recidivism by collecting and analyzing data on sexual offenders.

Keywords

Big Data

Sexual Violence Offenders Under the Age of 13

Social Support Module

Data Analysis Module

Risk Analysis

Recidivism Prevention

연구목적: 본 연구는 성폭력범죄자의 재범 증가에 따른 심각성을 인지하고, 위험성 정도에 따른 맞춤형 사회지원서비스를 제공하여 형벌보다는 회복과 복지 중심의 사회적응을 강화하여 재범의 피해를 최소화하기 위함이다. 연구방법: 기존모델과 본 연구에서 제안한 모델 시스템에 대한 효율성을 평가하고, 기존의 모델과는 어떠한 기능과 운영상에 차이가 있는 지를 비교·검토 하였다. 연구결과: 제안모델은 13세 미만 대상 성폭력범죄자에 대한 관련기관의 데이터를 수집하고, 위험성 알고리즘 등을 통해 세 가지 위험군으로 분류하여 각각의 위험군에 맞는 사회지원서비스를 제공하게 된다. 1차 사회지원서비스 매칭 데이터를 모니터링 하여 결과데이터로 저장·재분석하고 사회지원서비스를 재 매칭하게 함으로써 기존모델과 운영상의 차이점을 두었다. 결론: 본 연구의 제안모델은 재범위험 가능성이 높은 성폭력범죄자에 대한 장기적인 관점에서 재범의 피해를 최소화 방할 수 있는 사회지원모델 시스템의 기초자료를 제안하였다는데 그 의의가 있다.

키워드

빅데이터

13세 미만 대상 성폭력사범

사회지원모듈

데이터분석모듈

위험성분석

재범방지

References

Berk, R. (2017). "An impact assessment of machine learning risk forecasts on parole board decisions and recidivism." Journal of Experimental Criminology, Vol. 13, No. 2, pp. 259-294. 10.1007/s11292-017-9286-2
Brayne, S. (2017). "Big data surveillance: The case of policing." Journal of American Sociological Review, Vol. 82, No. 5, pp. 977-1008. 10.1177/0003122417725865
Cho, H.W., Do, K.J., Yoo, B.S., Park, J.S. (2021). "A study on an AI platform construction for the prevention of Ex‐prisoners recidivism based on big data." Journal of Collection Review, Vol. 70, pp.1-29. 10.35422/cwsk.2021.70.1
Choi, J.H. (2018). "The data-based prediction of police calls using machine learning." Journal of Korean Big Data Review, Vol. 23 No. 2, pp. 101-112. 10.36498/kbigdt.2018.3.2.101
Choi, M.J., Roh, K.S. (2016). "Exploratory study on crime prevention based on bigdata convergence - Through case studies of Seongnam City -." Journal of Digital Convergence Studies, Vol. 14, No. 11, pp. 125-133. 10.14400/JDC.2016.14.11.125
Heo, S.Y., Kim, J.R., Moon, T.H. (2017). "Crime incident prediction model based on bayesian probability." Journal of Korean Association of Geographic Information Studies, Vol. 20, No. 4, pp. 89-101.
https://www.data.go.kr/data/15032491/fileData.do
http://www.sisafocus.co.kr/news/articleView.html?idxno=249120
Jung, J.Y. (2019). "A comparative analysis of big data policing activities by the US and Korean Police." Journal of Korean Police Science Review, Vol. 18, No. 3, pp. 165-186. 10.38084/2019.18.3.7
Kim, B.S. (2018). "A study on the protection system of abuse victims." Journal of Collection Review, Vol. 28, No. 5, pp. 170-202.
Kim, D.H., Kim, T.H., Kwak, D.K. (2014). "A study on introducing intelligent electronic monitoring system through the analysis of the electronic supervision" Journal of Korean Society of Disaster Information, Vol. 10, No. 3, pp. 374-387. 10.15683/kosdi.2014.10.3.374
Kim, D.Y. (2021). "A study on the development of a fire site risk prediction model based on initial information using big data analysis." Journal of Korean Society of Disaster Information, Vol. 17, No. 2, pp. 245-253.
Kim, J.S., Kim, M.Y., Han, M.K., Jo, B.C. (2019). A Study on the Trend and Trend of Sex Offences in Children and Adolescents. Ministry of Gender Equality and Family, Korea.
Oh, S.Y., Lee, A.R. (2018). "Study of prevention system against second-convictions of sexual violence offenders by using smart electronic monitoring anklet." Journal of Advanced Information Technology and Convergence, Vol. 8, No. 2, pp. 69-77. 10.14801/JAITC.2018.8.2.69
Roh, S.H. (2015). "Testing the predictability of crime forecasting models using spatio-temporal analysis and risk terrain modeling." Journal of Korean Criminological Review, Vol. 26, No. 3, pp. 239-266.

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 17
No :3
Pages :535-542
DOI :https://doi.org/10.15683/kosdi.2021.9.30.535

[1] Berk, R. (2017). "An impact assessment of machine learning risk forecasts on parole board decisions and recidivism." Journal of Experimental Criminology, Vol. 13, No. 2, pp. 259-294. 10.1007/s11292-017-9286-2

[2] Brayne, S. (2017). "Big data surveillance: The case of policing." Journal of American Sociological Review, Vol. 82, No. 5, pp. 977-1008. 10.1177/0003122417725865

[3] Cho, H.W., Do, K.J., Yoo, B.S., Park, J.S. (2021). "A study on an AI platform construction for the prevention of Ex‐prisoners recidivism based on big data." Journal of Collection Review, Vol. 70, pp.1-29. 10.35422/cwsk.2021.70.1

[4] Choi, J.H. (2018). "The data-based prediction of police calls using machine learning." Journal of Korean Big Data Review, Vol. 23 No. 2, pp. 101-112. 10.36498/kbigdt.2018.3.2.101

[5] Choi, M.J., Roh, K.S. (2016). "Exploratory study on crime prevention based on bigdata convergence - Through case studies of Seongnam City -." Journal of Digital Convergence Studies, Vol. 14, No. 11, pp. 125-133. 10.14400/JDC.2016.14.11.125

[6] Heo, S.Y., Kim, J.R., Moon, T.H. (2017). "Crime incident prediction model based on bayesian probability." Journal of Korean Association of Geographic Information Studies, Vol. 20, No. 4, pp. 89-101.

[7] https://www.data.go.kr/data/15032491/fileData.do

[8] http://www.sisafocus.co.kr/news/articleView.html?idxno=249120

[9] Jung, J.Y. (2019). "A comparative analysis of big data policing activities by the US and Korean Police." Journal of Korean Police Science Review, Vol. 18, No. 3, pp. 165-186. 10.38084/2019.18.3.7

[10] Kim, B.S. (2018). "A study on the protection system of abuse victims." Journal of Collection Review, Vol. 28, No. 5, pp. 170-202.

[11] Kim, D.H., Kim, T.H., Kwak, D.K. (2014). "A study on introducing intelligent electronic monitoring system through the analysis of the electronic supervision" Journal of Korean Society of Disaster Information, Vol. 10, No. 3, pp. 374-387. 10.15683/kosdi.2014.10.3.374

[12] Kim, D.Y. (2021). "A study on the development of a fire site risk prediction model based on initial information using big data analysis." Journal of Korean Society of Disaster Information, Vol. 17, No. 2, pp. 245-253.

[13] Kim, J.S., Kim, M.Y., Han, M.K., Jo, B.C. (2019). A Study on the Trend and Trend of Sex Offences in Children and Adolescents. Ministry of Gender Equality and Family, Korea.

[14] Oh, S.Y., Lee, A.R. (2018). "Study of prevention system against second-convictions of sexual violence offenders by using smart electronic monitoring anklet." Journal of Advanced Information Technology and Convergence, Vol. 8, No. 2, pp. 69-77. 10.14801/JAITC.2018.8.2.69

[15] Roh, S.H. (2015). "Testing the predictability of crime forecasting models using spatio-temporal analysis and risk terrain modeling." Journal of Korean Criminological Review, Vol. 26, No. 3, pp. 239-266.

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

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