DOI QR코드

DOI QR Code

위험요인이 포함된 시공간 모형을 이용한 5대 강력범죄 분석

Spatio-temporal analysis with risk factors for five major violent crimes

  • 전영은 (영남대학교 통계학과) ;
  • 강석복 (영남대학교 통계학과) ;
  • 서정인 (국립안동대학교 정보통계학과)
  • Jeon, Young Eun (Department of Statistics, Yeungnam University) ;
  • Kang, Suk-Bok (Department of Statistics, Yeungnam University) ;
  • Seo, Jung-In (Department of Information Statistics, Andong National University)
  • 투고 : 2022.05.04
  • 심사 : 2022.06.28
  • 발행 : 2022.10.31

초록

5대 강력범죄(살인, 강도, 강간·강제추행, 절도, 폭력)는 사회 구성원들의 안전을 위협하는 대표 범죄들로 일상생활에서 자주 발생한다. 이러한 범죄들은 사회 구성원들의 삶의 질을 떨어뜨리는 등 부정적인 영향을 미친다. 대한민국의 수도인 서울의 경우, 지방에 있는 많은 인구가 서울로 이동하면서 서울의 인구 밀도는 증가하고, 이로 인해 5대 강력범죄 발생 위험성도 증가하고 있다. 본 연구에서는 이러한 위험성을 줄이기 위해 세 가지의 시공간 모형을 이용하여 서울의 5대 강력범죄 발생에 대한 상대위험도를 모델링하였다. 게다가, 상대위험도에 유의한 영향을 미치는 위험요인을 살펴보기 위해 다양한 위험요인을 포함하였다. 최적의 모형을 선택하기 위해 편차정보기준을 이용하였으며, 최적의 모형을 중심으로 다양한 시각화를 포함한 분석결과를 제공하였다. 본 연구는 각 자치구의 상대위험도와 5대 강력범죄에 대한 위험에 유의한 영향을 미치는 위험요인을 분석함으로써, 사람들의 안전한 일상생활을 유지하기 위한 효율적인 전략을 수립하는 데 도움을 준다.

The five major violent crimes including murder, robbery, rape·forced indecent act, theft, and violence are representative crimes that threaten the safety of members of society and occur frequently in real life. These crimes have negative effects such as lowering the quality of citizens' life. In the case of Seoul, the capital of Korea, the risk for the five major violent crimes is increasing because the population density of Seoul is increasing as a large number of people in the provinces move to Seoul. In this study, to reduce this risk, the relative risk for the occurrence of the five major violent crimes in Seoul is modeled using three spatio-temporal models. In addition, various risk factors are included to identify factors that significantly affect the relative risk of the five major violent crimes. The best model is selected in terms of the deviance information criterion, and the analysis results including various visualizations for the best model are provided. This study will help to establish efficient strategies to sustain people's safe everyday living by analyzing important risk factors affecting the risk of the five major violent crimes and the relative risk of each region.

키워드

과제정보

이 논문은 안동대학교 기본연구지원사업에 의하여 연구되었음.

참고문헌

  1. Bernardinelli L, Clayton D, Pascutto C, Montomoli C, Ghislandi M, and Songini M (1995). Bayesian analysis of space-time variation in disease risk, Statistics in Medicine, 14, 2433-2443. https://doi.org/10.1002/sim.4780142112
  2. Besag J, York J, and Mollie A (1991). Bayesian image restoration, with two applications in spatial statistics, Annals of the Institute of Statistical Mathematics, 43, 1-59. https://doi.org/10.1007/BF00116466
  3. Cho JT and Park J (2016). The effects of crime and fear of crime upon happiness of Seoul citizens, Seoul Studies, 17, 131-144. https://doi.org/10.23129/SEOULS.17.4.201612.131
  4. Jang JH (2018). Study on one-person household and incidence of crime: Based on panel data of 25 districts in Seoul, Seoul Studies, 19, 87-110.
  5. Jang YS, Kim SJ, and Cheong JS (2014). The effect of crime victimization and fear of crime on quality of life, The Journal of Police Science, 14, 33-65. https://doi.org/10.22816/POLSCI.2014.14.3.002
  6. Knorr-Held L (2000). Bayesian modelling of inseparable space-time variation in disease risk, Statistics in Medicin e, 19, 2555-2567. https://doi.org/10.1002/1097-0258(20000915/30)19:17/18<2555::AID-SIM587>3.0.CO;2-#
  7. Kwon TY and Jeon S (2016). A study on the violent crime and control factors in Korea, Journal of the Korean Data & Information Science Society, 27, 1511-1523. https://doi.org/10.7465/jkdi.2016.27.6.1511
  8. Kim HJ and Lee SW (2011). Determinants of 5 major crimes in Seoul metropolitan area: Application of mixed GWR model, Seoul Studies, 12, 137-155. https://doi.org/10.23129/SEOULS.12.4.201112.137
  9. Meng CYK and Dempster AP (1987). A Bayesian approach to the multiplicity problem for significance testing with binomial data, Biometrics, 43, 301-311. https://doi.org/10.2307/2531814
  10. Park H (2018). Spatial analysis of factors affecting fear of crime, Korean Criminological Review, 29, 91-117.
  11. Persad RA (2019). Bayesian space-time analysis of brain cancer incidence in Southern Ontario, Canada: 2010-2013, Medical Sciences, 7, 110. https://doi.org/10.3390/medsci7120110
  12. Spiegelhalter DJ, Best NG, Carlin BP, and Van Der Linde A (2002). Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64, 583-639. https://doi.org/10.1111/1467-9868.00353