Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
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Hotspot Analysis of Urban Crime Using Space-Time Scan Statistics

시공간검정통계량을 이용한 도시범죄의 핫스팟분석

  • 정경석 (경남발전연구원 남해안발전연구지원센터) ;
  • 문태헌 (경상대학교 도시공학과, 2단계 BK21 및 공학연구원) ;
  • 정재희 (경남발전연구원)
  • Received : 2010.05.20
  • Accepted : 2010.07.20
  • Published : 2010.09.30
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Abstract

The aim of this study is to investigate crime hotspot areas using the spatio-temporal cluster analysis which is possible to search simultaneously time range as well as space range as an alternative method of existing hotspot analysis only identifying crime occurrence distribution patterns in urban area. As for research method, first, crime data were collected from criminal registers provided by official police authority in M city, Gyeongnam and crime occurrence patterns were drafted on a map by using Geographic Information Systems(GIS). Second, by utilizing Ripley K-function and Space-Time Scan Statistics analysis, the spatio-temporal distribution of crime was examined. The results showed that the risk of crime was significantly clustered at relatively few places and the spatio-temporal clustered areas of crime were different from those predicted by existing spatial hotspot analysis such as kernel density analysis and k-means clustering analysis. Finally, it is expected that the results of this study can be not only utilized as a valuable reference data for establishing urban planning and crime prevention through environmental design(CPTED), but also made available for the allocation of police resources and the improvement of public security services.

본 연구의 목적은 공간적 분포 특성만을 고려하고 있는 기존의 핫스팟분석에 대한 대안적인 방법으로서 공간상에서 나타나는 사건간의 인과관계를 시간영역으로까지 확장하여 동시적 분석이 가능한 시공간분석 방법을 제안하는 것이다. 분석방법으로는 먼저 지리정보시스템을 이용하여 지방중소도시인 M시의 범죄자료를 데이터화 하였고, Ripley K함수와 시공간검정통계량 분석을 통해 M시의 범죄분포 패턴을 지도화 하였다. 연구결과, 범죄위험도가 유의미하게 높은 지역들이 나타났으며, 이들 시공간적 범죄 집중지역들은 기존의 공간분포만을 고려한 범죄분포 패턴과는 다소 차이가 있음을 발견할 수 있었다. 본 연구결과는 시공간적인 범죄분포 특성에 맞는 맞춤형의 경찰 인력 배치와 배분, 그리고 치안행정 서비스 등의 조정을 위한 참고자료로서, 또한 시공간적인 집중을 보이는 이들 지역을 중심으로 물리적 환경 변화의 유도와 공간이용의 개선 효과를 통해 범죄율을 줄여나가는 범죄예방 활동 및 정책수립을 위한 기초자료로도 유용하게 활용될 수 있을 것으로 기대된다.

Keywords

References

  1. 손학기, 박기호. 2008. 부동산 가격변동 핫스팟 탐색을 위한 공간통계기법. 대한지리학회지 43(3):392-411.
  2. 정경석, 문태헌, 정재희, 허선영. 2009. GIS와 공간통계기법을 이용한 시공간적 도시범죄 패턴 및 범죄발생 영향요인 분석. 한국지리정보학회지 12(1):12-25.
  3. 정경석. 2010. 공간범죄통합분석모형을 이용한 도시범죄의 시공간적 분포 특성 및 영향요인 분석. 경상대학교 대학원 박사학위논문. 151쪽.
  4. 황선영, 황철수. 2003. GIS를 활용한 도시 범죄의 공간패턴분석. 국토계획 38(1):53-66.
  5. Andersen, 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 Geographe canadien 50(4):487-502. https://doi.org/10.1111/j.1541-0064.2006.00159.x
  6. Anselin, L. 1992. Spatial data analysis with GIS: An introduction to application in the social sciences. NCGIA Technical Report, pp.92-10.
  7. Diggle, P.J., R. Haggkvist and S.E. Morris. 1995. Second-order Analysis of Space-Time Clustering. Statistical Methods in Medical Research 4:124-136. https://doi.org/10.1177/096228029500400203
  8. Eck, J.E., S. Chainey, J.G. Cameron, M. Leitner and R.E. Wilson. 2005. Mapping Crime: Understanding Hot Spots. U.S Department of Justice Office of Justice Programs National Institute of Justice, pp.23-25.
  9. Goldsmith, V., P. MGuire, J. Mollenkpof and T. Ross. 2000. Analyzing Crime Patterns: Frontiers of Practice. Thousand Oaks, Sage, pp.3-12.
  10. Hirschfield, A., P. Brown and P. Tod. 1995. GIS and the Analysis of Spatiallyreferenced Crime Data: Experiences in Merseyside, UK. International Journal of Geographical Information Systems 12:191-210.
  11. Jacquez, G.M. 1996. A k Nearest Neighbour Test For Space-Time in Interaction. Statistics in Medicine 15:1935-1949. https://doi.org/10.1002/(SICI)1097-0258(19960930)15:18<1935::AID-SIM406>3.0.CO;2-I
  12. Kang, H.J. 2009. Detecting agglomeration processes using space-time clustering analyses. The Annals of Regional Science 45(2):291-311.
  13. Knox, E.G. 1964. Epidemiology of Childhood Lukaemia in Northumberland and Durham. British Journal of Preventive and Social Medicine 18:17-24.
  14. Kulldorff, M. and N. Nagarwalla. 1995. Spatial disease clusters: Detection and Inference. Statistics in Medicine 14:799-810. https://doi.org/10.1002/sim.4780140809
  15. Kulldorff, M. 1997. A Spatial Scan Statistic. Communications in Statistics-Theory and Methods 26:1481-1496. https://doi.org/10.1080/03610929708831995
  16. Kulldorff, M. 2001. Prospective Time-periodic Geographical Disease Surveillance using a Scan Statistic. Journal of the Royal Statistical Society A164:61-72.
  17. Kulldorff, M., R. Heffernan, J. Hartman, R. Assuno and F. Mostashari. 2005. A Space-Time Permutation Scan Statistic for Disease Outbreak Detection. PLos Medicine 2(3):216-224.
  18. Levine, N. 2005. CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Location. Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington DC. Chapter9. 8pp.
  19. Mantel, N. 1967. The Detection of Disease Clustering and a Generalised Regression Approach. Cancer Research 27:209-220.
  20. Murray, T.A., I. McGuffog, S.J. Western, and P. Mullins. 2001. Exploratory Spatial Data Hotspot Analysis of Urban Crime Using Space-28 Time Scan Statistics Analysis Techniques for Examining Urban Crime. British Journal of Criminology 41(2):309-329. https://doi.org/10.1093/bjc/41.2.309
  21. Ripley, B.D. 1976. The Second-Order Analysis of Stationary Point Processes. Journal of Applied Probability 13:255-266. https://doi.org/10.2307/3212829
  22. Shimada, T. 2004. Spatial Diffusion of Residential Burglaries in Tokyo: Using Exploratory Spatial Data Analysis. Behaviormetrika 31(2):169-181. https://doi.org/10.2333/bhmk.31.169
  23. Weisburd, D. 1997. Reorienting Crime Prevention Research and Policy: From the Causes of Criminality to the Context of Crime, National Institute of Justice, Washington, DC.

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