Convergence Security Journal (융합보안논문지)

Korea convergence Security Association (한국융합보안학회)
권호 표지

Crime Prediction Model based on Meteorological Changes and Discomfort Index

기상변화 및 불쾌지수에 따른 범죄발생 예측 모델

  • Received : 2014.10.06
  • Accepted : 2014.10.20
  • Published : 2014.10.30
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Abstract

This study analyzed a correlation between crime and meteorological changes and discomfort index of Seoul and p resented a prediction expression through the regression analysis. For data used in this study, crime data from Januar y 2008 to December 2012 of Seoul Metropolitan Police Agency and meteorological records and discomfort index recor ded in the Meteorological Agency through the portal sites were used. Based on this data, SPSS 18.0 was used for the regression analysis and the analysis of correlation between crime and meteorological changes and discomfort index and a prediction expression was derived through the analysis and the risk index was shown in 5 steps depending on predicted values obtained through the prediction expression derived. The risk index of 5 steps classified like this is considered to be used as important data for crime prevention activities.

본 연구는 서울시의 범죄와 기상변화 및 불쾌지수를 상관관계분석을 하고 회귀분석을 통해 예측식을 제시하였다. 본 연구에서 사용된 데이터들은 서울지방경찰청 2008년 1월부터 2012년 12월까지의 범죄데이터와 포털사이트를 통해 기상청에 기록된 기상기록 및 불쾌지수를 사용하였다. 이 데이터를 토대로 범죄와 기상변화 및 불쾌지수의 상관관계분석과 회귀분석을 하기 위해 SPSS 18.0을 활용하였고, 분석을 통해 예측식을 도출하고 도출된 예측식을 통해 얻어진 예측값에 따라 위험지수를 5단계로 나타내었다. 이 같이 구분된 5단계의 위험지수를 통해 범죄예방활동에 중요한 자료로 활용될 것이라 판단된다.

Keywords

References

  1. 이윤호, 김연수, "날씨 및 요일특성과 범죄발생의 관계분석", 한국범죄심리학회지, 2010.
  2. Seong S. Chae, Chansoo Kim, Jong-Min Kim, William D. Warde, "Cluster analysis using different correlation coefficients", Statistical Papers Vol. 49, No.4, 2006, pp.715-727
  3. Ali Abbas, "Statistical Signal Processing Technique for Identification of Different Infected Sitesof the Diseased Lungs", Journal of medical systems Vol. 36, No.3, 2010, pp.1537-1541.
  4. Ozbayoglu G, Ozbayoglu ME (2006) A new approach for the prediction of ash fusion temperatures: a case study using Turkish lignites. Fuel 85:545-552. https://doi.org/10.1016/j.fuel.2004.12.020
  5. Mehmet Bilgili, "Prediction of soil temperature using regression and artificial neural network models", Meteorology and Atmospheric Physics Vol. 110, Numbers.1-2, 2010, pp.59-70 https://doi.org/10.1007/s00703-010-0104-x
  6. K. T. Chau, J. E. Chan, "Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island", Landslides Vol. 2, No. 4, 2005, pp.280-290. https://doi.org/10.1007/s10346-005-0024-x
  7. http://www.kma.go.kr/