CrimeStat을 이용한 핫스팟 분석: 잠재적 위험요인인 인구를 대리하는 변수로서 물리적 무질서의 사용

Abstract

As the interests in neighborhood crime prevention increase, so does the interests in GIS and geo‐statistical techniques. In particular, hot spot analysis has been getting lots of attention as an adequate alternative for effective and efficient distribution of the police resources. Unfortunately, however, little scientific research has been performed in Korean Criminal Justice field. Moreover, most crime hot spot studies that have been carried out in the areas of Urban Studies, Geography, and Construction Engineering have been constrained to the technique of detecting high‐volume area. In light of the fact that detecting high‐risk area can have better implications both for theory and practice, this study attempted to apply Nearest Neighbor Hierarchical Clustering routine (NNH) to identify high‐volume area and Risk‐adjusted Nearest Neighbor Hierarchical Clustering routine (RNNH) to detect high‐risk area. Based on the broken windows theory, this study adopted physical disorder as the proxy measure for underlying population at risk, especially for the analysis of RNNH. Four years of data from 1998‐2001 of a mid‐sized Midwestern city in North America were analyzed, and the results showed that there is much difference between high‐volume areas and high‐risk areas, and those crime hot spots tend to move year by year. Although this study was not performed in Korean contexts, it was expected that more future studies could utilize the NNH and RNNH to detect more exact crime hot spots, which further could help the crime prevention efforts of Korean law enforcement agencies.