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http://dx.doi.org/10.7848/ksgpc.2017.35.1.31

Exploring Spatial Patterns of Theft Crimes Using Geographically Weighted Regression  

Yoo, Youngwoo (Dept. of Urban Engineering, Dongeui University)
Baek, Taekyung (Dept. of Urban Engineering, Dongeui University)
Kim, Jinsoo (Dep. of Spatial Information Engineering, Pukyong National University)
Park, Soyoung (Graduate School of Earth Environmental Hazard System, Pukyong National University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.35, no.1, 2017 , pp. 31-39 More about this Journal
Abstract
The goal of this study was to efficiently analyze the relationships of the number of thefts with related factors, considering the spatial patterns of theft crimes. Theft crime data for a 5-year period (2009-2013) were collected from Haeundae Police Station. A logarithmic transformation was performed to ensure an effective statistical analysis and the number of theft crimes was used as the dependent variable. Related factors were selected through a literature review and divided into social, environmental, and defensive factors. Seven factors, were selected as independent variables: the numbers of foreigners, aged persons, single households, companies, entertainment venues, community security centers, and CCTV (Closed-Circuit Television) systems. OLS (Ordinary Least Squares) and GWR (Geographically Weighted Regression) were used to analyze the relationship between the dependent variable and independent variables. In the GWR results, each independent variable had regression coefficients that differed by location over the study area. The GWR model calculated local values for, and could explain the relationships between, variables more efficiently than the OLS model. Additionally, the adjusted R square value of the GWR model was 10% higher than that of the OLS model, and the GWR model produced a AICc (Corrected Akaike Information Criterion) value that was lower by 230, as well as lower Moran's I values. From these results, it was concluded that the GWR model was more robust in explaining the relationship between the number of thefts and the factors related to theft crime.
Keywords
Theft Crime; Spatial Pattern; Ordinary Least Squares; Geographically Weighted Regression;
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