베이지언 계층 모델을 이용한 도시 주거범죄의 사회 경제적 분석
An Analysis of Urban Economic and Social Status Effects to Residential Crimes using Bayesian Hierarchical Modeling
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초록
본 연구에서는 2000년 미국 오하이오주 콜럼버스시에서 발생한 도시주거범죄를 베이지언 계층 모델로 분석하였다. 도시 주거범죄는 대표적 도시문제로 해당 지역의 사회 인구적 특성 및 문제를 반영한다. 도시 주거범죄와 같은 공간 데이터는 공간 의존성(spatial dependence)과 이질성(heterogeneity) 같은 공간적인 속성들을 내재하는데, 이런 공간적 속성들은 일반적인 회귀분석에서 편향되고 비효율적인 모수값을 추정하게 한다. 그러나 본 연구는 공간데이터 분석과 관련된 문제들을 공간 의존적 요소와 이질성 요소를 설명하는 베이지언 계층모델을 이용하여 해결하였다. 본 연구는 (1) 도시 주거범죄를 해당 지역의 사회 인구적 측면에서 분석하고 (2) 공간 의존성과 이질성 같이 계량적 분석에서 문제가 되는 요소들을 처리하며 (3) 지도분석기법(map decomposition strategy)을 통하여 중요한 인자들의 공간적 특성을 제시한다. 그 결과 빈곤, 주거 안정성, 범죄 기회, 공간적인 특성들이 도시 주거범죄의 중요한 요인임을 보여준다.
Abstract
This study proposes an application of Bayesian hierarchical analysis to urban residential crimes in Columbus, Ohio that occurred in 2000. As one of the major urban problems urban residential crimes reflect underlying socio-demographic issues. Intrinsic spatial features of urban crime data, such as spatial dependence and heterogeneity, cause biased and/or inefficient estimations of parameters when estimated by standard ordinary least square models. This study overcomes the problems of spatial data analyses, applying a Bayesian hierarchical model, which includes a spatial dependence component and a heterogeneity component. This study (1) analyzes urban residential crimes in the perspective of neighborhood socio-demographic status, (2) overcomes limitation of the spatial quantitative data analysis by handling spatial dependence and heterogeneity, and (3) visualizes spatial effects of important factors in the analysis using a map decomposition strategy. Results indicate poverty, residential stability, crime opportunity, and spatial effects (e.g. spatial dependence and heterogeneity) are important factors estimating residential crimes.
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