지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이용수
초록· 키워드
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The objective of this study is to understand relationship between presidential election outcomes and residential real estate characteristics in Seoul metropolitan area. Spatial auto regression model(SAR) and spatial error model(SEM) are used to identify residential real estate characteristics that explain the 2012 Korean presidential election outcomes. By comparing those two spatial regression models to ordinary least square(OLS) model, this study concludes spatial error model(SEM) builds a better model than OLS model. This study also explores local patterns of independent variables included in spatial error model using Geographically weighted regression(GWR). Conclusions drawn from this study are as follows: First, 2012 Korean presidential election outcomes in sub-area of Seoul can be explained through residential real estate characteristics of sub-area of Seoul. Secondly, spatial methods, which controls spatial autocorrelation among spatial units, are required to analyze the election result. Finally, GWR result shows that coefficients on the average age and house size variables are globally related to a positive preference toward the conservative candidate, whereas coefficient on single household ratio variable is globally related to a negative preference to the candidate. Another global coefficient, house deterioration level, is positively related to a preference toward the candidate. GWR result also shows the local coefficient of homeownership variable in Southwestern Seoul, where preferences toward the liberal candidate is highly clustered, is positively and highly correlated to a preference toward the conservative candidate. Local coefficients of multi-family housing ratio variable and number of rooms per household member variable are positively correlated to a preference toward the conservative candidate across Gangnam, Seocho, Songpa, Gangdong Gu’s and localities near the Han River area.
목차
등록된 정보가 없습니다.
참고문헌 (15)
참고문헌 신청
Anselin, L / 1995 / Local indicators of spatial association ? LISA / Geographical Analysis 27 (2) : 93 ~ 115
Anselin, L. / 1992 / Space and Applied Econometrics:Introduction / Regional Science and Urban Economics 22 (3) : 307 ~ 316
Anselin, Luc / 1998 / Spatial dependence in linear regression models with an introduction to spatial econometrics / Statistics Textbooks and Monographs 155 : 237 ~ 290
Brunsdon, C. / 1998 / Geographically weighted regression-modeling spatial non-stationarity / The Statistician 47 (3) : 431 ~ 443
Doreian, P. / 1981 / Estimating linear models with spatially distributed data / Sociological methodology 12 : 359 ~ 388
Anselin, L. / 1992 / Space and Applied Econometrics:Introduction / Regional Science and Urban Economics 22 (3) : 307 ~ 316
Anselin, Luc / 1998 / Spatial dependence in linear regression models with an introduction to spatial econometrics / Statistics Textbooks and Monographs 155 : 237 ~ 290
Brunsdon, C. / 1998 / Geographically weighted regression-modeling spatial non-stationarity / The Statistician 47 (3) : 431 ~ 443
Doreian, P. / 1981 / Estimating linear models with spatially distributed data / Sociological methodology 12 : 359 ~ 388
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