• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.101-109
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• 2011

In most of the urbanized cities, socio-economic attributes tend to cluster as patterns of similarity in space, namely spatial autocorrelation, by agglomeration forces. The classical linear regression model, the most frequently adopted in the trip generation step, cannot sufficiently represent this effect. In order to take into account the effect properly, we need a model which adequately deals with the spatial dependence patterns. In this study, the Geographically Weighted Regression (GWR) model is adopted as an alternative method for the local analysis of relationships in multivariate data sets; that is GWR extends this traditional regression framework by estimating local rather than global parameters. This study shows the existence of spatial effects in the production and attraction of home base/non-home based trips through the GWR model using travel data collected in Daegu metropolitan area. Furthermore, LISA is employed to verify the fact that the local spatial autocorrelation exists.

• Korea journal of population studies
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• v.32 no.3
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• pp.1-20
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• 2009

본 연구는 지리정보시스템(GIS)과 지리적 가중 회귀(GWR)를 이용하여 건강 관련 삶의 질(HRQoL)의 사회인구학적 상관요인에 대한 공간분석을 시도한다. 관찰의 독립성과 오차의 동분산성을 가정하는 전통적 회귀분석과 달리, 지리적 가중 회귀분석은 속성정보뿐만 아니라 공간정보를 활용하는 공간분석 기법이다. 분석모형은 건강 관련 삶의 질을 종합적으로 측정하는 EQ-5D를 종속변수로 하고 지역의 사회인구학적 특성인 노령인구비율, 조이혼율, 병상수, 재정자주도를 독립변수로 하여 구성하였다. 종속변수는 질병관리본부에서 실시한 <지역사회건강조사>의 자료를 이용하였고, 독립변수는 통계청 온라인 DB에 수록된 지역별 자료를 이용하였다. 모형을 추정해 본 결과 전반적으로 사회적 특성보다는 노령인구비율이나 조이혼율과 같은 인구학적 특성이 건강 관련 삶의 질에 더 많은 영향을 미치는 것으로 나타났다. 공간적 변이를 고려하는 지역모형은 전역모형에서 드러나지 않았던 중요한 유형을 보여주는데, 노령인구비율 변수와 조이혼율 변수의 지역별 추정치를 지도상으로 살펴본 결과 변수들의 효과가 공간적 위치에 따라 차이를 보인다는 점이 확인되었다. 분석 결과는 또한 지리적 가중 회귀분석이 전통적 회귀분석에 비해 공간적 자기상관의 문제를 극복하고 모형의 부합도를 증가시킨다는 것을 보여준다.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.1
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• pp.103-109
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• 1990

The application of the Log-Pearson Type m distribution recommended by Water Resources Council, U. S. A. for flood frequency analysis requires the estimation of the regionalized skew coefficient. In this study, regionalized skew coefficients are estimated using a weighted regression model which relates at-site skews based on logarithms of observed annual flood peak series to both basin characteristics and precipitation data in the Han river and the Nakdong river basin. The model is developed with weighted least squares method in which the weights are determined by separating residual variance into that due to model error and due to sampling error. As the result of analysis, regionalized skews are estimated as - 0.732 and - 0.575 in the Han river and the Nakdong river basin, respectively.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.45-54
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• 2023

The settlement prediction during the design phase is primarily conducted using theoretical methods. However, measurement-based settlement prediction methods that predict future settlements based on measured settlement data over time are primarily used during construction due to accuracy issues. Among these methods, the hyperbolic method is commonly used. However, the existing hyperbolic method has accuracy issues and statistical limitations. Therefore, a weighted nonlinear regression hyperbolic method has been proposed. In this study, two weighting methods were applied to the weighted nonlinear regression hyperbolic method to compare and analyze the accuracy of settlement prediction. Measured settlement plate data from two sites located in Busan New Port were used. The settlement of the remaining sections was predicted by setting the regression analysis section to 30%, 50%, and 70% of the total data. Thus, regardless of the weight assignment method, the settlement prediction based on the hyperbolic method demonstrated a remarkable increase in accuracy as the regression analysis section increased. The weighted nonlinear regression hyperbolic method predicted settlement more accurately than the existing linear regression hyperbolic method. In particular, despite a smaller regression analysis section, the weighted nonlinear regression hyperbolic method showed higher settlement prediction performance than the existing linear regression hyperbolic method. Thus, it was confirmed that the weighted nonlinear regression hyperbolic method could predict settlement much faster and more accurately.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.31-38
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• 2000

Relationships between hydrologic variables are often nonlinear. Usually the functional form of such a relationship is not known a priori. A multivariate, nonparametric regression methodology is provided here for approximating the underlying regression function using locally weighted polynomials. Locally weighted polynomials consider the approximation of the target function through a Taylor series expansion of the function in the neighborhood of the point of estimate. The utility of this nonparametric regression approach is demonstrated through an application to nonparametric short term forecasts of the biweekly Great Salt Lake volume.volume.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.173-183
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• 2017

We investigate the factors affecting the price of apartments using the spatial and temporal data of private real estate prices. The factors affecting the price of apartment were analyzed using geographical and temporal weighted regression (GTWR) model which incorporates the temporal and spatial variation. In contrast to the OLS, a general approach used in previous studies, and GWR method which is most widely used for analyzing spatial data, GTWR considers both temporal and spatial characteristics of the house price, and leads to better description of the house price determination. Year of construction and floor area are selected as the significant factors from the analysis, and the house price are affected by them temporally and geographically.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.1-17
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• 2008

The ordinary least squares regression method of Haseman and Elston(1972) is most widely used in genetic linkage studies for continuous traits of sib pairs. Kruglyak and Lander(1995) suggested a statistic which appears to be a nonparametric counterpart to the Haseman and Elston(1972)'s regression method, but in fact these two methods are quite different. In this paper the relationships between these two methods are described and will be compared by simulation studies. One of the characteristics of the sib-pair linkage study is that the explanatory variable has only three different values and thus dependent variable is heavily replicated in each value of the explanatory variable. We propose a weighted least squares regression method which is more appropriate to this situation and the efficiency of the weighted regression in genetic linkage study was explored with normal and non-normal simulated continuous traits data. Simulation studies demonstrated that the weighted regression is more powerful than other tests.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.611-620
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• 2018

With the revitalization of alley market area becoming a major goal of the urban regeneration project, an understanding on customer characteristics that affect the sales of alley market areas is needed. As spatial heterogeneity appears to exist in alley market areas, the use of GWR (Geographically Weighted Regression) is required as an alternative to OLS (Ordinary Least Squares) regression. This study analyzes effects of customer characteristics on sales of 1007 alley market areas in Seoul. Comparing R squared and AICc, results show that GWR is better than OLS regression. According to OLS regression, the ratio of female, the ratio of 40's and 50's, the number of employees, the opening rate of establishment, the density of building and the size of alley market area have positive effects on sales, while the ratio of 20's and 30's, the distance of bus stop and that of subway station have negative effects. As a result of comparing local regression coefficients of geographically weighted regression analysis, the ratio of female customers has the greatest effect on the northwestern region, followed by the southwestern region, the central region and the northeastern region. The ratio of 20's and 30's and that of 40's and 50's effect on the southeastern and northeastern regions, and then the southwestern region. It is expected that this study will help to identify marketing target for each alley market area.

• Proceedings of the Korean Association for Survey Research Conference
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• 2002.06a
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• pp.157-162
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• 2002

패널 무응답자(panel nonrespondent)란 처음 조사에서는 응답을 하였으나 나중 조사에서는 응답을 하지 않은 사람을 의미한다. 패널조사에서는 앞 단계에서의 응답으로부터 뒷 단계의 무응답에 대한 정보를 얻을 수 있다. 무응답에 대한 수정 방법은 어떤 보조 변수들을 선택하고, 그 변수들이 수정하는 데 어떻게 사용하는 가를 결정하는 것이다. 우리는 가중 수정을 패널 무응답자에 대해서만 생각한다. 이러한 가중은 패널 무응답자에 대하여 보상하기 위하여 패널 무응답의 가중값을 수정한다. 종속 변수로서 패널응답 상태(status)는 로지스틱 회귀분석으로 패널 무응답에 대한 모형을 선택하는 방법이다. 로지스틱 회귀분석에서 패널무응답과 상관이 있는 변수들은 패널무응답 편향을 감소시키기 위하여 가중 수정에서 사용하기 위한 변수들이다.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.