• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.1
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• pp.67-79
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• 2013

For the sake of the Transit-Oriented Development that has been prominent recently, an analysis of the spatial structures of transit centers, above all, should be carried out at a local level. This study, thus, analyzes the spatial structures of subway influence areas by applying a Geographically Weighted Regression (GWR) model to individual parcels. As a result of the validity analysis of the model, it has turned out that the subway influence areas have different characteristics respectively, and there is spatial heterogeneity even in the same single area. Also, the result of the comparison among models has proved that the GWR model is more adequate than the Ordinary Least Square (OLS) model and $R^2$ has been also increased in the GWR model. Then, the results have been mapped by means of the GIS, which have made it possible to understand the spatial structures at a local level. If the Transit-Oriented Development is fulfilled in consideration of the spatial structural characteristics of the subway influence areas drawn respectively from the model analysis, it will be helpful in adopting effective policies.

• 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.

• Spatial Information Research
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• v.23 no.5
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• pp.65-73
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• 2015

Newtown Business of Seoul had been performed to reduce deterioration of Gangbuk and economic inequality between Gangnam and Gangbuk. According to this, Eunpyeong-gu was set as test-bed for Newtown business and Newtown business had been completed until 2013. This study aims to analyze the influence of social and economical factors which affect land price using GWR (Geographically Weighted Regression) considered spatial effect. As a result of analysis, GWR model demonstrated a better goodness-of-fit than OLS (Ordinary least square) model typically used in most study. Furthermore, AIC value and Moran's I of residual prove that GWR model is more suitable than OLS model. GWR model enable to explain more detailed than global regression model as coefficient and sign show different value locally. In future, this research will be helpful to develop Eunpyeong-gu considering spatial characters and strength effectiveness of development.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.917-934
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• 2014

Geographically weighted regression(GWR) model has been widely used to estimate spatially heterogeneous real estate prices. The GWR model, however, has some limitations of the selection of different price determinants over space and the restricted number of observations for local estimation. Alternatively, the geographically weighted LASSO(GWL) model has been recently introduced and received a growing interest. In this paper, we attempt to explore various local price determinants for the real estate by utilizing the GWL and its applicability to forecasting the real estate price. To do this, we developed the three hedonic models of OLS, GWR, and GWL focusing on the sales price of apartments in Seoul and compared those models in terms of model fit, prediction, and multicollinearity. As a result, local models appeared to be better than the global OLS on the whole, and in particular, the GWL appeared to be more explanatory and predictable than other models. Moreover, the GWL enabled to provide spatially different sets of price determinants which no multicollinearity exists. The GWL helps select the significant sets of independent variables from a high dimensional dataset, and hence will be a useful technique for large and complex spatial big data.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.65-76
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• 2017

The purpose of this study was to present a prediction model that reflects crime risk area analysis, including factors and spatial characteristics, as a precursor to preparing an alternative plan for crime prevention and design. This analysis of criminal cases in high-risk areas revealed clusters in which approximately 25% of the cases within the study area occurred, distributed evenly throughout the region. This means that using a multiple linear regression model might overestimate the crime rate in some regions and underestimate in others. It also suggests that the number of deserted houses in an analyzed region has a negative relationship with the dependent variable, based on the multiple linear regression model results, and can also have different influences depending on the region. These results reveal that closure signs in a study area affect the dependent variable differently, depending on the region, rather than a simple or direct relationship with the dependent variable, as indicated by the results of the multiple linear regression model.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.57-63
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• 2012

The ordinary least square (OLS) regression model is assumed that the relationship between distribution of longevity population and environmental factors to be identical. Therefore, the OLS regression analysis can't explain sufficiently the spatial characteristics of longevity phenomenon and related variables. The geographically weighted regression (GWR) model can be representing the spatial relationship of adjacent area using geographically weighted function. It also characterized which can locally explain the spatial variation of distribution of longevity population by environmental characteristics. From this point of view, this study was performed the comparative analysis between OLS and GWR model for ecological factors of longevity existing studies. In the results, GWR model has higher corresponded to model than OLS model and can be accounting for spatial variability about effect of specific environmental variables.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.43-51
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• 2013

This study aims to examine the presence of non-stationary relationship between water quality and land use in watersheds. In investigating the relationships between land use and water quality, most previous studies adopted OLS method which is assumed stationarity. However, this approach is difficult to capture the local variation of the relationships. We used 146 sampling data and land cover data of Korean Ministry of Environment to build conventional regressions and GWR models for BOD, TN and TP. Regression model and GWR models of BOD, TN, TP were compared with $R^2$, AICc and Moran's I. The results of comparisons and descriptive statistics of GWR models strongly indicated the presence of Non-Stationarity between water quality and land use.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.68-79
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• 2021

The recent occurrence of vacant houses in urban areas is a remarkable social problem. One of the physical declines, the occurrence of vacant houses, accelerates various social and economic declines, such as a decline in population and a slump in the commercial district. Vacant houses have regional characteristics and spatial influence, and it is necessary to approach them locally in order to grasp the exact status of vacant houses. Therefore, in this study, the effect of urban decline on the occurrence of vacant homes was examined by region using global Moran's I and Geographic Weighted Regression(GWR) model. As a result of the analysis, there were spatial autocorrelation and heterogeneity in the occurrence of vacant houses in each eup·myeon·dong, Busan metropolitan city. In addition, there is a difference in the influence of each variable of urban decline on the occurrence of vacant houses, and even the same variable of urban decline has different effects on the occurrence of vacant houses in different regions. Therefore, it is expected that a more efficient vacant home management plan can be presented if the GWR model is used to analyze the coefficient values differentiated by region and categorize the occurrence of vacant houses.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.223-235
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• 2011

Although ordinary least squares (OLS) regression analysis can be used to examine the spatial relationships between environmental equity and urban quality of life, this global method may mask the local variations in the relationships between them. These geographical variations can not be captured without using local methods. In this context, this paper explores the spatially varying relationships between environmental equity and urban quality of life across the Atlanta metropolitan area by geographically weighted regression (GWR), a local method. Environmental equity and urban quality of life were quantified with an integrated approach of GIS and remote sensing. Results show that generally, there is a negatively significant relationship between them over the Atlanta metropolitan area. The results also suggest that the relationships between environmental equity and urban quality of life vary significantly over space and the GWR (local) model is a significant improvement on the OLS (global) model for the Atlanta metropolitan area.

• 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.