• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.343-351
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• 2014

The correlation between regional Walkability Index and their physical socio-economic characteristics has evaluated by the spatial statistical analysis to understand the urban pedestrian environments, where has been emerging the significance, recently. Following to the study, the Walkability Indexes were calculated quantitatively from two administrative districts of Busan and measured Global Local spatial autocorrelation indices. Additionally, the Geographically Weighted Regression model was applied to define the correlation between Walkability Indexes and urban environmental variables. The spatial autocorrelation values and clusters on the Walkability Indexes were derived in statistically significant level. Furthermore, the Geographically Weighted Regression model has been derived more improved inference than the OLS regression model, so as the influence of local level pedestrian environment was identified. The results of this study suggest that the spatial statistical approach can be effective on quantitative assessing the pedestrian environment and navigating their associated factors.

• 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 Urban Science
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• v.9 no.1
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• pp.41-49
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• 2020

This study analyzed the factors affecting economic growth using multiple regression model and Geographically Weighted Regression in consideration of population, industry and employment, housing and political characteristics on economic growth by region. The analysis results are summarized as follows. First, the total employment growth rate, manufacturing employment growth rate, local election turnout and the level of party consensus between the central and local governments are having a positive impact on regional economic growth. Second, according to the GWR analysis, the population has a positive impact on economic growth in the southern region of Korea, and the increase in the total number of employees has a positive impact on the southern region of Gyeonggi Province, Gangwon Province, North Chungcheong Province and North Gyeongsang Province. Finally, the voter turnout of urbanites is positively affecting economic growth in South Chungcheong Province, Gangwon Province and the southern coast, while North Jeolla and South Jeolla provinces have a positive impact on economic growth as the parties of the central and local governments are equal. The results of this study may suggest the role of local government for regional economic development.

• 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.

• Environmental Analysis Health and Toxicology
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• v.29
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• pp.5.1-5.7
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• 2014

Objectives Numerous studies have revealed the adverse health effects of acute and chronic exposure to particulate matter less than $10{\mu}m$ in aerodynamic diameter ($PM_{10}$). The aim of the present study was to examine the spatial distribution of $PM_{10}$ concentrations and cardiovascular mortality and to investigate the spatial correlation between $PM_{10}$ and cardiovascular mortality using spatial scan statistic (SaTScan) and a regression model. Methods From 2008 to 2010, the spatial distribution of $PM_{10}$ in the Seoul metropolitan area was examined via kriging. In addition, a group of cardiovascular mortality cases was analyzed using SaTScan-based cluster exploration. Geographically weighted regression (GWR) was applied to investigate the correlation between $PM_{10}$ concentrations and cardiovascular mortality. Results An examination of the regional distribution of the cardiovascular mortality was higher in provincial districts (gu) belonging to Incheon and the northern part of Gyeonggi-do than in other regions. In a comparison of $PM_{10}$ concentrations and mortality cluster (MC) regions, all those belonging to MC 1 and MC 2 were found to belong to particulate matter (PM) 1 and PM 2 with high concentrations of air pollutants. In addition, the GWR showed that $PM_{10}$ has a statistically significant relation to cardiovascular mortality. Conclusions To investigate the relation between air pollution and health impact, spatial analyses can be utilized based on kriging, cluster exploration, and GWR for a more systematic and quantitative analysis. It has been proven that cardiovascular mortality is spatially related to the concentration of $PM_{10}$.

• 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.

• Health Policy and Management
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• v.26 no.4
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• pp.271-278
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• 2016

Background: This study purposed to analyze the relationship between regional obesity rates and regional variables. Methods: Data was collected from the Korean Statistical Information Service (KOSIS) and Community Health Survey in 2012. The units of analysis were administrative districts such as city, county, and district. The dependent variable was the age-sex adjusted regional obesity rates. The independent variables were selected to represent four aspects of regions: health behaviour factor, psychological factor, socio-economic factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis model, this study applied geographically weighted regression (GWR) analysis to calculate the regression coefficients for each region. Results: The OLS results showed that there were significant differences in regional obesity rates in high-risk drinking, walking, depression, and financial independence. The GWR results showed that the size of regression coefficients in independent variables was differed by regions. Conclusion: Our results can help in providing useful information for health policy makers. Regional characteristics should be considered when allocating health resources and developing health-related programs.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.75-81
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• 2016

This study focused on exploring real transaction price of apartment and spatial and temporal heterogeneity of the variables that influence real transaction price of apartment from the spatial and temporal perspective. As independent variables that are considered to influence real transaction price of apartment, transport, local characteristics, educational conditions, population, and economic characteristics were taken into account. Accordingly, the influence of independent variables and spatial distribution pattern were analyzed from the global and local aspects. The spatial and temporal changing patterns of real transaction price of apartment which is a dependent variable were analyzed. First, to establish an analysis model, OLS analysis and GWR analysis were conducted, and thereby more efficient and proper model was selected. Secondly, to find spatial and temporal heterogeneity of independent variables with the use of the selected GWR model, Local $R^2$ was used for local analysis. Thirdly, to look into spatial distribution of independent variables, kriging analysis was carried out. Therefore, based on the results, it is considered that it is possible to carry out more microscopic housing submarket analysis and lay the foundation for establishing a policy on real property.

• 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.