• Survey Research
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• v.10 no.3
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• pp.1-19
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• 2009

This study investigates the utilities of spatial analysis in the context of survey research using Geographical Information System(GIS) and Geographically Weighted Regression (GWR) which take account of spatial heterogeneity. Many social phenomena involve spatial dimension, and with the development of GIS, GPS receiver, and online location-based services, spatial information can be collected and utilized more easily, and thus application of spatial analysis in the survey research is getting easier. The traditional OLS regression models which assume independence of observations and homoscedasticity of errors cannot handle spatial dependence problem. GWR is a spatial analysis technique which utilizes spatial information as well as attribute information, and estimated using geographically weighted function under the assumption that spatially close cases are more related than distant cases. Residential survey data from a Primary Autonomous District are used to estimate a model of public service satisfaction. The findings show that GWR handles the problem of spatial auto-correlation and increases goodness-of-fit of model. Visualization of spatial variance of effects of the independent variables using GIS allows us to investigate effects and relationships of those variables more closely and extensively. Furthermore, GIS and GWR analyses provide us a more effective way of identifying locations where the effect of variable is exceptionally low or high, and thus finding policy implications for social development.

• Journal of the Economic Geographical Society of Korea
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• v.14 no.1
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• pp.33-51
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• 2011

This study investigates the spatial characteristics of the provision of and demand for the private tutoring service industries and the consumer groups. For the purpose, we analyze the spatial characteristics of various types of tutoring institutes in the Seoul Metropolitan area. In particular, we exam the spatial distribution patterns of attendants of tutoring institutes by institution type as well as the resident population by attendant age group. By applying spatial autocorrelation analysis, we examine the spatial clustering patterns of tutoring institutes and attendants by type. The results show distinct differences in the spatial distribution patterns by tutoring institute type as well as by attendant age group. We found significant socio-economic variables which influence on the spatial distribution of tutoring institutes. Finally, we propose private tutoring service provision models constructed with these variables through multiple regression analysis.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.1
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• pp.1-16
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• 2013

This study investigates the spatial distribution characteristics of Korean fashion industries during the last decade, in which the economic geography of fashion industries has changed dynamically with economic globalization and "thus resulted in increased" demand "of" diversification. In particular, this study examines the spatial distribution patterns of fashion industries in the Seoul metropolitan area where fashion industries are highly agglomerated. For the purpose, this study applies Moran's I Index of spatial autocorrelation analysis for seven functional sectors of fashion industries related to fashion production. The global and local agglomeration patterns are examined for each functional sector. The results clarify the distinction in the spatial agglomeration patterns among the seven functional sectors of fashion industries in the Seoul Metropolitan area. Logit models are developed to examine the interrelationships among functional sectors in their spatial agglomeration distribution patterns. By conducting binary logistic regression analysis, we find out how the spatial agglomeration of each functional sector is related to the others.

• Spatial Information Research
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• v.18 no.5
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• pp.107-117
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• 2010

Taking the East-Hwasung area as the case, this study first builds gridded population data based on the municipal population survey raw data, and then measures, by way of GIS tools, the major urban spatial variables that are thought to influence the composition of the regional population. For the purpose of comparison, the urban models based on the Genetic Algorithm technique and the regression technique are constructed using the same input variables. The findings indicate that the GA output performed better in differentiating the effective variables among the pilot model variables, and predicted as much consistent and meaningful coefficient estimates for the explanatory variables as the regression models. The study results indicate that GA technique could be a very useful and supplementary research tool in understanding the urban phenomena.

• Journal of Korean Academy of Nursing
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• v.53 no.4
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• pp.426-438
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• 2023

Purpose: Walking practice is a representative indicator of the level of physical activity of local residents. Although the world health organization addressed reduction in prevalence of insufficient physical activity as a global target, the rate of walking practice in Korea has not improved and there are large regional disparities. Therefore, this study aimed to explore the spatial variations of walking practice and its associated factors in Korea. Methods: A secondary analysis was conducted using Community Health Outcome and Health Determinants Database 1.3 from Korea Centers for Disease Control and Prevention. A total of 229 districts was included in the analysis. We compared the ordinary least squares (OLS) and the geographically weighted regression (GWR) to explore the associated factors of walking practice. MGWR 2.2.1 software was used to explore the spatial distribution of walking practice and modeling the GWR. Results: Walking practice had spatial variations across the country. The results showed that the GWR model had better accommodation of spatial autocorrelation than the OLS model. The GWR results indicated that different predictors of walking practice across regions of Korea. Conclusion: The findings of this study may provide insight to nursing researchers, health professionals, and policy makers in planning health programs to promote walking practices in their respective communities.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.271-280
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• 2005

Kriging methods as traditional spatial data analysis methods and geographical weighted regression models as statistical analysis methods are compared. In this paper, we apply data from the Ministry of Environment to spatial analysis for practical study. We compare these methods to performance with monthly carbon monoxide observations taken at 116 measuring area of air pollution in 1999.

• 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.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.741-746
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• 2016

Albedo is one of the climate variables that modulate absorption of solar energy, and its retrieval is important process for climate change study. High spatial resolution and long-term consistent periods are important considerations in order to efficiently use the retrieved albedo data. This study retrieved surface broadband albedo based on Landsat 8 as high resolution which is consistent with Landsat 7. First of all, we analyzed consistency of Landsat 7 channel and Landsat 8 channel. As a result, correlation coefficient(R) on all channels is average 0.96. Based on this analysis, we used multiple linear regression model using Landsat 7 albedo, which is being used in many studies, and Landsat 8 reflectance channel data. The regression coefficients of each channel calculated by regression analysis were used to derive a formula for converting the Landsat 8 reflectance channel data to broadband albedo. After Landsat 8 albedo calculated using the derived formula is compared with Landsat 7 albedo data, we confirmed consistency of two satellite using Root Mean Square Error (RMSE), R-square ($R^2$) and bias. As a result, $R^2$ is 0.89 and RMSE is 0.003 between Landsat 7 albedo and Landsat 8 albedo.

• 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 the Economic Geographical Society of Korea
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• v.16 no.3
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• pp.512-527
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• 2013

This study examines the spatial distribution characteristics of financial industry which has been a necessary service for contemporary urban life. In particular, we analyze the spatial distribution patterns of money lending business which is considered with informal financial services as well as the spatial distribution patterns of banks which are representative of the institutional financial services. For the purpose, their density distribution patterns are explored by Kernel density analysis for both financial services in first. Moran's I coefficients are estimated for these two financial services to clarify the distintion in their geographical concentration patterns. The results of spatial autocorrelation analysis show stark differences between the center city and outskirts of the Seoul metropolitan area. Multivariate regression models are developed to explain the relationships between the spatial distributions of financial services and geographical variables. Finally, we discuss financial exclusion problem in the Metropolitan Seoul based on these spatial distribution characteristics.