• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.147-159
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• 2014

Public transportation ridership and walkability of urban district can be enhanced through high quality of TOD(Transit Oriented Development) elements. Generally, TOD have been evaluated several physical components such as the diversity of land use pattern, accessibility of public transportation and aspects of urban design around the station area. Especially, Spatial characteristics of TOD planning elements have many potential dependent when considering the characteristics of Rail Station-Influenced Area Development which is performing around subway station. Therefore, researchers should be considering the variation of spatial properties for planning elements according the set of spatial area and their socioeconomic factors. However, existing many cases related TOD does not consider about this point. In this paper, the changes of TOD characteristics were analyzed by different spatial units surrounding subway station in Busan Metropolitan City. Multiple Regression Analysis was performed for an investigation of effective spatial unit of TOD planning elements in this area using subway ridership data. In addition, the application validity of socioeconomic variables was examined through a comparative analysis of regression results with the multiple regression that implied only physical TOD elements. As the result, the variation of spatial properties for TOD planning elements according to the set of spatial unit was found. Furthermore, the specific spatial unit to applicable TOD elements in this area was derived. And the multiple regression model which added socioeconomic variables was derived more improved estimate results than the multiple regression model that implied only physical TOD elements.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.302-305
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• 2004

The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.64-77
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• 2021

The purpose of this study is to examine the spatial distribution of highly educated human capital and to identify key factors affecting their spatial distribution. We analyzed the spatial concentration and inequality using Gini's coefficient and exploratory spatial data analysis and identified the economic and amenity factors to affect the spatial concentration of highly educated human capital using spatial regression model. The findings show that the spatial pattern of highly educated human capital is concentrated, imbalanced, and clustered in Capital region and part of Chungcheong and Gangwon region. The spatial concentration were more affected by economic factor than by amenity factors. This study provides some implication on the regional economic strategies to attract the human capital.

• International Journal of High-Rise Buildings
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• v.12 no.1
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• pp.83-91
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• 2023

Under the dual pressure of population growth and land shortage, threedimensional development is the inevitable choice for cities in China. In such a scenario, a mixed-use complex has considerable potential in its realization and research. Based on space syntax and the three-dimensional visibility graph analysis, this paper describes the spatial and functional layout of the Shanghai Super Brand Mall and studies the relationship between spatial visibility and user behaviour through linear regression analysis and correlation analysis. This paper studies three different types of user behaviour, namely, path selection, staying selection, and store selection, and finds that spatial visibility and accessibility have different effects on user behaviour depending on the type and purpose of the activity. This paper reveals the influence of spatial and functional layout on user behaviour and puts forward the corresponding design strategy under the three-dimensional environment.

• Korean Journal of Remote Sensing
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• v.30 no.3
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• pp.375-381
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• 2014

Soil moisture is an essential satellite-driven variable for understanding hydrologic, pedologic and geomorphic processes. The European Space Agency (ESA) has endorsed soil moisture as one of Climate Change Initiates (CCI) and had merged multi-satellites over 30 years. The $0.25^{\circ}$ coarse resolution soil moisture satellite data showed correlations with variables of a water stress index, Temperature-Vegetation Dryness Index (TVDI), from a stepwise regression analysis. The ancillary data from TVDI, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS were inputted to a multi-regression analysis for estimating the surface soil moisture. The estimated soil moisture was validated with in-situ soil moisture data from April, 2012 to March, 2013 at Andong observation sites in South Korea. The soil moisture estimated using satellite-based LST and NDVI showed a good agreement with the observed ground data that this approach is plausible to define spatial distribution of surface soil moisture.

• 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.22 no.3
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• pp.35-46
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• 2014

Population data are usually provided at administrative spatial units in Korea, so areal interpolation is needed for fine-grained analysis. This study aims to compare various methods of areal interpolation for population subgroups rather than the total population. We estimated the number of elderly people and single-person households for small areal units from Dong data by the different interpolation methods using 2010 census data of Seoul, and compared the estimates to actual values. As a result, the performance of areal interpolation methods varied between the total population and subgroup populations as well as between different population subgroups. It turned out that the method using GWR (geographically weighted regression) and building type data outperformed other methods for the total population and households. However, the OLS regression method using building type data performed better for the elderly population, and the OLS regression method based on land use data was the most effective for single-person households. Based on these results, spatial distribution of the single elderly was represented at small areal units, and we believe that this approach can contribute to effective implementation of urban policies.

• Health Policy and Management
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• v.23 no.4
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• pp.387-396
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• 2013

Background: The purpose of this study was to analyze the association between areas of Korea Train Express (KTX) region and external medical service use in Korean society using spatial statistical model. Methods: The data which was used in this study was extracted from 2011 regional health care utilization statistics and health insurance key statistics from National Health Insurance Corporation. A total spatial units of 229 districts (si-gun-gu) were included in this study and spatial area was all parts of the country excepted Jeju, Ulleungdo island. We conducted Kruskal-Wallis test, correlation, Moran's I and hot-spot analysis. And after, ordinary linear regression, spatial lag, spatial error analysis was performed in order to find factors which were associated with external medical service use. The data was processed by SAS ver. 9.1 and Geoda095i (windows). Results: Moran's I of health insurance patients' external medical service use was 0.644. Also, population density, Seoul region, doctor factors positively associated with health insurance patients' external medical service. In contrast, average age, health care organization per 100 thousand were negatively associated with health insurance patients' external medical service use. Conclusion: The finding of this study suggested that health insurance patient's external medical service use correlated for seoul region in korea. The study results imply the need for more attention medical needs in the region (si-gun-gu unit) for health insurance patients of seoul region. It is important to adapt strategy to activation of primary health care as well as enhancing public health institution for prevent leakage of patients to other areas.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.