• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1407-1419
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• 2020

Long time-series gridded data is crucial for the analyses of Earth environmental changes. Climate reanalysis and satellite images are now used as global-scale periodical and quantitative information for the atmosphere and land surface. This paper examines the feasibility of DCT-PLS (penalized least square regression based on discrete cosine transform) for the spatial gap filling of gridded data through the experiments for multiple variables. Because gap-free data is required for an objective comparison of original with gap-filled data, we used LDAPS (Local Data Assimilation and Prediction System) daily data and MODIS (Moderate Resolution Imaging Spectroradiometer) monthly products. In the experiments for relative humidity, wind speed, LST (land surface temperature), and NDVI (normalized difference vegetation index), we made sure that randomly generated gaps were retrieved very similar to the original data. The correlation coefficients were over 0.95 for the four variables. Because the DCT-PLS method does not require ancillary data and can refer to both spatial and temporal information with a fast computation, it can be applied to operative systems for satellite data processing.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.455-465
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• 2011

In order to assess and mitigate climate change, the role of forest biomass as carbon sink has to be understood spatially and quantitatively. Since existing forest statistics can not provide spatial information about forest resources, it is needed to predict spatial distribution of forest biomass under an alternative scheme. This study focuses on developing an upscaling method that expands forest variables from plot to landscape scale to estimate spatially explicit aboveground biomass(AGB). For this, forest stand variables were extracted from National Forest Inventory(NFI) data and used to develop AGB regression models by tree species. Dominant/codominant height and crown density were used as explanatory variables of AGB regression models. Spatial distribution of AGB could be estimated using AGB models, forest type map and the stand height map that was developed by forest type map and height regression models. Finally, it was estimated that total amount of forest AGB in Danyang was 6,606,324 ton. This estimate was within standard error of AGB statistics calculated by sample-based estimator, which was 6,518,178 ton. This AGB upscaling method can provide the means that can easily estimate biomass in large area. But because forest type map used as base map was produced using categorical data, this method has limits to improve a precision of AGB map.

• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.275-291
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• 2013

Monitoring the global Gross Primary Pproduction (GPP) is relevant to understanding the global carbon cycle and evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close with high significance ($R^2=0.8164$, $RMSE=0.6126g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$, $bias=-0.0271g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$). We also compared our results to those of other models. The component variables tended to be either over- or under-estimated when compared to those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.2
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• pp.63-70
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• 2008

This paper resents the spatial electric load forecasting algorithm using the multiple regression analysis method which is enhanced from the algorithm of the DISPLAN(Distribution Information System PLAN). In order to improve the accuracy of the spatial electrical load forecasting, input variables are selected for GRDP(Gross Regional Domestic Product), the local population and the electrical load sales of the past year. Tests are performed to analyze the accuracy of the proposed method for Gyeong-San City, Gu-Mi City, Gim-Cheon City and Yeong-Ju City of North Gyeongsang Province. Test results show that the overall accuracy of the proposed method is improved the percentage error 11.2[%] over 12[%] of the DISPLAN. Specially, the accuracy is enhanced a lot in the case of high variability of input variables. The proposed method will be used to forecast local electric loads for the optimal investment of distribution systems.

• Journal of Korean Society of Rural Planning
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• v.23 no.4
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• pp.99-110
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• 2017

This study is to analyze the spatial structure and POE of Jungja(pavilion) shelter for rural village regeneration. 14 Jungja shelter space at rural villages in Gyungbuk province, were investigated. An interview questionnaire was conducted for total 139 residents as POE. The use behavior and satisfaction for Jungja shelter space, were investigated. The statistical analysis were mean of satisfactions, reliability, factor analysis, and multiple regression analysis. The results and discussions can be objective data for rural village regeneration. In satisfaction level, 'Continuous use intention'(3.99), 'Well-suited approach'(3.87), and 'Helpful in resident living'(3.84) were shown to be high points of agreement in 5 point Likert type scale. But, the mean points were low as 2.01 in 'Surrounding landscape', 'Creation of green areas'(3.22), and 'Traffic safety'(3.22), respectively. Within use satisfaction, 5 factors were categorized, 'Use', 'Safety', 'Facility', 'Management' and 'Users'. By the result of multiple regression analysis, variables of 'Continuous use', 'Convenient location', and 'Image improvement', were shown to be main affecting variables to overall satisfaction. Furthermore, in spatial structure analysis, 4 types were categorized with the aspect of landform, roads, and location in village. The levels of satisfaction were shown to be high in village type of semi-open, road type of circular, and location type of center/back. Conclusively, these findings could be utilized as basic data and useful tool of space-structural satisfaction analytic method, and for each stage of planning/design and remodeling for rural village regeneration.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.331-344
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• 2015

A long-term gridded historical data at 3 km spatial resolution has been generated for practical regional applications such as hydrologic modelling. However, overly high or low values have been found at some grid points where complex topography or sparse observational network exist. In this study, the Inverse Distance Weighting (IDW) method was applied to properly smooth the overly predicted values of Improved GIS-based Regression Model (IGISRM), called the IDW-IGISRM grid data, at the same resolution for daily precipitation, maximum temperature and minimum temperature from 2001 to 2010 over South Korea. We tested various effective distances in the IDW method to detect an optimal distance that provides the highest performance. IDW-IGISRM was compared with IGISRM to evaluate the effectiveness of IDW-IGISRM with regard to spatial patterns, and quantitative performance metrics over 243 AWS observational points and four selected stations showing the largest biases. Regarding the spatial pattern, IDW-IGISRM reduced irrational overly predicted values, i. e. producing smoother spatial maps that IGISRM for all variables. In addition, all quantitative performance metrics were improved by IDW-IGISRM; correlation coefficient (CC), Index Of Agreement (IOA) increase up to 11.2% and 2.0%, respectively. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also reduced up to 5.4% and 15.2% respectively. At the selected four stations, this study demonstrated that the improvement was more considerable. These results indicate that IDW-IGISRM can improve the predictive performance of IGISRM, consequently providing more reliable high-resolution gridded data for assessment, adaptation, and vulnerability studies of climate change impacts.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.63-72
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• 2012

In contemporary times, "environmental designers" need to consider both exterior and interior aspects because of the growing trend in dissolution between exterior and interior spaces. To quantify "spatial sense" which serves as the standard for environmental design, this study has asked 63 subjects to evaluate 15 interior and 14 exterior spaces. The "spaciousness (small-large)", "openness(closed-open)", "warmness(warm-cold)", "brightness(bright-dark)", "softness(soft-hard)", "spatial intimacy" and "frequency of visit" were adopted as variables of spatial sense. Through the analysis of these variables, this study could gain the difference between spatial sense for exterior and interior environments, quantify the spatial sense that physically and psychologically appropriates to human beings. The result of this study can be summarized as follows: Twice the amount of spaciousness was observed between the interior and exterior spaces. And the standard on intimate space is established with W/H ratio of 5.71 and high Window/Wall Area ratio in the interior and an area of 3,800m2 and a W/H ratio of 5.57 in exterior. The difference between the spatial sense in the interior and exterior space is mostly dependent on the psychological sense. The increase of physical size caused by the interior space to be perceived as cold, dark and hard psychologically, but exterior space to be perceived as warm, bright and soft. Psychological senses, especially softness, affect spatial intimacy to the greatest extent among the given variables. As the psychological senses for interior spaces were largely independent from the given space's size and perceptive senses, the size of the interior space, which exhibited spatial intimacy, could not be deduced. In comparison to this, due to the high dependency between the psychological senses for exterior spaces and the given space's size and perceptive senses. The study also showed that interior and exterior spaces have relatively different spatial sense and physical standards. Such research results are predicted to provide applicable standards for environmental designers for exterior and interior spaces in the future.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.561-575
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• 2014

In this paper, we propose a penalized likelihood method to detect local spatial clusters associated with disease. The key computational algorithm is based on genlasso by Tibshirani and Taylor (2011). The proposed method has two main advantages over Kulldorff's method which is popoular to detect local spatial clusters. First, it is not needed to specify a proper cluster size a priori. Second, any type of covariate can be incorporated and, it is possible to find local spatial clusters adjusted for some demographic variables. We illustrate our proposed method using tuberculosis data from Seoul.

• Journal of Distribution Science
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• v.17 no.3
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• pp.21-30
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• 2019

Purpose - While many related prior studies have focused on the segregation by race and ethnicity, the academic interest in the separation of residence by income and social class is gradually increasing. This study aims to not only investigate spatial pattern of economic segregation and poverty rate in South Korea, but also shed light on what affect residential distribution of the poor. Research design, data, and methodology - The unit of analysis is Si-Gun-Gu municipal level entities of South Korea. Most demographic, socioeconomic, and residential variables were derived from Korean Census Data in 2015. In order to examine spatial patterns of economic segregation and poverty rate in South Korea, a series of measurements and visualization was conducted through the Geo-Segregation Analyzer and ArcGIS programs. Determinants of economic segregation and local poverty rates were investigated by regression analyses using STATA. Results - The spatial patterns of areas with high poverty rates were extremely clustered, while the distribution of areas with high economic segregation was relatively evenly distributed. Demographic, residential, and local factors appeared to affect whether the poor live in particular area or spread evenly. Conclusions - The factors that raise the poverty rate result in lower level of economic segregation, while factors that reduce the poverty rate lead to severe level of economic segregation.

• Journal of Korean Society of Rural Planning
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• v.4 no.2
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• pp.128-137
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• 1998

The objective of this study is to approach on the spatial boundary of rural development protect by areal classification technique with spatial reference to searching of areal homogeneities in two hierachial administrative units, Ri Eup/Myun. In this study, a criterion for judging areal homogeneities is the degree of agriculture and urbanizing. Variables selected by these two criteria are analysed with the method of fator analysis. The results of areal analysis are as follows: first, generally, the importance of agricultural factors in areal analysis is getting less. Second, areal classification by Myun, Ri in Ansong City is revealed variously because of urban factors. Urban factors make areal heterogeneities become greater, Therefore urban factors are important when analyzing areal characteristics. Third, lately, in areas near by Chung- cheong Do and areas with bad road's condition, areal heterogeneities have been also getting greater. The results of analysis about areal characteristics of Myun and Ri are different from each other. In addition, urban factors are more influential on the areal characteristics than agricultural factors. Therefore, the establishment of rural development project for inindle spatial boundary between Myun unit and Ri unit is needed.