• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1153-1166
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• 2020

The purpose of this study was to analyze the spatial correlation between the heat distribution map of the satellite imaging base and the factors that deepen the heat wave, and to explore the heat concentration area and the space where the risk of future heat wave may increase. The global Moran's I of population, land use, and buildings, which are the causes of heat concentration and heat wave deepening, is found to be high and concentrated in specific spaces. According to the analysis results of local Moran's I, heat concentration areas appeared mainly in large cities such as metropolitan and metropolitan areas, and forests were dominant in areas with relatively low temperatures. Areas with high population growth rates were distributed in the surrounding areas of Gyeonggi-do, Daejeon, and Busan, and the use of land and buildings were concentrated in the metropolitan area and large cities. Analysis by Bivarate Local Moran's I has shown that population growth is high in heat-intensive areas, and that artificial and urban building environments and land use take place. The results of this research can lead to the ranking of heat concentration areas and explore areas with environments where heat concentration is concentrated nationwide and deepens it, so ultimately it is considered to contribute to the establishment of preemptive measures to deal with extreme heat.

• The Korean Journal of Ecology
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• v.27 no.5
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• pp.257-261
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• 2004

Juniperus chinensis var. sargentii Henry is a short and creeping evergreen shrub which reaches about 50㎝ in height and occurs in the northeast Asia and in high mountains over the South Korea. Its distribution is restricted, and the number of individuals are gradually decreasing. This study was conducted to estimate spatial pattern, genetic diversity and spatial genetic structure of J. chinensis var. sargentii. A total of 131 clumps were studied in the study area (40m × 60m). The spatial pattern of this population was random (Aggregation index R=1.031). In spite of the small number and the limited distribution, the level of genetic diversity (Shannon's index 1=0.463) was relatively high as compared with those of other plant species with similar ecological characteristics. ISSR genotypes of all individuals were investigated to find the genetic relationship of clumps and genets. Fifteen clumps were composed to be clones, and a total of 116 unique genotypes were composed by separate genets. Spatial autocorrelation analysis using Tanimoto distance showed that the genetic patch was established within 8m. The effect of clonal reproduction on genetic structure was almost nothing.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.57-65
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• 2015

This study investigates characteristics of time series spatial distribution on climate factors in Andong Dam basin by estimating precise spatio-temporal distribution of hydro-meteorological information. A spatio-temporal distribution by estimating Semi-Variogram based on spatial autocorrelation was examined using the data from ASOS and 7 hydro-meteorological observatories in Andong Dam basin of upper Nakdonggang River, which were installed and observed by NIMR(National Institute of Meterological Research). Also, temperature and humidity as climate variables were analyzed and it was recognized that there is a variability in watershed area by time and months. Regardless of season, an equal spatial distribution of temperature at 14 o'clock and humidity at 10 o'clock was identified, and nonequal distribution was noticed for both variables at 18 o'clock. From monthly spatial analysis, the most unequal distribution of temperature was seen in January, and the most equal distribution was detected in September. The most unequal distribution of humidity was identified in May, and the most equal distribution was seen in January. Unlike in forest, seasonal spatial distribution characteristics were less apparent;but temperature and humidity had respective characteristics in hydro-meteorology.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.4
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• pp.53-66
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• 1990

Rainfall is a phenomenon that shows a high variability both in space and time, Hy drologists are usually interested in the description of spatial distribution of rainfall over watershed. The theory of Kriging, generalized covariance technique using nonstationary mean in the regions under orographic effect, was chosen to construct random surface of total storm depth. For the constructed random surface, the double Fourier analysis of the total storm depths was performed, and the principal harmonics of storm were determined. The local component, or storm residuals was obtained by subtracting the periodic component of the storm from total storm depths. It is assumed that the residuals are a sample function of a homogeneous random field. This random field can be characterized by an isotropic one dimensional autocorrelation function or its corresponding spectral density function. Under this assumption, this study proposed a theorectical model for spectral density function adapted to two watersheds.

• Steel and Composite Structures
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• v.24 no.5
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• pp.635-641
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• 2017

Safety service improvement and development of efficient maintenance strategies for corroded steel structures are undeniably essential. Therefore, understanding the influence of damage caused by corrosion on the remaining load-carrying capacities such as tensile strength is required. In this study, artificial neural network (ANN) approach is proposed in order to produce a simple, accurate, and inexpensive method developed by using tensile test results, material properties and finite element method (FEM) results to train the ANN model. Initially in reproducing corroded model process, FEM was used to obtain tensile strength of artificial corroded plates, for which surface is developed by a spatial autocorrelation model. By using the corroded surface data and material properties as input data, with tensile strength as the output data, the ANN model could be trained. The accuracy of the ANN result was then verified by using leave-one-out cross-validation (LOOCV). As a result, it was confirmed that the accuracy of the ANN approach and the final output equation was developed for predicting tensile strength without tensile test results and FEM in further work. Though previous studies have been conducted, the accuracy results are still lower than the proposed ANN approach. Hence, the proposed ANN model now enables us to have a simple, rapid, and inexpensive method to predict residual tensile strength more accurately due to corrosion in steel structures.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.59-64
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• 2014

This study describes analysis of genetic diversity and spatial genetic structure of Korean poplar (Populus koreana Rehder) in Mt. Odae using I-SSR markers. P. koreana is a deciduous broad-leaved tree species that primarily grows in the alpine valleys of China, Russia and North Korea. In South Korea, P. koreana is found limitedly in Gangwon province. Especially, the population in Mt. Odae is located on the southern limit line, its importance is emphasized from the genetic resource conservation perspective. The Shannon's diversity (I=0.230) and the expected heterozygosity (He=0.151) were relatively low as compared with those of other plant species. Spatial autocorrelation analysis using Tanimoto's distance showed that the genetic patch was founded within 400 m. It is suggested that individual trees for ex situ conservation should be sampled with a minimum distance of 400 m between trees.

• Journal of the Korean Geographical Society
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• v.45 no.6
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• pp.806-819
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• 2010

Population data has been essential and fundamental in spatial analysis and commonly aggregated into political boundaries. A conventional method for population distribution estimation was a regression model with land use data, but the estimation process has limitation because of spatial autocorrelation of the population data. This study aimed to improve the accuracy of population distribution estimation by adopting a Regression-Kriging method, namely RK Model, which combines a regression model with Kriging for the residuals. RK Model was applied to a part of Seoul metropolitan area to estimate population distribution based on the residential zones. Comparative results of regression model and RK model using RMSE, MAE, and G statistics revealed that RK model could substantially improve the accuracy of population distribution. It is expected that RK model could be adopted actively for further population distribution estimation.

• Journal of Preventive Medicine and Public Health
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• v.44 no.6
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• pp.242-248
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• 2011

Objectives: Busan is reported to have the highest mortality rate among 16 provinces in Korea, as well as considerable health inequality across its districts. This study sought to examine overall and cause-specific mortality and deprivation at the town level in Busan, thereby identifying towns and causes of deaths to be targeted for improving overall health and alleviating health inequality. Methods: Standardized mortality ratios (SMRs) for all-cause and four specific leading causes of death were calculated at the town level in Busan for the years 2005 through 2008. To construct a deprivation index, principal components and factor analysis were adopted, using 10% sample data from the 2005 census. Geographic information system (GIS) mapping techniques were applied to compare spatial distributions between the deprivation index and SMRs. We fitted the Gaussian conditional autoregressive model (CAR) to estimate the relative risks of mortality by deprivation level, controlling for both the heterogeneity effect and spatial autocorrelation. Results: The SMRs of towns in Busan averaged 100.3, ranging from 70.7 to 139.8. In old inner cities and towns reclaimed for replaced households, the deprivation index and SMRs were relatively high. CAR modeling showed that gaps in SMRs for heart disease, cerebrovascular disease, and physical injury were particularly high. Conclusions: Our findings indicate that more deprived towns are likely to have higher mortality, in particular from cardiovascular disease and physical injury. To improve overall health status and address health inequality, such deprived towns should be targeted.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.411-411
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• 2017

유역 내 발생하는 강우의 공간적인 분포는 인접성 및 거리에 따라 달라질 수 있다. 공간자기상관 분석은 공간단위(유역 또는 행정구역)의 변수(강수 등)가 주변지역과 갖는 관계를 통해 얼마나 분산되어 있는지 혹은 군집되어 있는지를 판별하는 기법으로 최근 많은 연구에서 활성화 되고 있다. 본 연구에서는 낙동강유역을 대상으로 1980~2000년까지 20개년의 기상청을 통해 수집한 강우자료와 CMIP5(Coupled Model Intercomparison Project Phase 5)에서 제공하는 기후변화 자료 중 가용할 수 있는 20개 모델의 강우를 수집하였다. 기후변화 자료는 정상성 분위사상법으로 지역오차보정을 실시하고 불확실성을 저감하고자 베이지안 모델 평균기법을 통해 새로운 시계열을 생성하였다. 생성된 시계열의 공간적인 분포를 정량적으로 평가하고자 중권역별 공간자기상관 분석을 수행하였다. 대부분의 연구에서는 GIS를 활용하여 정성적으로 강우의 분포를 나타내고 있지만 본 연구에서는 공간단위의 인접성 또는 거리에 따른 척도를 기반으로 공간자기상관을 탐색할 수 있는 Moran's I와 LISA(Local Indicators of Spatial Association)기법을 적용하였다. Moran's I는 전체 연구지역에 대한 관계를 하나의 값으로 보여주는 전역적인 기법이며, LISA는 상대적으로 넓은 지역을 국지적으로 구분하여 특정지역에 대한 Hot spot 및 Cold spot을 통해 공간자기상관 정도를 나타내는 국지적인 기법이다. 두 기법을 적용하기 위하여 인접성 기반의 공간매트릭스를 산정하고 계절별 관측값과 베이지안 앙상블 강우의 Moran's I 및 LISA 분석을 실시하였다. 관측자료와 베이지안 앙상블 강우의 분석결과가 매우 유사하게 나타남으로써 베이지안 앙상블 강우의 공간적인 분포가 관측강우를 충분히 재현하고 있다고 판단된다.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.