• Journal of Korean Society of Rural Planning
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• v.15 no.1
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• pp.31-46
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• 2009

Community facilities in most rural villages have many problems in the aspect of their size or functions as well as they are generally too superannuated to support diverse community activities; which results in a low degree of inhabitants' satisfaction with community facilities, and inconvenience for using them. Therefore, it may carefully be said that it's time to need the established studies that are necessary with consider to community facilities which can reflect inhabitants' diverse activities. In this study, 5 places which the most common events among the major monthly events of total 25 rural villages were held were selected as the subject place for survey; and then investigated, by means of the participant observation method, the using behavior of inhabitants who used community facilities. Focusing on size, factors, and layout that were being faced by community facilities in most rural villages, This study investigated the number of users, the characteristics of traffic line and behavior, and the using behavior by group; through considering their correlation with the physical setting of community space, it deduced the problems of use; and it proposed the direction of improvement on the basis thereof. Therefore, this study will serve in the future as useful basic materials for designing a rural village's community facilities in consideration of size, factors and layout which can appropriately support inhabitants' community activities.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.583-598
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• 2015

This paper presents derivation of background temperature from geostationary satellite and its validation based on ground measurements and Geographic Information System (GIS) for future use in weather and surface heat variability. This study only focuses on daily and monthly brightness temperature in 2012. From the analysis of COMS Meteorological Data Processing System (CMDPS) data, we have found an error in cloud distribution of model, which used as a background temperature field, and in examining the spatial homogeneity. Excessive cloudy pixels were reconstructed by statistical reanalysis based on consistency of temperature measurement. The derived Brightness temperature has correlation of 0.95, bias of 0.66 K and RMSE of 4.88 K with ground station measurements. The relation between brightness temperature and both elevation and vegetated land cover were highly anti-correlated during warm season and daytime, but marginally correlated during cold season and nighttime. This result suggests that time varying emissivity data is required to derive land surface temperature.

• Health Policy and Management
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• v.20 no.4
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• pp.97-113
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• 2010

The purpose of this study was to analyze the relationship between the locational characteristics of areas surrounding oriental medicine hospitals and the number of patients who visited study hospitals. Administrative data collected from the annual report of 5 ward offices in Daejeon used to assess the geographical attributes. Two oriental medicine hospitals operated in Daejeon provided data for the number of inpatient and outpatient. Number of patients who visited study hospitals was calculated in each Dong which is the smallest administrative district. The geographical attributes of Daejeon were evaluated by the demographic and economic factors which were assumed to influence the health care demand. Each criterion was measured from each Dong. Weights of factors was calculated by Analytic Hierarchy Process (AHP) method. Evaluation scores which representing the geographical attributes of Dong was computed by multiplying the eight factors and weights. Results showed positive correlation coefficients between the evaluation scores of Dong and the number of patients. One hospital which was more closely located to areas with high evaluation scores had higher number of patients than that of the other hospital. Buffering analysis with varying size support the analysis results. This finding proposed the importance of location for the management of oriental medicine hospitals in a metropolitan city. Applying study model to other cities will enhance the validity of study results.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.47-58
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• 2018

In this study, we evaluated the uncertainty in the process of selecting GCM and downscaling method for assessing the impact of climate change, and influence of user-centered climate change information on reproducibility of Chungju Dam inflow was analyzed. First, we selected the top 16 GCMs through the evaluation of spatio-temporal reproducibility of 29 raw GCMs using 30-year average of 10-day precipitation without any bias-correction. The climate extreme indices including annual total precipitation and annual maximum 1-day precipitation were selected as the relevant indices to the dam inflow. The Simple Quantile Mapping (SQM) downscaling method was selected through the evaluation of reproducibility of selected indices and spatial correlation among weather stations. SWAT simulation results for the past 30 years period by considering limitations in weather input showed the satisfactory results with monthly model efficiency of 0.92. The error in average dam inflow according to selection of GCMs and downscaling method showed the bests result when 16 GCMs selected raw GCM analysi were used. It was found that selection of downscaling method rather than selection of GCM is more is important in overall uncertainties. The average inflow for the future period increased in all RCP scenarios as time goes on from near-future to far-future periods. Also, it was predicted that the inflow volume will be higher in the RCP 8.5 scenario than in the RCP 4.5 scenario in all future periods. Maximum daily inflow, which is important for flood control, showed a high changing rate more than twice as much as the average inflow amount. It is also important to understand the seasonal fluctuation of the inflow for the dam management purpose. Both average inflow and maximum inflow showed a tendency to increase mainly in July and August during near-future period while average and maximum inflows increased through the whole period of months in both mid-future and far-future periods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.341-343
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• 2019

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

• Wind and Structures
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• v.34 no.5
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• pp.421-435
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• 2022

To investigate the non-Gaussian properties of fluctuating wind pressures and the error margin of extreme wind loads on a long-span curved roof with matching and mismatching ratios of turbulence integral scales to depth (L_u^x/D), a series of synchronized pressure tests on the rigid model of the complex curved roof were conducted. The regions of Gaussian distribution and non-Gaussian distribution were identified by two criteria, which were based on the cumulative probabilities of higher-order statistical moments (skewness and kurtosis coefficients, S_k and K_u) and spatial correlation of fluctuating wind pressures, respectively. Then the characteristics of fluctuating wind-loads in the non-Gaussian region were analyzed in detail in order to understand the effects of turbulence integral-scale. Results showed that the fluctuating pressures with obvious negative-skewness appear in the area near the leading edge, which is categorized as the non-Gaussian region by both two identification criteria. Comparing with those in the wind field with matching L_u^x/D, the range of non-Gaussian region almost unchanged with a smaller L_u^x/D, while the non-Gaussian features become more evident, leading to higher values of S_k, Ku and peak factor. On contrary, the values of fluctuating pressures become lower in the wind field with a smaller L_u^x/D, eventually resulting in underestimation of extreme wind loads. Hence, the matching relationship of turbulence integral scale to depth should be carefully considered as estimating the extreme wind loads of long-span roof by wind tunnel tests.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.37-47
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• 2024

In the summer of 2018, the Korea-Japan (KJ) region experienced an extremely severe and prolonged heatwave. This study examines the GloSea6 model's prediction performance for the 2018 KJ heatwave event and investigates how its prediction skill is related to large-scale circulation patterns identified by the k-means clustering method. Cluster 1 pattern is characterized by a KJ high-pressure anomaly, Cluster 2 pattern is distinguished by an Eastern European high-pressure anomaly, and Cluster 3 pattern is associated with a Pacific-Japan pattern-like anomaly. By analyzing the spatial correlation coefficients between these three identified circulation patterns and GloSea6 predictions, we assessed the contribution of each circulation pattern to the heatwave lifecycle. Our results show that the Eastern European high-pressure pattern, in particular, plays a significant role in predicting the evolution of the development and peak phases of the 2018 KJ heatwave approximately two weeks in advance. Furthermore, this study suggests that an accurate representation of large-scale atmospheric circulations in upstream regions is a key factor in seasonal forecast models for improving the predictability of extreme weather events, such as the 2018 KJ heatwave.

• Journal of the Korean association of regional geographers
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• v.19 no.4
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• pp.615-626
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• 2013

In this study, spatial patterns of the urban flood vulnerability index in Seoul are examined by considering climate exposure, sensitivity, and adaptability associated with floodings for recent 5 year (2006~2010) period by the smallest administrative unit called Dong. According to the results of correlation analyses based on the IPCC(Intergovernmental Panel on Climate Change)'s vulnerability model, among many variables associated with urban flooding, rainwater tank capacity, 1-day maximum precipitation and flood pumping station capacity have statistically-significant, and relatively-high correlations with the number of flood damage in Seoul. The flood vulnerability map demonstrates that the extensive areas along Anyang and Joongnang streams show relatively high flood vulnerability in Seoul due to high sensitivity. Especially in case of Joongnang stream areas, climatic factors also contribute to the increase of flood vulnerability. At local scales, several Dong areas in Gangdong-gu and Songpa-gu also show high flood vulnerability due to low adaptability, while those in Gangnam-gu do due to high sensibility and climate factor such as extreme rainfall events. These results derived from the flood vulnerability map by Dong unit can be utilized as primary data in establishing the adaptation, management and proactive policies for flooding prevention within the urban areas in more detail.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.363-373
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• 2021

An ecological niche is defined as the specific role of a species influenced by time, space, and other resources. By investigating overlaps between ecological niches of different species, we could estimate the degrees of interspecific competition. Such studies often use geographic information systems (GIS) to discover niche overlaps between species. In this study, we used GIS to estimate the spatial niches of two Korean frog species(Pelophylax nigromaculatus and P. chosenicus). This enabled us to predict their geographic distributions in order to identify their coexistence regions and distribution patterns. The results confirmed that altitude was an important variable for predicting their distribution, with a correlation with their climatic range. Spatial distributions of the two frog species were highly overlapped, as the distribution range for P. nigromaculatus included most of the range of P. chosenicus, showing a sympatric distribution pattern. Within the coexisting regions, however, the presence sites for the two species did not overlap, implying weak competition. To confirm the principal factors influencing their competitive relationship and reasons for their sympatric distribution pattern, we need more detailed in-depth studies on the diverse environmental variables within the regions where the two species coexist. By doing so, we would be able to identify various mechanisms for avoiding competition in sympatric frog species.