• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• Journal of Korean Association for Spatial Structures
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• v.12 no.4
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• pp.91-98
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• 2012

As the occurrence rate of terror and hazard is increasing throughout the world, GSA, DoD, and FEMA are proceeding a study about mitigating the damage of terror. Korea is no more a safe place from the terrorist's threat, so we need to make measures against them. In this study we developed modified RVS System by revising some items to adjust the system to the domestic condition and conducted a risk assessment on several tall buildings in Korea. By using IRVS system which is developed by DHS, we also carried out the risk assessment. Comparing the results between RVS with IRVS, we performed terror risk evaluation of tall buildings. Through risk assessment of several tall buildings, we analyzed key factors of each scenarios and suggested the mean value of each items, so we would like to help the counter-terrorism in the design phase.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Particle and aerosol research
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• v.17 no.1
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• pp.9-20
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• 2021

As a large city advanced, the urban environment is becoming an issue. The contribution of vehicle emissions in air pollutants was very high according to the clean air policy support system (CAPSS). In order to improve the air quality in large cities, it is necessary to establish improvement measures by sources, analyzing the air quality of roadside. We divided Bucheon city into 4 regions to investigate the roadside pollutants of each district using the mobile laboratory (ML) and air quality monitoring station (AQMS). ML was used to measure pollutants emitted from vehicles and AQMS data was used as a comparison group of ML data. As a measurement result of pollutants in the roadside, the concentration of air pollutants in industrial & engineering complex area was the highest and concentration of air pollutants in residential & forest complex area was lower. By street, Bucheon-ro, Sinheung-ro, Sosa-ro, and Gyeongin-ro were identified as high concentrations. Therefore, further researches on preparing management measures for roads in the hot-spot area are needed.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.462-462
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• 2022

Wetlands are one of the most vital natural habitats on the planet. India is incredibly blessed to have a number of multifunctional wetland ecosystems. Wetlands, in addition to their functional importance, can act as sources or sinks for greenhouse gases (GHGs) depending on their intrinsic factors. Carbon (CO2) and Methane (CH4) are the major greenhouse gases (GHG's) emitted in wetlands. It is demonstrated that, despite having 4.6 percent of its area covered by natural or man-made wetlands, being home to a large number of wetlands, and being the world's second largest cultivator of paddy, India's wetlands, including paddy fields that are intermittently flooded as typical wetlands, have been very poorly studied in terms of GHG emissions. The purpose of this paper is to examine the status of Indian wetlands and wetlands in terms of CH4 and CO2 emissions. The present study also reviews various literature to provide the equations, parameters that are required for estimating carbon and methane and some of the best strategies for conserving carbon in wetlands. The findings suggest that both non-manipulative and manipulative measures can be used to improve Carbon Sequestration (CS). Non-manipulative measures aim to improve CS by increasing the spatial extent of wetlands, whereas manipulative measures aim to change the characteristics of specific wetland components that influence CS. Uncertainty in carbon dynamics projections under changing environmental conditions is caused by a number of Knowledge gaps: i) There is a lack of knowledge on how organic matter mineralizes and partitions into carbon dioxide, methane, and dissolved organic carbon, ii) With the notable exception of methane dynamics, models that represent the dynamic interaction of processes and their controls have yet to be established. As a result, more research is needed to fully understand the importance of wetlands in terms of GHG emissions and carbon sequestration in India.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.129-141
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• 2019

In recent years, fast growing cities in Korea are showing signs of being vulnerable to more disasters as their population and facilities increase and intensify. In particular, fire is one of the most common disasters in Korea's cities, along with traffic accidents. Therefore, in this study, we analyze what type of factors affect the fire that threatens urban people. Fire data were acquired for 10 years, from 2007 to 2017, in Jinju, Korea. Spatial distribution pattern of fire occurrence in Jinju was assessed through the spatial autocorrelation analysis. First, spatial autocorrelation analysis was carried out to grasp the spatial distribution pattern of fire occurrence in Jinju city. In addition, correlation and multiple regression analysis were used to confirm spatial dependency and abnormality among factors. Based on this, OLS (Ordinary Least Square) regression analysis was performed using space weighting considering fire location and spatial location of each facility. As a result, First, LISA (Local Indicator of Spatial Association) analysis of the occurrence of fire in Jinju shows that the most central commercial area are fire department, industrial area, and residential area. Second, the OLS regression model was analyzed by applying spatial weighting, focusing on the most derived factors of multiple regression analysis, by integrating population and social variables and physical variables. As a result, the second kind of neighborhood living facility showed the highest correlation with the fire occurrence, followed by the following in the order of single house, sales facility, first type of neighborhood living facility, and number of households. The results of this study are expected to be useful for analyzing the fire occurrence factors of each facility in urban areas and establishing fire safety measures.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.94-106
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• 2018

The epidemiological model is useful for creating simulation and associated preventive measures for disease spread, and provides a detailed understanding of the spread of disease space through contact with individuals. In this study, propose an agent-based spatial model(ABM) integrated with spatial big data to simulate the spread of MERS-CoV infections in real time as a result of the interaction between individuals in space. The model described direct contact between individuals and hospitals, taking into account three factors : population, time, and space. The dynamic relationship of the population was based on the MERS-CoV case in Seoul Metropolitan Government in 2015. The model was used to predict the occurrence of MERS, compare the actual spread of MERS with the results of this model by time series, and verify the validity of the model by applying various scenarios. Testing various preventive measures using the measures proposed to select a quarantine strategy in the event of MERS-CoV outbreaks is expected to play an important role in controlling the spread of MERS-CoV.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.29-58
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• 2022

Recently, Korea has been experiencing a problem of population decline, therefore the transition to a compact spatial structure is being urged. However, what is required is not just physical compression, but a compact city that also considers connectivity, in view of the changes in today's demographic and industrial structure. From this point of view, this study measures the compressibility of domestic cities suffering from extinction risk due to low birth rates, aging population, and population decline, and examined the spatial structure characteristics. In addition to the compressibility evaluation index used in previous studies, the compressibility of six indicators (population, land use, service accessibility, transport accessibility, connectivity, and concentration) was compared and analyzed, and a comprehensive compression index was calculated. The analysis results, based on the comprehensive compression index, classified 2.3% cities in the first grade, 4.6% in the second grade, 16.09% in the third grade, 43.68% in the fourth grade, and 33.33% in the fifth grade areas. Currently, the urban characteristics affecting the compactness index differ from region to region. Therefore, it is necessary to establish measures and policies for extinction risk considering the influence of each region's compactness index. This study is meaningful in that connectivity was considered using spatial syntax, and the compactness of cities at risk of extinction was compared and analyzed quantitatively. It is expected that this study will be used as basic data to establish the direction and action strategy for extinction risk cities.

• Journal of the Korean association of regional geographers
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• v.23 no.2
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• pp.388-402
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• 2017

This paper presents an enhancement of the two-step floating catchment area (2SFCA) method for measuring spatial accessibility between three age groups, addressing the problem of uniform access within the catchment by applying multiple impedance function to account for distance decay and by applying weights to different age groups to account for medical service preference. The enhancement is provided to be another special care of the gravity model. When applying this modified three-step floating catchment area to measure the spatial access to emergency medical services in a study area, Chungnam province in South Korea, we find that it reveals the variation of spatial accessibility patterns between cities and rural areas and delineates more spatially explicit medical service shortage areas in southern Chungnam areas, especially remoted local rural areas. Finally, this method may be used to help the health and medical service divisions and the state departments improve designation of medical shortage areas. From the discussions, it is easy to implement in planning spatial policies of medical service and straightforward to be used as a basic, but core element for health and medical strategies in the province.