• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• Journal of Distribution Science
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• v.20 no.3
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• pp.71-82
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• 2022

Purpose: This study aims to not only measure the elderly welfare supply index but investigate spatial patterns and determinants of local elderly welfare services in South Korea. Research design, data, and methodology: The index for local elderly welfare services is measured by employing standardized scores for critical variables related to elderly welfare. The present study utilized the GIS technique and Moran's I index to examine spatial patterns of elderly welfare services. The determinants of local elderly welfare service are analyzed by a series of regression models using R. Results: Spatial imbalance and asymmetric distribution were serious in the supply of elderly welfare service. It was also confirmed that the factors affecting the level of welfare services for the elderly vary depending on the type of service. In particular, the higher the proportion of the elderly population and the social welfare budget, the lower the level of welfare services for the elderly. Conclusions: Given the circumstance of spatial mismatch between supply and demand for elderly welfare services, it is necessary to consider and provide policy tactics about how the economic benefits and welfare budgets generated in the region can contribute to strengthening the welfare service system for the elderly.

• Journal of the Korean GEO-environmental Society
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• v.9 no.7
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• pp.73-85
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• 2008

320 national groundwater monitoring stations have been constructed since 1995 and groundwater levels are measured automatically 4 times a day at each well. It has a difficulty to estimate an average recharge rate of watershed using the recharge rate of the monitoring site because of the lack of its representative on converting a point recharge rate into a spatial one. In this study, the relations between site characteristics (topography, hydraulics, geology, facilities, etc.) and recharge rates of 223 monitoring sites, which were selected using cluster analysis, were analyzed using statistical methods, and finally, regression models were constructed for a recharge rate estimation of non-measured areas. The independent variables for these simple regression models, 1) width of adjacent stream, 2) distance to the nearest stream, 3) topographic slope, and 4) rock type, are proposed using analysis of variance. These models have lots of advantages such as an easy data collection from topographic and geologic maps, a few input variables, and also simplicity in use. Suitability analysis from the comparison between estimation values and original ones at monitoring sites shows that these models are useful for a groundwater recharge estimation.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5917-5921
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• 2015

Background: Prostate cancer is the most commonly diagnosed form of cancer and the sixth leading cause of cancer-related deaths among men in the entire world. Reported standardized incidence rates are 12.6, 61.7, 11.9 and 27.9 in Iran, developed countries, developing countries and the entire world, respectively. The present study investigated the relative risk of PC in Iran at the province level and also explored the impact of some factors by the use of Bayesian models. Materials and Methods: Our study population was all men with PC in Iran from 2005 to 2008. Considered risk factors were smoking, fruit and vegetable intake, physical activity, obesity and human development index. We used empirical and full Bayesian models to study the relative risk in Iran at province level to estimate the risk of PC more accurately. Results: In Iran from 2005 to 2008 the total number of known PC cases was 10,361 with most cases found in Fars and Tehran and the least in Ilam. In all models just human development index was found to be significantly related to PC risk Conclusions: In the unadjusted model, Fars, Semnam, Isfahan and Tehran provinces have the highest and Sistan-and-Baluchestan has the least risk of PC. In general, central provinces have high risk. After adjusting for covariates, Fars and Zanjan provinces have the highest relative risk and Kerman, Northern Khorasan, Kohgiluyeh Boyer Ahmad, Ghazvin and Kermanshah have the lowest relative risk. According to the results, the incidence of PC in provinces with higher human development index is higher.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.797-803
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• 2001

This paper obtained Bayes prediction density for the spatial linear model with non-informative prior. It showed the results that predictive inferences is completely unaffected by departures from the normality assumption in the direction of the elliptical family and the structure of prediction density is unchanged by more than one additional future observations.

• Land and Housing Review
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• v.12 no.2
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• pp.13-32
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• 2021

Rapid urbanization has resulted in the unprecedented growth of population in Metro Manila, the Philippines and has led to a 'dual' housing crisis - vacant/unoccupied socialized housing and a chronic housing shortage or delayed housing supply. By developing two GIS-based statistical models, this study is to examine socialized housing in comparison with private housing with respect to location patterns, integration, accessibility, social and economic aspects, and vulnerability to environmental hazards. Multiple regression analysis was integrated with the GIS to identify significant variables that influence the spatial distribution of socialized housing. The comparison between the two regression models has shown that socialized housing is located in areas with inappropriate land use and poor accessibility to transportation facilities and built urban resources. Moreover, both regression models have proven the statistical significance of the vulnerability of socialized housing to environmental hazards. The finding explains how the current housing policies do not address the country's housing crisis, especially for the marginalized and low-income households. Thus, the findings provide implications for urban planners and local decision-makers in reforming the current policy interventions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.4
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• pp.723-733
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• 2016

This study aims to analyze the effects of urban characteristics on the amount of damage caused by natural disasters. It is focused on the areas of a municipal level in Korea. Also, it takes into account the spatial autocorrelation of the damage caused by natural disasters. Moran's I statistics was estimated to examine the spatial autocorrelation in the damage from the study area. Subsequent to evaluating the suitability for spatial regression models and the OLS regression model, the spatial lag model was employed as an empirical analysis for the study. It showed that the increase in residential area leads to the decrease in the amount of natural disaster damage. On the other hand, the increase in green area and river basin is associated with the increase in the damage. As a result of empirical analysis, appropriate policy establishment and implementation about the damage-adding factors is needed in order to reduce the amount of damage in the future.

• Journal of The Geomorphological Association of Korea
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• v.23 no.2
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• pp.109-125
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• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.