• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.865-877
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• 2009

While characterized initially as an urban-scale pollutant, ozone has increasingly been recognized as a regional and even global-scale phenomenon. The complexity of environmental data dynamics often requires models covering non-linearity. This study deals with modeling ozone with meteorology in Seoul area. The relationships are used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.823-835
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• 2021

Identification of factors influencing regional population is critical for establishing government's population policies as well as for improving residents' social, economic and cultural well-being in the region. In this study we analysed the data from 2019 Population Housing Survey in Korea to identify the factors affecting the population size in each of the three regions: Seoul, metropolitan cities, and provincial regions. We applied a Bayesian quantile regression to account for asymmetry and heteroscedasticity of data. The analysis results showed that the effects of factors vary greatly between the three regions of Seoul, metropolitan cities, and provincial regions as well as between sub regions within the same region. These results suggest that population-related variables have very heterogeneous characteristics from region to region and therefore it is important to establish customized population policies that suit regional characteristics rather than uniform population policies that apply to every region.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.638-650
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• 2014

The objective of this study is to identify the causes of pedestrian volume path to the destination by investigating the influential levels of regional and planning features in the central area of Seoul. Regional characteristics can be classified from the result of the analysis and through the spatial characteristics of pedestrian volume. For global scale analysis, Ordinary Least Squares (OLS) regression is used for the degree of influence of each characteristics to pedestrian volume. For the local scale, Geographically Weighted Regression (GWR) is used to identify regional influential factors with consideration for spatial differences. The results of OLS indicate that boroughs with transportation facilities, commercial business districts, universities, and planning features with education research facilities and planning facilities have a positive effect on pedestrian volume path to the destination. Correspondingly, transportation hubs and congested areas, commercial and business centers, and university towns and research facilities in the Seoul central area can be identified through the results of GWR. The results of this study can provide information with relevance to existing plans and policies about the importance of regional characteristics and spatial heterogeneity effects on pedestrian volume, as well as significance in the establishment of regional development plans.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.835-847
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• 2016

To analyze lattice or areal data, a conditionally autoregressive (CAR) model has been widely used in the eld of spatial analysis. The spatial neighborhoods within CAR model are generally formed using only inter-distance or boundaries between regions. Kyung and Ghosh (2010) proposed a new class of models to accommodate spatial variations that may depend on directions. The proposed model, a directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Properties of maximum likelihood estimators of a Gaussian DCAR are discussed. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Water for future
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• v.23 no.1
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• pp.79-87
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• 1990

In this study, a regression model, which relates annual flood peak flows collected at stramflow gaging stations in the Han river and Nakdong river basin to both basin characteristics and precipitation data, is developed by using the generalized least squares method which can provide reasonable and unbiased estimator of error variance by separating error variance of the regression model into that due to model error and due to sampling error. This model may be used as a mechanism for transferring hydrologic information from the gaged sites to ungaged sites.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.229-243
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• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.3
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• pp.139-154
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• 1993

The major purpose of this study is to develop a regionalized regression model, which predicts flood peaks from the characteristics of the ungaged catchments, through the regional flood frequency analysis for the selected stage gauging stations located on several natural rivers of Korea. The magnitude and the frequency of flood peaks with specified recurrence intervals were estimated from the flood frequency analysis on the 28 selected stage gauging stations distributed on the five major rivers of Korea. The results of the analysis were compared with the predictions from the two different flood frequency models. From the statistical evaluation of these models, it was revealed that the POT model (Peaks Over a Threshold model), which is based on the partial duration method, is more effective in predicting flood peaks from short period records than the ANNMAX model (ANNual MAXimum model) which is based on the annual maximum series method. A regionalized regression model was developed to facilitate the estimation of design floods for ungaged catchments through the regression analysis between flood peaks and the topographic characteristics of the catchments assumed to be important in runoff processes. In addition to this, the correlation diagrams are presented which show the relationships between flood peaks with specified recurrence intervals and the major characteristics of the catchments.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.561-568
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• 2015

In this study we predict apartment prices per unit in Daegu-Gyeongbuk areas by spatial lag and spatial error models, both of which belong to so-called spatial regression model. A spatial weight matrix is constructed by k-nearest neighbours method and then the models for the apartment prices in March, 2012 are fitted using the weight matrix. The apartment prices in March, 2013 are predicted by the fitted spatial regression models and then performances of two spatial regression models are compared by RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error).

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.878-878
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• 2012

도시개발에 따른 인구증가와 강수의 계절적 편중 등으로 인하여 우리나라의 수자원량은 부족한 실정이다. 따라서 이러한 수자원을 효율적으로 이용하기 위해서는 유역의 가용 수자원량의 파악과 이에 따른 최적배분이 필요하다. 이러한 하천유량은 우량이나 수위와 같이 연속관측이 어렵기 때문에 관측치가 한정되어 있는 것이 일반적이며 자연하천에서 실시간으로 유량자료를 생산하는 것은 많은 인력과 장비, 경비가 필요하게 된다. 따라서 본 연구에서는 유량자료의 생산에 있어서 시간과 비용의 경제성 등을 고려하고 좀 더 효율적인 방법을 찾기 위하여 낙동강 유역의 제 1지류인 금호강 유역 내에 위치한 동촌 수위관측소의 유량자료를 이용하여 상류에 위치한 금호 단포교 지점을 미계측 유역이라 가정한 후 유량추정방법에 따른 적용성 검토를 위해 강우-유출모형인 SWAT(Soil and Water Assessment Tool)과 유역면적만을 활용하는 비유량법(Drainage-area ratio method), 유출에 영향을 주는 지형인자를 이용하는 지역회귀방법(Regional regression method)을 적용하여 그 타당성을 비교하였다. 모의된 결과, 동촌 금호 단포교 지점의 연간 상하류 유량비교에서 유량반전은 없었으며 비유량법의 유량추정에서는 높은 상관성을 보였으나 2008년과 2009년의 가뭄으로 인하여 강우-유출모형의 유량추정에서는 낮은 상관성을 보여주었다. 지역회귀방법에서는 수위관측소별 유황자료를 종속변수로 유역면적, 유역평균경사, 유로연장을 독립변수로 하는 회귀식을 산정하여 비교하였으나 본 연구에서는 사용된 자료수가 적고 수리구조물을 이용한 회귀수량 등으로 인하여 갈수량이 실측유량과는 다소 차이가 발생하였다. 미계측 유역의 유량추정시에는 자료의 축척기간과 연도별로 안정된 호우사상, 유역의 적절한 배분에 따라 결과치가 좌우되며 본 연구에서 사용된 유량추정은 관측 자료를 기초로 한 간접적인 방법들이였다. 결과적으로 금호강 유역의 동촌 지점을 이용하여 유량추정방법들을 적용해본 결과 비유량법과 강우-유출모형을 사용하는 것이 적정하였으나 관측 자료의 축적기간이 길고 상하류 간의 유량이 안정된 유역에서는 지역회귀방법의 적용으로도 안정된 유량을 산정할 것이라고 판단된다.