• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1215-1224
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• 2016

고해상도 격자 단위 기후정보는 농업, 관광학, 생태학, 질병학 등 다양한 분야의 현상을 설명하는 중요 요인이다. 고해상도 기후정보는 동적 모형과 통계적 모형을 통해 얻을 수 있다. 통계적 모형은 동적 모형에 비해 계산 시간이 저렴하여 시공간 해상도가 높은 기후자료 생성에 주로 이용한다. 본 연구에서는 2003년부터 2012년까지 1월에 관측된 일 평균기온자료를 토대로 통계적 모형의 일 평균 기온을 생성하였다. 통계적 모형으로 선형모형을 기반으로한 일반선형모형, 일반화가법모형, 공간선형모형, 베이지안공간선형모형을 고려하였다. 예측성능평가를 위해 60개소의 지상관측소에서 관측된 일 평균기온을 모형적합 자료로 사용하여 352개소의 자동기상관측의 일 평균기온을 검증하였다. 평균제곱오차와 상관계수를 보면 베이지안공간모형의 예측성능이 다른 모형에 비해 상대적으로 우수하였다. 최종적으로 $1km{\times}1km$ 격자 단위 일 평균기온 지도를 생성하였다.

• 한국환경과학회지
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• 제27권7호
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.177-190
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• 2006

The aim of this study is to propose a Bayesian model for fitting mortality rate of colon cancer. For the analysis of mortality rate of a disease, factors such as age classes of population and spatial characteristics of the location are very important. The model proposed in this study allows the age class to be a random effect in addition to its conventional role as the covariate of a linear regression, while the spatial factor being a random effect. The model is fitted using Metropolis-Hastings algorithm. Posterior expected predictive deviances, standardized residuals, and residual plots are used for comparison of models. It is found that the proposed model has smaller residuals and better predictive accuracy. Lastly, we described patterns in disease maps for colon cancer.

• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.315-327
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• 2021

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

• 한국지형학회지
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• 제26권3호
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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.

• Journal of the Korean Data and Information Science Society
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• 제28권1호
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• pp.29-37
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• 2017

산림경영 계획을 위한 필요한 산림재적을 보다 효율적으로 추정하기 위해서 다양한 연구가 요구되어져 왔는데, 이러한 산림구조에 관한 연구는 주로 현장조사와 위성영상을 이용하여 이루어진다. 현장조사를 통한 연구는 비교적 정확하나 시간과 비용이 많이 들 뿐 아니라 접근의 용이성이 떨어지는 지역이 있기 때문에, 넓은 지역의 조사가 어렵다는 단점이 있다. 최근에는 항공기에서 발사된 레이저 펄스가 반사되어 돌아오는 시간을 측정하여 대상의 3차원 좌표를 얻는 LiDAR (Light Detection and Ranging) 기술을 활용하여 획득한 정밀한 수치형자료를 이용한 산림의 구조에 관한 연구가 이루어지고 있다. 일반적으로 산림재적을 추정하기 위해서 LiDAR자료를 이용한 수고자료와 산림 재적에 대한 회귀모형의 중요성이 점차 높아지는데, 국내의 경우 수목의 종류와 그 분포가 다르기 때문에 회귀모형만으로 재적을 추정하는 데 한계가 있다. 따라서 본 논문에서는 산림의 수고와 흉고직경을 측정하여 재적값을 추정하고 산림의 공간효과를 고려한 계층적 베이지안 분석을 통해 관측되지 않은 전체 산림재적에 대한 추정을 하고자 한다.

• 응용통계연구
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• 제23권2호
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• pp.335-344
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• 2010

공간자료분석에서 공간적 상관성을 배제한 일반적인 회귀모형을 통한 모수 추정값들은 신뢰성의 문제가 지적 되어 오고 있다. 본 연구에서는 공간자료의 상관성을 고려한 모형을 구축하기 위하여 일변량 조건부자기회귀모형을 이용하였으며 베이지안 기법을 통하여 모수를 추정하고 공간상관성이 고려된 공간 가산자료모형과 고려되지 않은 일반 가산자료모형을 비교하였다. 연구 대상으로는 서울시의 25개 행정자치구별 총범죄 자료를 이용하였으며 자료분석을 통하여 도시계획과 같은 국가 정책의 수립에 참고자료로 활용될 수 있으리라 판단된다.