• 대한원격탐사학회지
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• 제33권1호
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• 보건행정학회지
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• 제26권1호
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• pp.30-38
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• 2016

Background: This study purposed to analyze the relationship between spatial distribution of Diabetes prevalence rates and regional variables. Methods: The unit of analysis was administrative districts of city gun gu. Dependent variable was the age- and sex- adjusted diabetes prevalence rates and regional variables were selected to represent three aspects: demographic and socioeconomic factor, health and medical factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis, geographically weighted regression (GWR) was applied for the spatial analysis. Results: Analysis results showed that age- and sex-adjusted diabetes prevalence rates were varied depending on regions. OLS regression showed that diabetes prevalence rates had significant relationships with percent of population over age 65 and financial independence rate. In GWR, the effects of regional variables were not consistent. These results provide information to health policy makers. Conclusion: Regional characteristics should be considered in allocating health resources and developing health related programs for the regional disease management.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• 대한공간정보학회지
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• 제24권4호
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• pp.75-81
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• 2016

본 연구에서는 아파트 실거래가와 이에 영향을 미치는 변인들의 공간적 이질성을 시공간적인 측면에서 탐색하는데 초점을 두었다. 아파트 실거래가에 영향을 미칠 것으로 사료되는 독립변수로서 교통 및 지역적 특성과 교육여건, 인구 경제적 특성을 고려하였다. 따라서 전역적인 측면과 국지적인 측면에서 독립변수의 영향력과 공간상의 분포패턴을 분석하였으며, 종속변수인 아파트 실거래가의 시공간적인 변화패턴을 살펴보았다. 먼저, 분석모형 구축을 위해 일반최소제곱분석과 지리가중회귀분석을 수행하여 보다 효율적이고 적합한 모형을 채택하였다. 2010년과 2013년의 모형 분석결과는 유사한 패턴을 보이며, 두 시기 모두 지리가중회귀모형이 일반최소제곱모형보다 더 설명력이 있는 모형인 것으로 분석되었다. 둘째, 채택된 지리가중회귀모형을 이용하여 독립변수의 시공간적 이질성을 파악하기 위해 Local $R^2$를 이용하여 국지적 분석을 수행하였다. Local $R^2$값은 지역별로 상이하게 나타났으며 이는 공간상의 이질성이 존재함을 보여주는 것으로 판단할 수 있다. 셋째, 지리가중회귀분석 시 종속변수로 사용했던 아파트 실거래가의 시기별/전용면적별 공간분포를 살펴보기 위해 크리깅분석을 실시하였다. 이를 통해 아파트 실거래가와 같은 공간데이터에 영향을 미치는 외부적 환경도 지역별 이질성이 크기 때문에 공간적 편차가 있는 것으로 나타났다. 따라서 이러한 결과를 바탕으로 보다 미시적인 주택하위시장분석을 수행할 수 있고, 부동산정책을 수립하는데 근간이 될 수 있을 것으로 사료된다.

• 응용통계연구
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• 제23권2호
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• pp.367-374
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• 2010

As an alternative to the k-means clustering the k-spatial medians clustering has many good points because of advantages of spatial median. However, it has not been used a lot since it needs heavy computation. If the number of objects and the number of variables are large the computation time problem is getting serious. In this study we propose fast algorithm for the k-spatial medians clustering. Practical applicability of the algorithm is shown with some numerical studies.

• 한국물환경학회지
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• 제27권5호
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• pp.699-709
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• 2011

The uses of multi-dimensional hydrodynamic and water quality models are increasing to support a sustainable management of large dam reservoirs in Korea. Any modeling study requires selection of a proper spatial dimension of the model based on the characteristics of spatial variability of concerned simulation variables. For example, a laterally averaged two-dimensional (2D) model, which has been widely used in many large dam reservoirs in Korea, assumes that the lateral variations of hydrodynamic and water quality variables are negligible. However, there has been limited studies to give a justification of the assumption. The objectives of this study were to present the characteristics of spatial variations of water quality variables through intensive field monitoring in Daechung Reservoir, and provide information on a proper spatial dimension for different water quality parameters. The monitoring results showed that the lateral variations of water temperature are marginal, but those of DO, pH, and conductivity could be significant depending on the hydrological conditions and local algal biomass. In particular, the phytoplankton (Chl-a) and nutrient concentrations showed a significant lateral variation at R2 (Daejeongri) during low flow periods in 2008 possibly because of slow lateral mixing of tributary inflow from So-oak Stream and wind driven patchiness.

• Journal of Biosystems Engineering
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• 제39권4호
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• Spatial Information Research
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• 제10권2호
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• pp.215-232
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• 2002

본 연구는 근린유흥시설의 분포를 나타내는 헤도닉 모델의 근린변수가 지리정보시스템의 공간분석기능을 충분히 이용하여 측정되었을 경우, 그렇지 않을 경우에 비해 해당변수를 포함하는 헤도닉 모델의 통계적 질을 향상시킬 수 있는 가를 평가하고 있다. 평가결과는 해당변수가 지리정보시스템의 공간분석기능을 충분히 이용하여 측정되었을 경우가 헤도닉 모델의 설명력측면에서 우월함을 보이고 있다. 본 연구결과는 지리정보시스템이 단순한 직선거리를 추정하는 것 이상으로 헤도닉 모델의 질을 향상시킬 수 있는 잠재력을 가지고 있다는 것을 보이고 있으며 주택시장의 행태를 설명하는 이론적으로 타당한 근린변수의 측정을 위해 보다 적극적으로 이용되어야 함을 실증적으로 보이고 있다.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제4권2호
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• pp.79-85
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• 2023

The Tibetan Plateau is home to the only alpine crane species, the black-necked crane (Grus nigricollis). Conservation efforts are severely hampered by a lack of knowledge on the spatial distribution and breeding habitats of this species. The ecological niche modeling framework used to predict the spatial distribution of this species, based on the maximum entropy and occurrence record data, allowed us to generate a species-specific spatial distribution map in Ladakh, Trans-Himalaya, India. The model was created by assimilating species occurrence data from 486 geographical sites with 24 topographic and bioclimatic variables. Fourteen variables helped forecast the distribution of black-necked cranes by 96.2%. The area under the curve score for the model training data was high (0.98), indicating the accuracy and predictive performance of the model. Of the total study area, the areas with high and moderate habitat suitability for black-necked cranes were anticipated to be 8,156 km² and 6,759 km², respectively. The area with high habitat suitability within the protected areas was 5,335 km². The spatial distribution predicted using our model showed that the majority of speculated conservation areas bordered the existing protected areas of the Changthang Wildlife Sanctuary. Hence, we believe, that by increasing the current study area, we can account for these gaps in conservation areas, more effectively.

• 한국경제지리학회지
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• 제14권1호
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• pp.33-51
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• 2011

본 연구의 목적은 우리나라에서 지역의 사회 경제적 속성에 결정적인 영향을 미치는 것으로 인식되고 있는 사교육 시설의 수요와 공급에 나타나는 공간적 특성을 분석하는 것이다. 이를 위하여 우리나라 전체 인구의 절반 정도가 거주하고 있으며, 사교육에 대한 수요와 공급이 집중되어 있는 수도권 지역을 대상으로 현재 우리나라 사교육 시설 중 가장 대표적인 사설학원의 유형별 수요와 공급의 공간적 분포 특성을 분석하였다. 특히 사교육 수요층을 기존의 초 중 고 학생뿐만 아니라 학령기 이전의 유치원생 및 대학생까지 확대하여 각 연령대별 그들의 거주지 분포와 관련 사교육 시설의 분포에 나타나는 공간적 특징을 분석하였다. 또한 공간적 자기상관 분석(LISA)을 통하여 사설학원의 유형에 따라 사설학원 수강자의 수요와 군집 패턴에 뚜렷한 차이가 있음을 확인하였고, 다중회귀분석을 통해 지역의 사설학원 유형별 시설 수 및 수강자의 분포에 영향을 미치는 유의미한 사회 경제적 설명변수를 도출하였다.