• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.221-221
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• 2016

최근 레이더를 이용한 정량적 강수추정과 관련된 연구가 활발히 수행되고 있으며, 우량계와 레이더 자료의 합성과 관련된 연구가 수행되고 있다. 이는 정도 높은 우량계 자료의 장점과 강우의 공간분포를 파악할 수 있는 레이더 자료의 장점을 결합하여 고품질의 자료를 생산할 수 있기 때문이다. 자료합성과 관련된 다양한 기법이 도입되었고, 크리깅의 한 종류인 코크리깅이 널리 사용되고 있다. 크리깅은 값을 알고 있는 지점의 자료를 가중선형 조합하여 미지점의 값을 예측하는 경험적 방법으로 연속적이며 정규분포를 따르는 자료에 대해 유효하다. 그러나 강우자료는 강한 양의 왜곡도를 나타나고 간헐성도 강하게 나타나 크리깅의 이러한 조건을 만족시키지 못한다. 이로 인해 강우 자료에 크리깅을 수행할 경우 예측 값이 왜곡되거나 편향될 가능성이 크다. 이에 본 연구에서는 강우의 간헐성과 정규분포를 따르지 않는 특성을 고려하여 단순크리깅의 적용방법을 개선하였다. 단순크리깅은 가장 간단한 크리깅 기법으로 설명이 쉽고 적용사례를 비교하기 유리하여 이를 개선하면 다른 복잡한 크리깅 기법에도 쉽게 적용이 가능한 이점이 있다. 본 연구에서는 모의 자료와 레이더 강우 자료를 이용하여 단순크리깅을 수행하였고, 그 결과를 비교하여 자료의 간헐성과 비정규적 특성이 예측 값에 미치는 영향을 분석하였다.

• Journal of the Korean Geotechnical Society
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• v.26 no.5
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• pp.57-64
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• 2010

Kriging techniques have been used to estimate the spatial distribution of soil layers and soil properties in the geotechnical engineering area. Since the selected kriging technique may provide different values of estimation, the selection of method is important in the geotechnical estimation. In this paper, the spatial distribution of the thickness of consolidation layer of Songdo International City is estimated using simple, ordinary, and universal kriging techniques, and the reliability of estimated results is analyzed. It is shown that the consolidation layer thickness estimated by the simple kriging technique is larger than those by other kriging techniques when the location of estimation is far from the locations where the measured data exist. In this case, the reliability of the simple kriging technique is observed to be lower than those of other techniques. Universal kriging gives a negative value for thickness of consolidation layer in some locations away from the data. It is concluded that the ordinary kriging is the most optimized estimation technique because the reliability of ordinary kriging technique is higher than those of other ones and the consolidation layer thickness estimated by the ordinary kriging locates within the reasonable range.

• Spatial Information Research
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• v.19 no.4
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• pp.81-89
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• 2011

This paper presents a multivariate kriging algorithm that integrates categorical data as secondary data for spatial interpolation of sparsely sampled ground survey data. Instead of using constant mean values in each attribute of categorical data, disaggregated local mean values at target grid points are first estimated by area-to-point kriging and then are used as local mean values in simple kriging with local means. This algorithm is illustrated through a case study of spatial interpolation of a geochemical copper element with geological map data. Cross validation results indicates that the presented algorithm leads to significant respective improvement of 15% and 25% in prediction capability, compared with univariate ordinary kriging and conventional simple kriging with constant mean values. It is expected that the multivariate kriging algorithm applied in this study would be effectively applied for spatial interpolation with categorical data.

• Journal of the Korean earth science society
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• v.26 no.5
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• pp.429-435
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• 2005

3D RMR (Rock Mass Rating) analysis has been performed by applying the Geostatistical integration technique for geophysical and borehole data. Of the various geostatistical techniques for the integrated data analysis, in this study, we applied the SKlvm (Simple Kriging with local varying means) method that substitutes the values of the interpreted geophysical result with the mean values of the RMR at the location to be inferred. The substitution is performed by the indicator transform between the result of geophysical interpretation and the observed RMR values at borehole sites. The used geophysical data are the electrical resistivity and MT result, and 10 borehole sites are investigated to obtain the RMR values. This integrated analysis makes the interpretation to be more practical for identifying the realistic RMR distribution that supports the regional geological situation.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.125-140
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• 2017

Until recently, regression based spatial interpolation methods and Kriging based spatial interpolation methods have been largely used to estimate land price or housing price, but less attention has been paid on comparing the performance of these spatial interpolation methods. In this regard, this research applied regression based spatial interpolators and Kriging based spatial interpolators for estimating the land prices in Dalseo-gu, Daegu metropolitan city and evaluated the accuracy of eight spatial interpolators. OLS, SLM, SEM, and GWR were used as regression based spatial interpolators while SK, OK, UK, and CK were employed as Kriging based spatial interpolators. The global accuracy was statistically evaluated by RMSE, adjusted RMSE, and COD. The relative accuracy was visually compared by three-dimensional residual error map and scatterplot. Results from statistical and visual analyses indicate that GWR reflecting the spatial non-stationarity was a relatively more accurate spatial predictor to estimate land prices in the study area than SAR and Kriging based spatial interpolators considering the spatial dependence. The findings from this research will contribute to the secondary research into analyzing the urban spatial structure with land prices.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.1-14
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• 2013

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Despite of its importance, AADT is estimated through permanent traffic counts (PTC) at limited locations because of constraints in budget and so on. At most of locations, AADT is estimated using short-term traffic counts (STC). Though many studies have been carried out at home and abroad in an effort to enhance the accuracy of AADT estimate, the method to simplify average STC data has been adopted because of application difficulty. A typical model for estimating AADT is an adjustment factor application model which applies the monthly or weekly adjustment factors at PTC points (or group) with similar traffic pattern. But this model has the limit in determining the PTC points (or group) with similar traffic pattern with STC. Because STC represents usually 24-hour or 48-hour data, it's difficult to forecast a 365-day traffic variation. In order to improve the accuracy of traffic volume prediction, this study used the geostatistical approach called co-kriging and according to their reports. To compare results, using 3 methods : using adjustment factor in same section(method 1), using grouping method to apply adjustment factor(method 2), cokriging model using previous year's traffic data which is in a high spatial correlation with traffic volume data as a secondary variable. This study deals with estimating AADT considering time and space so AADT estimation is more reliable comparing other research.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.29-41
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• 2010

In this study, the prediction errors of various spatial interpolation methods used to model values at unmeasured locations was compared and the accuracy of these predictions was evaluated. The root mean square (RMS) was calculated by processing different parameters associated with spatial interpolation by using techniques such as inverse distance weighting, kriging, local polynomial interpolation and radial basis function to known elevation data of the east coastal area under the same condition. As a result, a circular model of simple kriging reached the smallest RMS value. Prediction map using the multiquadric method of a radial basis function was coincident with the spatial distribution obtained by constructing a triangulated irregular network of the study area through the raster mathematics. In addition, better interpolation results can be obtained by setting the optimal power value provided under the selected condition.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.187-187
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• 2010

공간자료를 다루는 일반적인 과정은 연구자의 정의에 따라 달라질 수 있지만, 일반적으로 자료 수집, 자료 구축, 분석 및 결과 도출의 일반적인 과학/공학적 분석 절차와 유사하다. 산업체의 관점에서 볼 때, 1990년대 초기 국가GIS 사업이 시작될때부터 현재까지는 공인된 자료 구축에 많은 주안점을 두어서 기존 아날로그 자료의 디지털화, 자료 가공, 데이터베이스 구축, 자료의 시각화 등의 일반적인 자료 구축 및 도시에 주안점을 두어왔다. 또한 다양한 공간해상도의 원격탐사 자료와 같이 다중 근원 자료의 이용이 빈번해짐에 따라 공간자료의 갱신 또한 중요한 부분을 차지하고 있다. 그러나, 공간자료를 다루는 일련의 과정이 궁극적으로는 특정 분야에서의 의사 결정보조자료의 제공 등을 지향한다고 간주할 때, "from data to information to knowledge"의 중간 혹은 최종 단계의 결과물을 산출하기 위한 적절한 분석 기술의 개발 및 적용 또한 중요한 부분을 차지한다. 공간분석을 별도의 학문분야로 간주하느냐 아니냐의 문제와는 상관없이, 최근 20년간 공간분석은 GIS 및 원격탐사 분야뿐만 아니라 기본적으로 공간자료를 다루는 많은 응용분야에서 공간자료의 이해와 부가정보의 생산을 위한 중요한 기술 분야로 간주되어 왔다. 공간분석의 여러 응용 분야중에서 환경분야에의 적용 연구는 또한 환경과학이라는 별도의 분야 뿐만 아니라, 기존 학문들인 지리학, 생태학, 지구과학, 사회학, 경제학, 도시 계획 등의 하위분야에서 중요한 방법론으로 자리 잡고 있다. 이 기술 세미나에서는 환경분야에 직간접적으로 활용이 가능한 공간정보 분석 기술의 동향을 지구통계학을 중심으로 소개하고자 한다. 국내에서 크리깅으로 대표되어온 지구통계학은 적용하는 학문 분야에 따라 보다 넓은 의미를 가지는 공간 통계학이라는 용어로 사용되고 있지만, 보다 학문적/기술적 의미로 살펴보면 공간분석의 특화된 분야로 간주할 수 있다. 1950년대 알려진 광상의 위치 정보를 이용하여 은둔 광상의 위치를 추정하기 위해 기본 개념이 소개된 이후에 수학적으로 이론이 1960년대 정립된 지구통계학은 많은 발전을 이루어 현재 다양한 분야에서 적용되고 있다. 그러나 외국과 달리 국내에서는 크리깅을 고급 내삽 기법으로만 간주하여 단순 주제도 작성에 제한적으로 사용하고 있다. 이 기술 세미나에서는 특정 학문분야에서 적용되기 보다는 일반적으로 통용될 수 있는 지구통계학의 기본 개념을 우선 소개한 후에, 국내외 학계에서의 환경주제도 제작과 관련된 주요 응용분야를 소개하고자 한다. 이후에 지구통계학이 적용될 수 있으면서, 다학제적 관점에서의 이슈가 될 수 있는 분야를 제시하고자 한다.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.669-676
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• 2006

Hydrological factors, especially the spatial distribution of interpretation on precipitation is often topic of interest in studying of water resource. The popular methods such as Thiessen method, inverse distance method, and isohyetal method are limited in calculating the spatial continuity and geographical characteristics. This study was intended to overcome those limitations with improved method that will yield higher accuracy. The monthly and yearly precipitation data were produced and compared with the observed daily precipitation to find correlation between them. They were then used as secondary variables in Co-kriging method, and the result was compared with the outcome of existing methods like inverse distance method and kriging method. The comparison of the data showed that the daily precipitation had high correlation with corresponding year's average monthly amounts of precipitation and the observed average monthly amounts of precipitation. Then the result from the application of these data for a Co-kriging method confirmed increased accuracy in the modeling of spatial distribution of precipitation, thus indirectly reducing inconsistency of the spatial distribution of hydrological factors other than precipitation.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.69-79
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• 2013

This paper presents a two-stage geostatistical integration approach that aims at downscaling of coarse scale remote sensing data. First, downscaling of the coarse scale sedoncary data is implemented using area-to-point kriging, and this result will be used as trend components on the next integration stage. Then simple kriging with local varying means that integrates sparse precise observation data with the downscaled data is applied to generate thematic information at a finer scale. The presented approach can not only account for the statistical relationships between precise observation and secondary data acquired at the different scales, but also to calibrate the errors in the secondary data through the integration with precise observation data. An experiment for precipitation mapping with weather station data and TRMM (Tropical Rainfall Measuring Mission) data acquired at a coarse scale is carried out to illustrate the applicability of the presented approach. From the experiment, the geostatistical downscaling approach applied in this paper could generate detailed thematic information at various finer target scales that reproduced the original TRMM precipitation values when upscaled. And the integration of the downscaled secondary information with precise observation data showed better prediction capability than that of a conventional univariate kriging algorithm. Thus, it is expected that the presented approach would be effectively used for downscaling of coarse scale data with various data acquired at different scales.