• Journal of the Korean Association of Geographic Information Studies
• /
• v.13 no.4
• /
• pp.138-147
• /
• 2010

As inaccurate traffic volume prediction may result in inadequate transportation planning and design, traffic volume prediction based on traffic volume data is very important in spatial decision making processes such as transportation planning and operation. In order to improve the accuracy of traffic volume prediction, recent studies are using the geostatistical approach called kriging and according to their reports, the method shows high predictability compared to conventional methods. Thus, this study estimated traffic volume data for St. Louis in the State of Missouri, USA using the kriging method, and tested its accuracy by comparing the estimates with actual measurements. In addition, we suggested a new method for enhancing the accuracy of prediction by the kriging method. In the new method, we estimated traffic volume data: first, by applying anisotropy, which is a characteristic of traffic volume data appearing in determining variogram factors; and second, by performing co-kriging analysis using interstate highway, which is in a high spatial correlation with traffic volume data, as a secondary variable. According to the results of the analysis, the analysis applying anisotropy showed higher accuracy than the kriging method, and co-kriging performed on the application of anisotropy produced the most accurate estimates.

• Journal of the Korean earth science society
• /
• v.34 no.1
• /
• pp.13-27
• /
• 2013

Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.1
• /
• pp.51-66
• /
• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.

• Journal of the Korean Geographical Society
• /
• v.43 no.6
• /
• pp.1002-1015
• /
• 2008

We investigate the potential of digital elevation model and multivariate geostatistical kriging in mapping of temperature and rainfall based on sparse weather station observations. By using elevation data which have reasonable correlation with temperature and rainfall, and are exhaustively sampled in the study area, we try to generate spatial distributions of temperature and rainfall which well reflect topographic effects and have less smoothing effects. To illustrate the applicability of this approach, we carried out a case study of Jeju island using observation data acquired in January, April, August, and October, 2005. From the case study results, accounting for elevation via colocated cokriging could reflect detailed topographic characteristics in the study area with less smoothing effects. Colocated cokriging also showed much improved prediction capability, compared to that of traditional univariate ordinary kriging. According to the increase of the magnitude of correlation between temperature or rainfall and elevation, much improved prediction capability could be obtained. The decrease of relative nugget effects also resulted in the improvement of prediction capability.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.879-879
• /
• 2012

강우현상의 공간적 변동성에 대한 해석은 수자원 계획 및 관리를 위해 중요한 관심사가 되고 있다. 일반적으로 우리가 얻을 수 있는 강우자료는 한 지점에 설치되어 있는 우량계에 의한 관측된 지점강우량자료이다. 기존의 집중형 수문모형이 유출과정의 공간적인 분포 및 변화를 유역단위로 평균화해서 취급하는 개념기반의 모형임에 반해서 분포형 수문모형은 유역을 수문학적으로 균일한 매개변수를 갖는 소유역 또는 격자망으로 구분하여 적용하는 것으로, 도시화 등 토지이용의 변화나 기타 유역내의 물리적인 특성의 변화가 수문과정에 미치는 영향을 잘 모의할 수 있다. 따라서 본 연구에서는 Vflo$^{TM}$ 분포형 강우-유출 모형과 IDW, Ordinary Kriging, Thiessen 등의 강우 분포 기법을 이용하여 낙동강 제 1지류인 금호강의 동촌 수위관측소 유역($1,544km^2$)을 출구로 하여 강우-유출모의를 하였다. 이를 위하여 강우-유출에 영향을 주는 매개변수를 선정하고 동촌 수위관측소의 실측 유량자료를 바탕으로 하여 IDW, Kriging, Thiessen 등의 면적강우량 산정방법별로 모형의 보정(2007, 2009) 및 검증(2010)을 실시하였다. 모의 된 유출량과 실측유량의 상관성은 결정계수 $R^2$에서 IDW 과 Kriging의 경우 0.95 ~ 0.99의 상관성을 나타냈으며 Thiessen 의 경우 0.94 ~ 0.99의 값을 나타냈다. Nash-Sutcliffe 모형효율은 IDW의 경우 0.95 ~ 0.98, Kriging의 경우 0.94 ~ 0.99를 나타냈으며 Thiessen의 경우는 0.90 ~ 0.98의 모형효율을 나타내었다. 이때 포화투수계수와 조도계수가 전체 유량과 첨두시간에 영향을 주었다. 호우사상을 선정하여 검보정을 실시 한 결과, 유역의 유출 모의를 수행하였을 때 선행강우량에 따라서 토양의 침투능에 영향을 많이 주고 있기 때문에, 선행 토양함수조건(Antecedent Moisture Condition: AMC)으로 분류한 뒤에 AMC 조건에 따라서 유출-모의를 수행하는 것이 타당하다고 판단된다.

• Korean Journal of Remote Sensing
• /
• v.26 no.1
• /
• pp.29-38
• /
• 2010

It is necessary to develop an integration model which can account for various data acquired at different measurement scales in environmental thematic mapping with high-resolution ground survey data and relatively low-resolution remote sensing data. This paper presents and applies a multi-scale geostatistical methodology for downscaling of thematic maps generated from lowresolution remote sensing data. This methodology extends a traditional ordinary kriging system to a block kriging system which can account for both ground data and remote sensing data which can be regarded as point and block data, respectively. In addition, stochastic simulation based on block kriging is also applied to describe spatial uncertainty attached to the downscaling. Two downscaling experiments including SRTM DEM and MODIS Leaf Area Index (LAI) products were carried out to illustrate the applicability of the geostatistical methodology. Through the experiments, multi-scale geostatistics based on block kriging successfully generated relatively high-resolution thematic maps with reliable accuracy. Especially, it is expected that multiple realizations generated from simulation would be effectively used as input data for investigating the effects of uncertain input data on GIS model outputs.

• Korean Journal of Remote Sensing
• /
• v.22 no.6
• /
• pp.581-593
• /
• 2006

Various geological thematic maps have been generated by interpolating sparsely sampled ground survey data and geostatistical kriging that can consider spatial correlation between neighboring data has widely been used. This paper applies multi-variate geostatistical algorithms to integrate secondary information with sparsely sampled ground survey data for geological thematic mapping. Simple kriging with local means and kriging with an external drift are applied among several multi-variate geostatistical algorithms. Two case studies for spatial mapping of groundwater level and grain size have been carried out to illustrate the effectiveness of multi-variate geostatistical algorithms. A digital elevation model and IKONOS remote sensing imagery were used as secondary information in two case studies. Two multi-variate geostatistical algorithms, which can account for both spatial correlation of neighboring data and secondary data, showed smaller prediction errors and more local variations than those of ordinary kriging and linear regression. The benefit of applying the multi-variate geostatistical algorithms, however, depends on sampling density, magnitudes of correlation between primary and secondary data, and spatial correlation of primary data. As a result, the experiment for spatial mapping of grain size in which the effects of those factors were dominant showed that the effect of using the secondary data was relatively small than the experiment for spatial mapping of groundwater level.

• Journal of the Korean association of regional geographers
• /
• v.17 no.4
• /
• pp.432-442
• /
• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• Spatial Information Research
• /
• v.20 no.4
• /
• pp.17-27
• /
• 2012

This study was performed to evaluate the spatial distribution of heavy metals using principal component analysis and Ordinary Kriging technique in the Gangwon Mine site. In the soils from the sub soil, the contents of Zn and Ni in the PC1 were gradually dispersed from south to north direction, while the components of Cd and Hg in the PC2 showed an increase significantly from middle-south area in the Gangwon Mine site. According to the cluster analysis, pollutant metals of As and Cu were presented a strong spatial autocorrelation structure in cluster D. The concentration of As was 0.83mg/kg and shown to increase from the south to north direction. The spatial distribution maps of the soil components using geostatistical method might be important in future soil remediation studies and help decision-makers assess the potential health risk affects of the abandoned mining sites.

• Journal of Astronomy and Space Sciences
• /
• v.25 no.3
• /
• pp.267-282
• /
• 2008

The GNSS (Global Navigation Satellite System) signal is delayed by the neutral atmosphere at the troposphere, so that the delay is one of major error sources for GNSS precise positioning. The tropospheric delay is an integrated refractive index along the path of GNSS signal. The refractive index is empirically related to standard meteorological variables, such as pressure, temperature and water vapor partial pressure, therefore the tropospheric delay could be calculated from them. In this paper, it is presented how to generate meteorological data where observation cannot be performed. KASI(Korea Astronomy & Space Science Institute) has operated 9 GPS (Global Positioning System) permanent stations equipped with co-located MET3A, which is a meteorological sensor. Meteorological data are generated from observations of MET3A by Ordinary Kriging. To compensate a blank of observation data, simple models which consider periodic characteristics for meteorological data, are employed.