• Spatial Information Research
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• v.11 no.3
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• pp.251-260
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• 2003

Distribution pattern of seawater intrusion was analyzed from the spatial distribution map of chloride using the geostatistics and CIS analyses. The chloride distribution map made by kriging(ordinary kriging and co-kriging) after exploratory spatial data analysis. Kriging provides an advanced methodology which facilitates quantification of spatial features and enables spatial interpolation. TDS, Na$^{＋}$, Br$^{[-10]}$ were selected as second parameters of co-kriging which is higher value of correlation coefficients between chloride and others groundwater properties. Chloride concentration is highest in yeminchon and coastal area. And result in co-kriging was accurate than ordinary kriging.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.55-63
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• 2015

The need for estimating the real transaction price of land is increasing in order to build foundation for transparent land transaction and fair taxation. This study looked into the applicability of cokriging combining real transaction price of land, altitude and gradient for effective price estimation on the points where the real transaction does not take place in the course of using the real transaction price of land. The real transaction price of land have been estimated using the real transaction materials of Yeongcheon, Gyeongsangbuk-do from January 2012 to June 2014, and the results have been compared with the estimation results of ordinary kriging. As a result of analyzing the mean error and root mean square error (RMSE) of the estimated price and 2,575 verification points, it was found that compared to ordinary kriging, cokriging results were more effective in terms of the real transaction price estimation and actualization. The reason that cokriging is more effective in the real transaction price estimation is because it takes account of altitude and gradient which are the forces influencing the land value.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.13-27
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• 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 Environmental Impact Assessment
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• v.27 no.5
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• pp.447-474
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• 2018

The purpose of this study is to build a new dataset of spatially interpolated climate data of South Korea by performing various geo-statistical interpolation techniques for comparison with the LDAPS grid data of KMA. Among 595 observation data in 2017, 80 % of the total points and remaining 117 points were used for spatial mapping and quantification,respectively. IDW, cokriging, and kriging were performed via the ArcGIS10.3.1 software and Python3.6.4, and each result was then divided into three clusters and four watersheds for statistical verification. As a result, cokriging produced the most suitable grid climate data for instantaneous temperature. For 1-hr accumulated precipitation, IDW was most suitable for expressing local rainfall effects.

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

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.130-134
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• 2008

제주도는 표고가 높은 산악지형으로 이루어져 있으며, 산악지형에서의 강수 발생일수와 강수량 값은 평지보다 월등히 높으므로 수자원 설계시 표고에 따른 강우 보정을 실시한다. 그러나 현재 실무에서 적용되고 있는 시우량 자료를 이용한 표고와 연강우량의 관계에 따른 보정 방법은 여러 문제점을 야기시키고 있다. 이에 본 연구에서는 크리깅 기법을 이용하여 새로운 강우보정 방법을 제시하였으며, 격자형 강우보정계수를 산정하여 보정된 강우를 분포형 모형에 적용하였다. 제주도내 17개 강우관측소 및 제주 재난안전대책본부 41개 관측소의 강우자료를 이용하여 공동크리깅을 수행하였고, 격자 형태의 강우보정계수를 산정하였다. 제주 관측소의 강우자료로 확률강우량을 산정하여 강우보정을 하였고, 분포형 모형에 적용하여 유출량을 산정하였다. 또한, 기존 고도보정 방법 및 HEC-HMS 모형으로 산정된 유출량과 비교하였다. 본 연구에서 제시한 강우보정 방법으로 지속시간에 따른 강우 증가를 고려할 수 있을 뿐만 아니라 고도에 따른 강우보정시 홍수량이 과대 산정되는 문제점을 해결할 수 있었다.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.380-388
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• 2014

Annual average daily traffic(AADT) serves as important basic data in the transportation sector. AADT is used as design traffic which is the basic traffic volume in transportation planning. Despite of its importance, at most locations, AADT is estimated using short term traffic counts. An accurate AADT is calculated through permanent traffic counts at limited locations. This study dealt with estimating AADT using various models considering both the spatial correlation and time series data. Kriging models which are commonly used spatial statistics methods were applied and compared with each model. Additionally the External Universal kriging model, which includes explanatory variables, was used to assure accuracy of AADT estimation. For evaluation of various kriging methods, AADT estimation error, proposed using national highway permanent traffic count data, was analyzed and their performances were compared. The result shows the accuracy enhancement of the AADT estimation.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2016.10a
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• pp.171-171
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• 2016

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.3
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• pp.147-163
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• 2013

The purpose of this study is to compare precipitation distributions in precipitation data sets over South Korea produced by three interpolation methods. The differences of precipitation caused by interpolation methods is an important information when the interpolated precipitation data sets were used in researches such as ecological and hydrological modeling as well as regional climate impact studies. In this study, the precipitation data sets were produced by IDW(Inverse Distance Weighting) and Cokriging in this study and the PRISM(Precipitation-elevation Regressions on Independent Slopes Model) data set obtained from Climate Change Information Center of Korea. The spatial resolution of the precipitation data is 1km. As a result, there was a great precipitation difference caused by interpolation methods in data of mountainous watersheds in general. Especially the difference of monthly precipitation was 10~20% or more in the mountainous watersheds near the Military Demarcation Line dividing North and South Korea, Mt. Sobaik, Mt. Worak, Mt. Deogyu, Mt. Jiri and Taeback Mountain Range. It means that a final result of a research can be affected by adopted interpolation method when an interpolated precipitation data set is used in the research for the these study sites.