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http://dx.doi.org/10.5351/KJAS.2013.26.1.093

On the Hierarchical Modeling of Spatial Measurements from Different Station Networks  

Choi, Jieun (Department of Statistics, Sungshin Women's University)
Park, Man Sik (Department of Statistics, Sungshin Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.26, no.1, 2013 , pp. 93-109 More about this Journal
Abstract
Geostatistical data or point-referenced data have the information on the monitoring stations of interest where the observations are measured. Practical geostatistical data are obtained from a wide variety of observational monitoring networks that are mainly operated by the Korean government. When we analyze geostatistical data and predict the expectations at unobservable locations, we can improve the reliability of the prediction by utilizing some relevant spatial data obtained from different observational monitoring networks and blend them with the measurements of our main interest. In this paper, we consider the hierarchical spatial linear model that enables us to link spatial variables from different resources but with similar patterns and guarantee the precision of the prediction. We compare the proposed model to a classical linear regression model and simple kriging in terms of some information criteria and one-leave-out cross-validation. Real application deals with Sulfur Dioxide($SO_2$) measurements from the urban air pollution monitoring network and wind speed data from the surface observation network.
Keywords
Sulfur Dioxide; wind speed; hierarchical model; kriging; spatial association; cross-validation;
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Times Cited By KSCI : 10  (Citation Analysis)
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1 Banerjee, S., Gelfand, A. E. and Carlin, B. P. (2004). Hierarchical Modeling and Analysis for Spatial Data, Boca Raton: Chapman & Hall/CRC.
2 Cho, H. L. and Jeong, J. C. (2006). Application of spatial interpolation to rainfall data, The Journal of GIS Association of Korea, 14, 29-41.
3 Cho, J. Y., Choi, S. B. and Kim, K. K. (2001). Comparative study on the prediction of geo-statistics with general statistics, The Journal of Korea Data Analysis Society, 3, 41-49.
4 Choi, J. S. and Park, M. S. (2009). Spatial prediction based on the Bayesian kriging with Box-Cox transformation, Communications of the Korean Statistical Society, 16, 851-858.   과학기술학회마을   DOI   ScienceOn
5 Cressie, N. A. C. (1993). Statistics for Spatial Data, John Wiley & Sons, New York.
6 Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation, Oxford University Press, New York.
7 Han, J. S., Kim, Y. M., Ahn, J. Y., Kong, B. J., Choi, J. S., Lee, S. U. and Lee, S. J. (2004). Spatial distribution and variation of long-range transboundary air pollutants flux during 1997-2004, Journal of Korean Society for Atmospheric Environment, 22, 99-106.   과학기술학회마을
8 Heo, T. Y. and Park, M. S. (2009). Bayesian spatial modeling of precipitation data, The Korean Journal of Applied Statistics, 22, 425-433.   과학기술학회마을   DOI   ScienceOn
9 Heo, T. Y., Park, M. S., Eom, J. K. and Oh, J. S. (2007). A study on the prediction of traffic counts based on shortest travel path, The Korean Journal of Applied Statistics, 20, 459-473.   과학기술학회마을   DOI   ScienceOn
10 Hohn, M. E. (1999). Geostatistics and Petroleum Geology, Kluwer Academic Publishers.
11 Huh, T. Y., Suh, E. H. and Kwon, W. T. (2004). Spatial analysis on the rainfall data by utilizing Variogram models, The Journal of Korea Data Analysis Society, 6, 473-491.
12 Jeong, S. H., Park, M. S. and Kim, K. W. (2010). Spatial prediction of wind speed data, The Korean Journal of Applied Statistics, 23, 345-356.   과학기술학회마을   DOI   ScienceOn
13 Jung, J. Y., Jin, S. H. and Park, M. S. (2008). Precipitation analysis based on spatial linear regression model, The Korean Journal of Applied Statistics, 21, 1093-1107.   과학기술학회마을   DOI   ScienceOn
14 Kim, B. S., Ku, C. Y. and Choi, J. M. (2010). Population distribution estimation using regression-kriging model, The Korean Geographical Society, 45, 806-819.   과학기술학회마을
15 Ministry of Environment (2007). Annual Report of Air Quality in Korea.
16 Kim, H. Y. (2010). A study on the improvement of the accuracy of photovoltaic facility location using the geostatistical analysis, Journal of the Korean Association of Geographic Information Studies, 13, 146-156.
17 Kim, K. K. and Choi, S. B. (2000). A study on the prediction of Geo-statistical methods using environmental data, The Journal of Korea Data Analysis Society, 4, 499-510.
18 Korea Meteorological Administration Homepage. http://www.kma.go.kr.
19 National Institute of Environmental Research. http://www.nier.go.kr.
20 Park, N. W. and Jang, D. H. (2008). Mapping of temperature and rainfall using DEM and multivariate kriging, The Korean Geographical Society, 43, 1002-1015.   과학기술학회마을
21 Schabenberger, O. and Gotway, C. A. (2005). Statistical Methods For Spatial Data Analysis. Boca Raton: Chapman & Hall/CRC.
22 Shin, K. I., Choi, B. H. and Lee, S. E. (2007). Evaluations of small area estimations with/without spatial terms, The Korean Journal of Applied Statistics, 20, 229-244.   과학기술학회마을   DOI   ScienceOn