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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Uncertainty of Spatial Disaggregation Procedures: Conditional Autoregressive Versus Geostatistical Models

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DOI: http://dx.doi.org/10.15439/2016F539

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 449–457 ()

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Abstract. Consider the problem of allocation of spatially correlated gridded data to finer spatial scale, conditionally on covariate information observable in a fine grid. Spatial dependence of the process can be captured with the conditional autoregressive structure, suitable for gridded (areal level) data. Also geostatistical methods, particularly universal kriging, can be used for this purpose. In this study, we compare prediction results as well as prediction standard errors for two disaggregation procedures, based on the inventory of agricultural ammonia emissions reported in Pomeranian Voivodeship of Poland.

References

  1. K. Boychuk and R. Bun, “Regional spatial inventories (cadastres) of GHG emissions in the Energy sector: Accounting for uncertainty,” Climatic Change, vol. 124, pp. 561–574, 2014. http://dx.doi.org/10.1007/s10584-013-1040-9.
  2. R. Bun, K. Hamal, M. Gusti, and A. Bun, “Spatial GHG inventory at the regional level: accounting for uncertainty,” Climatic Change, vol. 103, no. 1-2, pp. 227–244, 2010. http://dx.doi.org/10.1007/s10584-010-9907-5.
  3. U. Dragosits, M. Sutton, C. Place, and A. Bayley, “Modelling the spatial distribution of agricultural ammonia emissions in the UK,” Environ. Pollut., vol. 102, no. S1, pp. 195–203, 1998. doi: 10.1016/S0269-7491(98)80033-X.
  4. H. Song, M. Fuentes, and S. Ghosh, “A comparative study of Gaussian geostatistical models and Gaussian Markov random field models,” J. Multivariate Anal., vol. 99, pp. 1681–1697, 2008. http://dx.doi.org/10.1016/j.jmva.2008.01.012.
  5. T. Misselbrook, T. Van Der Weerden, B. Pain, S. Jarvis, B. Chambers, K. Smith, V. Phillips, and T. Demmers, “Ammonia emission factors for UK agriculture,” Atmos. Environ., vol. 34, pp. 871–880, 2000. http://dx.doi.org/10.1016/S1352-2310(99)00350-7.
  6. G. Velthof, C. van Bruggenb, C. Groenesteinc, B. de Haand, M. Hoogeveene, and J. Huijsmans, “A model for inventory of ammonia emissions from agriculture in the Netherlands,” Atmos. Environ., vol. 46, pp. 248–255, 2012. http://dx.doi.org/10.1016/j.atmosenv.2011.09.075.
  7. P. Barak, B. Jobe, A. Krueger, L. Peterson, and D. Laird, “Effects of long-term soil acidification due to nitrogen fertilizer inputs in Wisconsin,” Plant Soil, vol. 197, pp. 61–69, 1997. http://dx.doi.org/10.1023/A:1004297607070. [Online].
  8. R. Bobbink, M. Hornung, and J. Roelofs, “The effects of airborne nitrogen pollutants on species diversity in natural and semi-natural European vegetation,” J. Ecol., vol. 86, pp. 717–738, 1998. http://dx.doi.org/10.1046/j.1365-2745.1998.8650717.x.
  9. J. Erisman and M. Schaap, “The need for ammonia abatement with respect to secondary PM reductions in Europe,” Environ. Pollut., vol. 129, pp. 159–163, 2004. http://dx.doi.org/10.1016/j.envpol.2003.08.042.
  10. J. Horabik and Z. Nahorski, “Improving resolution of a spatial inventory with a statistical inference approach,” Climatic Change, vol. 124, no. 3, pp. 575–589, 2014. http://dx.doi.org/10.1007/s10584-013-1029-4.
  11. P. Legendre and L. Legendre, Numerical Ecology, ser. Developments in Environmental Modelling. Elsevier Science, 2012. ISBN 9780444538697
  12. European Environment Agency, “Corine Land Cover 2000,” http://www.eea.europa.eu/data-and-maps/data, 2000.
  13. J. Besag, “Spatial interaction and the statistical analysis of lattice systems (with discussion),” J. Roy. Stat. Soc. B, vol. 36, pp. 192–236, 1974. [Online]. Available: http://www.jstor.org/stable/2984812
  14. ——, “Statistical analysis of non-lattice data,” J. Roy. Stat. Soc. D-Sta., vol. 24, no. 3, pp. 179–195, 1975. http://dx.doi.org/10.2307/2987782.
  15. N. Cressie, Statistics for spatial data, ser. Wiley series in probability and mathematical statistics: Applied probability and statistics. J. Wiley, 1993. ISBN 9780471002550. [Online]. Available: http://books. google.pl/books?id=4SdRAAAAMAAJ
  16. A. Gelfand, P. Diggle, P. Guttorp, and M. Fuentes, Handbook of Spatial Statistics, ser. Chapman & Hall/CRC Handbooks of Modern Statistical Methods. Taylor & Francis, 2010. ISBN 9781420072884
  17. J. Horabik and Z. Nahorski, “The Cramer-Rao lower bound for the estimated parameters in a spatial disaggregation model for areal data,” in Intelligent Systems 2014, P. Angelov, K. Atanassov, L. Doukovska, M. Hadjiski, V. Jotsov, and J. Kacprzyk, Eds. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11313-5. ISBN 978-3-319-11312-8 pp. 661–668.
  18. S. Banerjee, B. Carlin, and A. Gelfand, Hierarchical Modeling and Analysis for Spatial Data, ser. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Taylor & Francis, 2004. ISBN 9780203487808. [Online]. Available: http://books.google.pl/books?id=YqpZKTp-Wh0C
  19. M. Stein, Interpolation of Spatial Data. Some Theory for Kriging, ser. Springer Series in Statistics. Springer, New York, 1999.
  20. P. Ribeiro Jr. and P. Diggle, “geoR: a package for geostatistical analysis,” R-NEWS, vol. 1, no. 2, pp. 15–18, 2001. [Online]. Available: http://cran.R-project.org/doc/Rnews