Edzer J. Pebesma and Kor de Jonga and David Briggs.
Interactive Visualization of Uncertain Spatial and Spatio-Temporal Data Under Different Scenarios: an Air Quality Example.
In International Journal of Geographical Information Science, vol. 21, no. 5, pp. 515--527, 2007.

Links:

Abstract:

This paper introduces a method for visually exploring spatiotemporal data or predictions that come as probability density functions, e.g. output of statistical models or Monte Carlo simulations, under different scenarios. For a given moment in time, we can explore the probability dimension by looking at maps with cumulative or exceedance probability while varying the attribute level that is exceeded, or by looking at maps with quantiles while varying the probability value. Scenario comparison is done by arranging the maps in a lattice with each panel reacting identically to legend modification, zooming, panning, or map querying. The method is illustrated by comparing different modelling scenarios for yearly NO2 levels in 2001 across the European Union.NONE

Bibtex:

@Article{        pebesma:2007:IVUS,
  Author = 	 {Edzer J. Pebesma and Kor de Jonga and David Briggs},
  title = 	 {Interactive Visualization of Uncertain Spatial and
                  Spatio-Temporal Data Under Different Scenarios: an
                  Air Quality Example},
  journal = 	 {International Journal of Geographical Information
                  Science},
  year = 	 {2007},
  volume = 	 {21},
  number = 	 {5},
  pages = 	 {515--527},
}

Images:

References:

AERTS, J.C.J.H., CLARKE, K.C. and KEUPER, A.D., 2003, Testing popular visualization techniques for representing model uncertainty. Cartography and Geographic Information Science, 30, pp. 249-261.
CHILE` S, J.-P. and DELFINER, P., 1999, Geostatistics, Modeling Spatial Uncertainty (New York:Wiley).
CLEVELAND, W.S., 1993, Visualizing Data (Summit, NJ: Hobart Press).
CLEVELAND, W.S. and MCGILL, M.E., 1988, Dynamic Graphics for Statistics (California:Wadsworth & Brooks Cole).
EHLSCHLAEGER, C.R., SHORTRIDGE, A.M. and GOODCHILD, M.F., 1997, Visualizing spatiald ata uncertainty using animation. Computers & Geosciences, 23, pp. 387-395.
ESRI, 2004, Getting Started With ArcGIS (Redlands, CA: ESRI).
EVANS, B.J., 1997, Dynamic display of spatial data reliability: does it benefit the map user? Computers & Geosciences, 23, pp. 409-422.
GOOVAERTS, P., 1997, Geostatistics for Natural Resources Evaluation (USA: Oxford University Press).
HENGL, T., 2003, Visualisation of uncertainty using the HSI colour model: computations with colours. In Proceedings of the 7th International Conference on GeoComputation, University of Southampton, 8-10 September 2003 (CD-ROM).
HENGL, T., HEUVELINK, G.M.B. and STEIN, A., 2004, A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 122, pp. 75-93.
HEUVELINK, G.B.M., 1998, Error Propagation in Environmental Modelling with GIS (London:Taylor & Francis).
IHAKA, R. and GENTLEMAN, R., 1996, R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5, pp. 299-314.
PEBESMA, E.J. and DE KWAADSTENIET J.W., 1997, Mapping groundwater quality in the Netherlands. Journal of Hydrology, 200, pp. 364-386.
PEBESMA, E.J., KARSSENBERG, D. and DE JONG K., 2000, The stochastic dimension in a dynamic GIS. In Compstat 2000, Proceedings in Computational Statistics, J.G. Bethlehem and P.G.M. van der Heijden (Eds), pp. 379-384 (Heidelberg: PhysicaVerlag).
SWAYNE, D.F., TEMPLE LANG, D., BUJA, A. and COOK, D., 2003, Evolving from XGobi into an Extensible Framework for Interactive Data Visualization, Journal of Computational Statistics and Data Analysis, 43, pp. 423-444.
SWITZER, P., 2000, Multiple simulation of spatial fields. In Accuracy 2000: Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, G.B.M. Heuvelink and MJ.P.M. Lemmens (Eds), pp.521-528 (Delft: Delft University Press).