Lydia Gerharz and Edzer Pebesma and Harald Hecking.
Visualizing Uncertainty in Spatio-Temporal Data.
In Spatial Accuracy 2010, pp. 169--172, 2010.

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Abstract:

Visualization methods to show uncertainties in geospatial data are important tools for communication. Methods have been mainly developed for marginal probability distribution functions (pdfs) describing uncertainties independently for each location in space and time. Often uncertainties can be described better by joint pdfs, including the spatio-temporal dependencies of uncertainties. In this paper, methods for visualization of marginal distributions for space-time grids or features were compared to the case where the full joint distribution needs to be considered in order to find typical or rare spatial or spatio-temporal patterns, such as in ensemble weather forecasts. A number of statistical methods to sample representative realizations from a collection of model ensembles based on the spatio-temporal dependencies such as Mahalanobis distance were investigated and compared. We conclude that taking the full joint probability into account by showing a set of selected ensembles besides visualization methods using marginal distributions is helpful to understand the spatio-temporal structure.

Bibtex:

@InProceedings{  gerharz:2010:VUST,
  author = 	 {Lydia Gerharz and Edzer Pebesma and Harald Hecking},
  title = 	 {Visualizing Uncertainty in Spatio-Temporal Data},
  booktitle =    {Spatial Accuracy 2010},
  pages = 	 {169--172},
  year = 	 {2010},  
}

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References:


Hengl, T. (2003). Visualisation of uncertainty using the HIS colour model: Computations with colours. In: Proceedings of the 7th International Conference on GeoComputation, Southampton, United Kingdom. CDRom, pp. 8-17.
Heuvelink, G.B.M. (1993). Error propagation in quantitative spatial modelling: applications in geographical information systems. Nederlandse geografische studies, Doctoral thesis, University Utrecht.
Heuvelink, G.B.M, Brown, J.D., van Loon, E.E. (2007). A probabilistic framework for representing and simulating uncertain environmental variables. International Journal of Geographical Information Science, 21(5):497-513.
MacEachren, A.M. (1992). Visualizing uncertain information. Cartographic Perspectives 13:10-19. MacEachren, A.M., Robinson, A., Hopper, S., Gardner, S., Murray, R.,
Gahegan, M., Hetzler, E. (2005). Visualizing geospatial information uncertainty: what we know and what we need to know. Cartography and Geographic Information Science, 32(3):139-160.
Mahalanobis, P.C. (1936). On the generalised distance in statistics. Proceedings of the National Institute of Science of India,12:49-55.
Pebesma, E.J., de Jong, K. and Briggs, D.J. (2007). Interactive visualisation of uncertain spatial and spatio-temporal data under different scenarios: an air quality example. International Journal of Geographical Information Science, 21(5):515-527.
Switzer, P. (2000). Multiple simulation of spatial fields. In: G. Heuvelink and M. Lemmens (Eds) Accuracy 2000: Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resource & Environmental Sciences: Amsterdam, July 2000 (pp. 629-635). Delft: Delft Univ Press