Jeroen C. J. H. Aerts and Keith C. Clarke and Alex D. Keuper.
Testing Popular Visualization Techniques for Representing Model Uncertainty.
In Cartography and Geographic Information Science, vol. 30, no. 3, pp. 249--261, 2003.


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Abstract:

Many land allocation issues, such as land-use planning, require input from extensive spatial databases and involve complex decision-making. Spatial decision support systems (SDSS) are designed to make these issues more transparent and to support the design and evaluation of land allocation alternatives. In this paper we analyze techniques for visualizing uncertainty of an urban growth model called SLEUTH, which is designed to aid decision-makers in the field of urban planning and fits into the computational framework of an SDSS. Two simple visualization techniques for portraying uncertainty-static comparison and toggling-are applied to SLEUTH results and rendered with different background information and color schemes. In order to evaluate the effectiveness of the two visualization techniques, a web-based survey was developed showing the visualizations along with questions about the usefulness of the two techniques. The web survey proved to be quickly accessible and easy to understand by the participants. Participants in the survey were mainly recruited among planners and decision-makers. They acknowledged the usefulness of portraying uncertainty for decision-making purposes. They slightly favored the static comparison technique over toggling. Both visualization techniques were applied to an urban growth case study for the greater Santa Barbara area in California, USA.

Bibtex:

@Article{        aerts:2003:TPVU,
  author = 	 {Jeroen C. J. H. Aerts and Keith C. Clarke and Alex D. Keuper},
  title = 	 {Testing Popular Visualization Techniques for Representing Model Uncertainty},
  journal =      {Cartography and Geographic Information Science},
  volume =       {30},
  number =       {3},
  pages =        {249--261},
  year =         {2003},
}

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References:

Aerts, J.C.J.H., and G.B.M. Heuvelink. 2002. Using simulated annealing for resource allocation. International Journal of GIS 16(6): 571-87.
Aerts, J.C.J.H. 2002. Spatial decision support for resource allocation. Integration of optimization, uncertainty analysis and visualization techniques. Dissertation, University of Amsterdam. Amsterdam, The Netherlands: Thela Thesis publishers.
Armstrong, M.P., P.J. Densham, P. Lolonis, and G. Rushton. 1992. Cartographic displays to support locational decision making. Cartography and Geographic Information Systems 19(3): 154-64.
Bastin, L., J. Wood, and P.F. Fischer. 2000. Visualising uncertainty in fuzzy thematic classifications of multispectral satellite imagery.In: W. Shi, M.F. Goodchild, and P.F. Fisher (eds), International Symposium on Spatial Data Quality, Hong Kong. pp. 243-52.
Beard, M.K., and B.P. Buttenfield. 1991. Visualization of spatial data quality. Scientific Report for the Specialist Meeting June 1991. NCGIA Technical Paper 91-26, Santa Barbara, California.
Beard, M.K., and B.P. Buttenfield. 1999. Detecting and evaluating errors by graphical methods. In: P.A.Longley, M.F. Goodchild, D.J. Maguire, and D.W. Rhind (eds), Geographic information systems. pp. 219-33.
Bertin, J. 1983. Semiology of graphics. Madison, Wisconsin: The University of Wisconsin Press.
Brewer, C.A. 1999a. Color use guidelines for mapping and visualization. [http://www.personal.psu.edu/faculty/c/a/ cab38/ColorSchHome.html. Last visited June 2003)].
Brewer, C. A. 1999b. Color use guidelines for data representation. Proceedings of the Section on Statistical Graphics. American Statistical Association, Alexandria, Virginia. pp. 55-60.
Buttenfield, B., and M.K. Beard. 1994. Graphical and geographical components of data quality. In: H. M.Hernshaw and D. J. Unwin (eds), Visualization in geographical information systems. New York, New York: J. Wiley. pp. 150-7.
Candau, J., S. Rasmussen, and K.C. Clarke. 2000. A coupled cellular automaton model for land use/land cover dynamics. GIS/EM4 Conference, Banff Alberta 2000. [http://www.Colorado.EDU/research/cires/banff/ pubpapers/94/. Last visited June 2003)].
Clarke, K.C., and L.J. Gaydos. 1998. Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/ Baltimore. International Journal of Geographical Information Science 12(7): 699-714.
Clarke, K.C., P.D. Teague, and H.G. Smith. 2000. Virtual depth-based representation of cartographic uncertainty. In: W. Shi, M.F. Goodchild, and P.F. Fisher (eds), International Symposium on Spatial Data Quality, Hong Kong. pp. 253-9.
Cleaves, D.A. 1995. Assessing and communicating uncertainty in decision support systems: Lessons from an ecosystem policy analysis. AI Applications 9(3): 87- 102.
Crossland, M.D., B.E. Wynne, and W.C. Perkins. 1995. Spatial decision support systems: An overview of technology and a test of efficacy. Decision Support Systems 14: 219-35.
Davis, T.J., and C.P. Keller. 1997. Modelling and visualizing multiple spatial uncertainties. Computers and Geosciences 23(4): 397-408.
Drecki, I. 2000. Visualizing of uncertainty in geographic data. In: W. Shi, M.F. Goodchild, and P.F. Fisher (eds), International Symposium on Spatial Data Quality, Hong Kong. pp. 260-71.
Ehlschlaeger, Ch.R. 2002. Representing multiple spatial statistics in generalized elevation uncertainty models: Moving beyond the variogram. International Journal of Geographic Information Science 16(3): 259-85.
Ehlschlaeger, Ch.R., A.M. Shortridge, and M.F. Goodchild. 1997. Visualizing spatial data uncertainty using animation. Computers and Geosciences 23(4): 387-395.
Ehlschlaeger, Ch.R., and M.F. Goodchild. 1994. Uncertainty in spatial data: Defining, visualizing and managing data errors. Proceedings of GIS/LIS, Phoenix, Arizona. pp. 246-53.
Evans, B.J. 1997. Dynamic display of spatial data reliability: Does it benefit the map user? Computers & Geosciences 23(4): 409-22.
Fisher, P.F. 1994. Visualization of the reliability in classified remotely sensed images. Photogrammetric Engineering and Remote Sensing 60: 905-10.
Fowler, F. 1993. Survey research methods, 2 nd ed. Newbury Park, California: Sage.
Goodchild, M.F., B. Buttenfield, and J. Wood. 1994. Introduction to visualizing data validity. In: H. M. Hernshaw, , and D. J. Unwin (eds), Visualization in geographical information systems. New York, New York: J. Wiley. pp.141-9.
Grabaum, R., and B.C. Meyer. 1998. Multi criteria optimization of landscapes using GIS-based functional assessments. Landscape and Urban Planning 43: 21-34.
Heuvelink, G.B.M. 1998. Error propagation in environmental modeling with GIS. London, U.K.: Taylor & Francis.
Hunter, G.J., and M.F. Goodchild. 1995. Dealing with error in spatial databases: A simple case study. Photogrammetric Engineering & Remote Sensing 61(5): 529-37.
Hunter, G.J. 1999. Managing uncertainty in GIS. In: P.A. Longley, M.F. Goodchild, D.J. Maguire, and D.W. Rhind (eds), Geographic information systems. New York, New York: J. Wiley. pp. 633-41.
Kraak, M.J. 1999. Visualising spatial distributions. In: P.A. Longley, M.F. Goodchild, D.J. Maguire, and D.W. Rhind (eds), Geographic information systems. New York, New York: J. Wiley. pp. 157-73.
Leitner, M., and B.P. Buttenfield 1997. Cartographic guidelines for visualizing attribute accuracy. Proceedings, AUTO-CARTO 13, Seattle, Washington. Pp. 184-94.
Leitner M., and B.P Buttenfield. 2000. Guidelines for the display of attribute certainty. Cartography and Geographic Information Science 27(1): 3-14.
MacEachren, A.M. 1992. Visualizing uncertain information. Cartographic Perspective 13: 10-19. MacEachren, A.M. 1994. Some truth with maps: A primer on symbolization & design. Association of American Geographers, Washington, D.C.
MacEachren, A.M., and M.J. Kraak. 1997. Exploratory cartographic visualization: Advancing the agenda. Computers & Geosciences 23(4): 335-43.
MacEachren, A.M., M. Wachowicz, R. Edsall, and R. Masters. 1999. Constructing knowledge from multivariate spatiotemporal data: Integrating geographical visualization with knowledge discovery in database methods. International Journal of Geographical Information Science 13(4): 311-34.
Nedovic-Budic,Z. 1999. Geographic information science implications for urban and regional planning. URISA Journal 12(2): 81-93.
Pang, A.T., C.M. Wittenbrink, and S. Lodha. 1997. Approaches to uncertainty visualization. The Visual Computer 13: 370-90.
Paoli, G., and B. Bass. 1997. Editorial: Climate change and variability, uncertainty and decision making. Journal of Environmental Management 49(1): 1-6.
Rosenthal, R., and R. Rosnow. 1991. Essentials of behavioral research: Methods and data analysis. New York, New York: McGraw-Hill.
SBC (Santa Barbara County). 2000. Santa Barbara County 2030 land and population. Planning and Development Division. [http://www.co.santa- barbara.ca.us/plandev/. (Last visited June 2003)].
Schatten, A. 1999. Cellular automata, digital worlds. [http://www.ifs.tuwien.ac.at/~aschatt/info/ca/ca.html. Last visited June 2003)].
Silva, E.A., and K.C. Clarke. 2001. Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems 26(6): 525-52.
Van der Wel, F.J.M., R.M. Hootsmans, and F. Ormeling. 1994. Visualization of data quality. In: A.M. MacEachren and D.R. Fraser Taylor (eds), Visualization in modern cartography. New York, New York: Elsevier Scientific.
Van der Wel, F.J.M, and L.C. Van der Gaag. 1998. Visual exploration of uncertainty in remote sensing classification. Computers & Geosciences 24(4): 335-43.
Watkins, E.T. 2000. Improving the analyst and decision- makers perspective through uncertainty visualization. Thesis AFIT/GCS/ENG/00M-24.
Department of the Air Force Air University. Air force Institute of Technology, Wright Patterson Air Force Base, Ohio. [http://www.au.af.mil/au/database/research/ay2000/ afit-gcs-eng-00m-24.htm. (Last visited June 2003)].