Geovisualization. CSISS Workshop, Ann Arbor. May Mark Gahegan, GeoVISTA Center, Department of Geography, Penn State university

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1 Geovisualization CSISS Workshop, Ann Arbor. May 2001 Mark Gahegan, GeoVISTA Center, Department of Geography, Penn State university Introduction Visualization is an emerging science that draws from a rich and diverse research literature, including: computer science (graphics, geometry, software engineering), statistics (exploratory data analysis, data presentation), and cognitive science (human perception and cognition). Visualization can be regarded as a form of interactive display technology, with potential to improve the efficiency and effectiveness of communication between the machine and the user. Geovisualization (visualization of geographic information) provides an approach to analysis that is based around collaboration between the machine and the geographer, by providing images of data from a variety of perspectives that can engage the deep, head-knowledge of the human expert. It is finding application in a wide variety of roles, ranging from the generation of photo-realistic scenes representing alternative realities (such as the planned redevelopment of a city streetscape) to the interactive exploration of highly abstract data-spaces in the search for unanticipated clusters (such as might be indicative of a specific relationship between a population and a disease). Outline 1. A brief history 2. Scope: the many roles visualization can play in geographical analysis. 3. Inference: Ways of thinking and the support that geovisualization can provide to aid the knowledge discovery process. 4. Different approaches and metaphors for information visualization. 5. Perception and cognition: communicating effectively with visual media. 6. Some practical issues: available systems, languages, computers. 7. Future directions in geovisualization: collaborative knowledge construction, augmented reality, ubiquitous devices. 1. History Visualization derives from research within many disciplines: computer science (graphics, geometry, system engineering) Palamidese, Treinish statistics (exploratory data analysis, data presentation) Cleveland and McGill, MacDougall, Asimov, Cook Cartography and graphical design (symbolization, layout). Tufte, Tukey, Bertin, MacEachren Cognitive science (human perception and cognition) Gordon, Triesman, Bertin, Rheingans 2. Scope, the many roles of visualization Visualization has found a variety of uses within the Social Sciences, including:

2 representing spatio-temporal models (e.g. study of traffic flow, migration, gentrification, spread of disease) data presentation and uncertainty analysis, exploratory analysis and knowledge discovery urban planning (alternate reality). Why Visualize? images are engaging we can see more we can see better Highly Multivariate Analysis Geospatial datasets are large, complex, heterogeneous and becoming more so Data has an increasing tendency to be tagged with some form of geographic locator new datasets keep emerging Attempting to find structure and pattern in such a complex attribute space presents many methodological and practical problems Several approaches to analysis are possible: expert or model driven (deductive) example driven (inductive) exploratory (abductive). 3. Inference ways of thinking Visually encoding data Geographic datasets may contain huge numbers of patterns or relationships. Many (most?) will be entirely meaningless for a given analysis goal... The task of searching for some signal or pattern can be restated as perceiving a relevant visual stimulus (Visual popout). To achieve popout, concepts of interest require a differentiable visual stimulus--that allows the observer to home in on these patterns. Perceptual knowledge is very useful here Many different styles of visualization have been proposed. Three of the most popular are: Graph-based Iconographic Landscape (compositional) Graph-based methods Iconographic Methods 5. Perception and cognition: communicating effectively with visual media Why does visualization work? For many (but not all), images are more immediately understood than text or figures Visualization is able to engage the expert s deep conceptual models and reasoning abilities

3 and able to make use of the human s superior abilities to interpret patterns and structures. Mixed initiative systems -- able to integrate human-based expertise with the capabilities of the computer. BUT What we understand from a visualization depends on many factors... Human Factors Education Experience Culture Individual differences in visual systems Humans introduce a degree of non-determinism, and bias we do not all see the same. The visual assignment problem 1. The choice of data to display. 2. The assignment of that data to the visual variables, temporal behavior or scene properties provided. 3. The mappings used to transform data values to these visual, temporal or scene variables. 4. The non-linear perceptual hardware in our visual systems. 5. The difficulty in differentiating patterns because they may visually interfere with each other. 6. The very large number of irrelevant patterns that a complex geographic dataset may contain 7. or that may appear as a consequence of the transformations performed on the data. 8. The varying biases, assumptions and expertise that different users might bring. Visual Assignment makes a difference Mixing visual and computational approaches Mixed initiative systems use a variety of approaches to analysis. GeoVISTA Studio is one example visualization, statistical analysis and artificial intelligence (machine learning) techniques are applied together It is possible to monitor strategies used for exploring data and identify those that are successful 6. Some practical issues: available systems, languages, computers Systems and languages: Graphics libraries (tcl/tk, ENVI/IDL, Matlab, VRML, Java 3D) Visualization environments (IBM Data Explorer, Iris Explorer, AVS) Geographically oriented environments: ArcView, Descartes, GeoVISTA Studio. Computers: the Need for Speed

4 Visualization is a heavyweight application but no longer the domain of the special-purpose machine Interaction requires a minimum rendering throughput of 10 frames per second Graphics hardware development is progressing faster than Moore s law currently. 7. Current and future directions in Geovisualization Immersive reality Immersadesk, virtual workbenches, caves Collaborative knowledge construction allow groups of experts to examine data from different perspectives Visualization-based co-laboratories are under development (e.g. Pacific Northwest labs). Augmented reality and ubiquitous devices Supplementing vision with additional digital information Retinal projection headsets, position and orientation tracking Applications include: utilities maintenance, navigation, historical guides and many others. Annotated bibliography / notes from this talk is online at: GeoVISTA Studio can be downloaded from: Some useful references General Visualization Buttenfield, B. P. and Mackaness, W. A. (1991). Visualisation. In: Geographical Information Systems (Ed. Maguire, D. J., Goodchild, M. F. and Rhind, D. W.) Longman, Vol., Ch. 28, pp Card, S., J. Mackinlay, and B. Shneiderman Information visualization. In: Card, S., J. Mackinlay, and B. Shneiderman (eds), Readings in information visualization. San Francisco, California: Morgan-Kaufmann. pp Duclos, A. M. and Grave, M. (1993). Reference Models and Formal Specification for Scientific Visualisation. In: Palamidese, P. (Ed) Scientific Visualisation: Advanced Software Techniques, pp Mackinlay J. D. (1986). Automating the Design of Graphical Presentations of Relational Information. ACM Trans. Graphics, Vol. 5, No. 2, pp McCormick, B. H., Defanti, T. A. and Brown, M. D. (1987). Visualisation in scientific computing, SIGGRAPH Computer Graphics Newsletter, Vol. 21, No. 6. Mihalisin, T., J. Timlin, and J. Schwegler Visualizing multivariate functions, data and distributions. IEEE Computer Graphics and Applications 19(13): Palamidese, P. (Ed) (1993). Scientific Visualisation: Advanced Software Techniques. Robertson, P. K A methodology for choosing data representations. IEEE Computer Graphics and Applications 11(13):

5 Senay, H., and Ignatius, E Rules and principles of scientific data visualization. [ Senay, H. and Ignatius, E. (1994). A knowledge-based System for Visualisation Design. IEEE Computer Graphics and Applications. pp Perception, Cognition & Graphical design Bertin, J. (1985). Graphical Semiology. University of Wisconsin Press, Madison, Wisconsin, USA. Chernoff, H The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association 68: Gordon, I. E., 1989, Theories of Visual Perception, (Chichester, UK: Wiley). Grinstein, G. and Levkowitz, H. (Eds.) (1995). Perceptual Issues in Visualisation, Springer- Verlag, Berlin, Germany. Keller, P. R. and Keller M. M. (1993). Visual Cues: Practical Data Visualisation, IEEE Press, Los Alamitos, CA, USA. Livingstone, M. and Hubel, D. (1988). Segregation of form, colour, movement and depth: anatomy, physiology and perception. Science, Vol. 240, pp Maeder, A., 1997, Human understanding limits in visualisation. In: Spatial Computing: Issues in Vision, Multimedia and Visualisation Technologies (Eds. Caelli, T., Lam, P. and Bunke, H.), (Singapore: World Scientific), pp Pickett, R. M., Grinstein, G., Levkowitz, H. and Smith, S. (1995). Harnessing preattentive perceptual processes in visualisation. In: Perceptual Issues in Visualisation, Grinstein, G. and Levkowitz, H. (Eds.), Springer-Verlag, Berlin, Germany, pp Rheingans, P. and Landreth, C. (1995). Perceptual Principles for Effective Visualisations. In: Perceptual Issues in Visualisation, Grinstein, G. and Levkowitz, H. (Eds.), Springer-Verlag, Berlin, Germany, pp Treisman, A. (1986a). Properties, parts and objects. In: Handbook of Perception, Volume II, pp Treisman, A. (1986b). Features and objects in early vision. Scientific American, Nov. 1986, pp. 114B-125 Treisman, A. and Gormican, S. (1988). Feature analysis in early vision: evidence from search asymmetries. Psychological Review, Vol. 95, pp Tufte, E. R Envisioning Information. Cheshire, Connecticut: Graphics Press. Tukey, J. W Exploratory data analysis. Reading, Mass.: Addison-Wesley. Geovisualization Andrienko, G. L., and N.V. Andrienko. 1999b. Interactive maps for visual data exploration. International Journal of Geographic Information Science 13(4):

6 Brewer, C. A., 1994, Colour use guidelines for mapping and visualisation. In: Visualisation in Modern Cartography (Eds. MacEachren, A. M. and Fraser Taylor, D. R.), (Oxford, UK: Pergammon), pp Dorling, D Cartograms for human geography. In: Hearnshaw, H. M., and D. J. Unwin (eds), Visualization in geographical information systems. Chichester, England: Wiley. pp Ehlschlaeger, C. R., Shortridge, A. M. and Goodchild, M. F., 1997, Visualising spatial data uncertainty using animation. Computers and Geosciences, 24 (4), Fotheringham, A. S., and M. Charlton GIS and exploratory data analysis: An overview of some basic research issues. Geographical Systems 1(4): Gahegan, M. N., 1998, Scatterplots and scenes: visualisation techniques for exploratory spatial analysis. Computers, Environment and Urban Systems, Vol. 21, No. 1, pp Gahegan, M Visual exploration in geography: Analysis with light. To appear in: Miller, H. J., and J. Han (eds), Geographic knowledge discovery and spatial data mining. London, England: Taylor & Francis. Gahegan, M. N. and O Brien, D. L. (1997). A strategy and architecture for the visualisation of complex geographical datasets. International Journal of Pattern Recognition and Artificial Intelligence, Vol. 11, No. 2, pp Kraak, M.-J., and A. M. MacEachren (eds) International Journal of Geographic Information Science (special issue on exploratory cartographic visualization) 13(4). MacDougall, E. B Exploratory analysis, dynamic statistical visualization and geographic information systems. Cartography and Geographical Information Systems 19(4): MacEachren, A. M How maps work. New York, New York: Guilford Press. MacEachren, A. M., and M.-J. Kraak (eds) Exploratory cartographic visualization. Computers & Geosciences (special issue on exploratory cartographic visualization) 23(4): Peterson, M. P Active legends for interactive cartographic animation. International Journal of Geographic Information Science 13(4): Rhyne, T.-M Going virtual with geographic information and scientific visualization. Computers & Geosciences (special issue on exploratory cartographic visualization) 23(4): Robertson P.K Visualizing spatial data: the problem of paradigms. International Journal of Pattern recognition and Artificial Intelligence, 11(2): Wachowicz, M.. In Press. GeoInsight: An approach for developing a knowledge construction process based on the integration of GVis and KDD methods. To appear in: Miller, H. J., and J. Han, J. (eds), Geographic knowledge discovery and spatial data mining. London, U.K.: Taylor & Francis. Exploratory Visual Analysis / Visual Data Mining Asimov, D The grand tour: A tool for viewing multidimensional data. SIAM Journal of Science and Statistical Computing 6:

7 Bryson, S., D. Kenwright, M. Cox, D. Ellsworth, and R. Haimes Visually exploring gigabyte data sets in real time. Communications of the ACM 42(8): Buja, A., D. Cook, and D. Swayne Interactive high-dimensional data visualization. Journal of Computational and Graphical Statistics 5(1): Cleveland, W. S., and M.E. McGill Dynamic graphics for statistics. Belmont, California: Wadsworth & Brookes/Cole. Cook, D., A. Buja, J. Cabrera, and C. Hurley Grand tour and projection pursuit. Computational and Graphical Statistics 4(3): Gahegan, M., M. Takatsuka, M. Wheeler, and F. Hardisty GeoVISTA Studio: A geocomputational workbench. Proc., 4 th Annual Conference on GeoComputation, UK, August [ Haslett, J., R. Bradley, P. Craig, A. Unwin, and G. Wills Dynamic graphics for exploring spatial data with application to locating global and local anomalies. The American Statistician 45(3): Hinneburg, A., D. Keim, and M. Wawryniuk HD-Eye: Visual mining of high dimensional data. IEEE Computer Graphics and Applications (September/October 1999), pp Inselberg, A The plane with parallel coordinates. The Visual Computer 1: Inselberg, A Multidimensional detective. In: Proc., IEEE conference on Visualization (Visualization 97), Phoenix, Arizona. Los Alamitos, California: IEEE Computer Society. pp Keim, D., and H.-P. Kriegel Visualization techniques for mining large databases: A comparison. IEEE Transactions on Knowledge and Data Engineering (Special Issue on Data Mining). MacEachren, A. M., M. Wachowicz, R. Edsall, D. Haug, and R. Masters Constructing knowledge from multivariate spatio-temporal data: Integrating geographical visualization with knowledge discovery in database methods. International Journal of Geographic Information Science 13(4): Pickett, R. M., and G. C. Grinstein Iconographic displays for visualizing multidimensional data. In: Proc. IEEE Conference on Systems, Man and Cybernetics. Piscataway, New Jersey: IEEE Press. pp Robertson, G. G Cone trees: Animated 3D visualization of hierarchical information. In: Proc. ACM CHI 91, New Orleans, LA: ACM Press, pp Wong, P. C Visual data mining. IEEE Computer Graphics and Applications 19(5): 20-1.