VISUALIZATION STRATEGIES AND TECHNIQUES FOR HIGH-DIMENSIONAL SPATIO- TEMPORAL DATA

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1 VISUALIZATION STRATEGIES AND TECHNIQUES FOR HIGH-DIMENSIONAL SPATIO- TEMPORAL DATA Summary B. Schmidt, U. Streit and Chr. Uhlenküken University of Münster Institute of Geoinformatics Robert-Koch-Str Münster, Germany In order to explore high-dimensional data spaces and to gain insight into the underlying geoprocesses suitable tools are needed. Especially interactive, computer-generated representations enrich our perception, so that complex phenomena can be comprehended intuitively. Although several helpful visualization techniques are available today, there is a growing demand for more advanced tools and strategies. Latter must ensure that techniques appropriate to the nature of data and suitable for the objectives are applied throughout the whole data exploration process. This paper outlines a software environment, that supports those strategic considerations, taking today's geo-information processing technology into account. 1. Introduction Increasing availability of geoscientific data and not at least the evolution of hardware and software technology are the main reasons for the current research and development activities in the field of spatial data visualization. In particular, the use of simulation models often leads to vast data sets, usually characterized by quite high dimensionalities. In order to extract the decisive structures and to recognize the underlying relations and phenomena the human visual sense is the most suitable. The scientific discipline of Visualization in Scientific Computing (ViSC) offers a collection of methods, which can be used for this purpose. Cooperating with three other Westfalian university departments, the Institute of Geoinformatics, Münster, works in a research project named "Bits, Bilder, Bedeutung". The aim is the development of visualization strategies for structuring, mapping and exploring high-dimensional dynamical and spatial data, e.g. generated by the mesoscale atmospheric model S_KIMO (Institute of Geoinformatics) (BERNARD AND STREIT 1996). The strategies will be transferred into specific techniques and linked to already existing software modules. Simultaneously, the generated tools will be coupled with a geographical information system (GIS). 1

2 2. Spatial data visualization Practically, generation, analysis and visualization of spatial data is increasingly a process of handling high-dimensional data. Nowadays growing attention is paid to the third spatial dimension (z) and to time as a fourth dimension. Atmospheric phenomena with its dynamical behavior are an excellent example. Our main data sources are measured data and simulation results. Since "raw" data material is of no value until it is processed into a useable form, different transformation operations will be performed. Especially filter operators influence our view on the data. Different selection criteria such as spatial or temporal queries, thematic selections, deletion of outliers, smoothing operators, level-of-detail filters etc. are the most common techniques to extract the "relevant" information. In this way, relationships between the visualized themes, space and time can be identified more easily. After the "irrelevant" information is filtered out sequentially (this is quite a subjective process), a choice of the several existing visualization techniques is required. Corresponding to the nature of the processed data and the addressee one has to select an appropriate visualization method. This is important to highlight the relevant elements in order to make the underlying processes transparent. It also could focus on building a visual analysis environment for a more detailed visual data exploration. measured data "mapping" image (view) derivation "public" presentation data filter visual analysis simulation data filter modification information condensation "private" knowledge Fig. 1: Data exploration process Fig. 1 illustrates the process of data exploration considering the above mentioned filter and mapping methods. All in all it is an interactive and iterative process, in which the human with his visual sense and scientific background is directly included. The main purposes should be the extraction of new scientific knowledge by an expert in visual exploration and to support the explanation of geoprocesses for "public" presentation. These new discoveries 2

3 provide a feedback in the visualizing process to generate presentations for public purposes. These are, up to now, mostly non-interactive images, maps and videos. Concerning the transfer of knowledge from scientific research to the decision makers and planners realistic and interactive visualization tools will act as a bridge in the near future ( HEARNSHAW AND UNWIN 1994). A further benefit of working in such a visualization cycle is the possibility of model validation by comparing simulation data and parallely taken measured data visually. The improvement of simulation models enhances data quality and can prevent from misinterpretations during the exploration process. At the Institute of Geoinformatics special attention is focused on visualizing data sets produced by the boundary layer model S_KIMO. The simulation results consist of six different dimensions (we define a space s dimensionality as the sum of all independent variables dimensions). We ve got three spatial dimensions x, y, and z, time t, a thematic dimension (which corresponds to physical variables such as temperature, air pressure, wind vectors etc.), and at least we can distinguish different scenarios (e.g. alternative plannings). 3. ViSC-techniques at a glance Some requirements of great importance concerning software tools for spatial data visualization are listed below. Different degrees of interactivity: This may vary from completely interactive data exploration to self-running videos, e.g. MPEGs, for pure presentation purposes. Level of abstraction: It may change from abstract symbolic diagrams (glyphs) to photorealistic representations (as the other extreme). Mostly the chosen level depends on the addressee (lay vs. professional). Supported visualizaton techniques: There are quite a lot of visualization techniques which should be included into a suitable software solution. For our purposes mainly the most common rendering methods, texture mapping facilities, generation of isolines and isosurfaces, slicing functions, display of vector fields, calculation of streamlines and wind trajectories, color editing and reclassification functions, glyph representations, semi-transparent volume rendering and animation have to be mentioned. The screen shots in fig. 2 and 3 are taken from the software package AVS (ADVANCED VISUAL SYSTEMS 1992). They illustrate how different visualization techniques may help to gain insight into the underlying geo processes. Navigation tools: Orientation and navigation ("where am I?" resp. "where to go?") in time, space and thematic dimensions can be difficult. Hence a comprehensive visualization environment should give hardware and software assistance to the user throughout the data exploration process (e.g. by using modern input and output devices such as data gloves and shutter glasses, or 3

4 appropriate graphical input devices such as sliders, diallers, birdeye windows to keep the overall view, trackballs etc.). Dynamically linked views: Windows (viewports) have to be linked dynamically, i.e. performing an action inside a window should update the other windows contents. Feature space representation: It can be helpful to show the graph of the data values of one theme against the values of another theme (scatterplot; e.g. temperature against elevation). Providing meta data: Meta data management systems could be very valuable tools throughout the data exploration process (e.g. syntactical and semantical information, data quality, "private" knowledge, model assumptions). Transparent algorithms: The numerical methods to compute isolines, trajectories etc. should be transparent. Further on there should be the possibility to integrate user-defined algorithms and to extract (user-defined) features such as local circulation systems, ventilation zones or areas of cold air production. Editable objects: Object-editability inside a (rendered) scene could be very useful (e.g. deleting outliers, modifying landscape models, creating marker symbols, or managing map components). Cartographic design: Further on there should be paid attention to graphical and cartographic design guidelines, e.g. in Bertin s sense (BERTIN 1974). Fig. 2: Horizontal slicing (air temperature) 4

5 4. Using MAM/VRS Fig. 3: Tracers (wind field) At the Institute of Geoinformatics, we started to design a flexible software tool, which will take our methodical results into account. The user interface will be built on top of the the extensible and portable C++ toolkit MAM, which integrates geometric modeling, animation and interaction (DÖLLNER AND HINRICHS 1995). One of the basic ideas of MAM is to define hierarchially composed objects and time-dependent behaviors by two directed acyclic graphs. Additionally, a set of constraints is applied to both the geometry and the behavior graph. Together with MAM, we will use the virtual rendering system VRS, hence several standard 3D graphics packages (such as OpenGL) are supported (DÖLLNER AND HINRICHS 1995a). 5. Further research Special attention is turned to the possibility of integrating visualization components and GIS technology. E.g. a 4D-GIS browser to view atmospheric processes for planning purposes could be set up (BERNARD AND STREIT 1996). In 5

6 this context we will use the four-dimensional, dynamical geodata model OO- GDM (BECKER ET AL. 1996) with its object-oriented conceptual design. The distribution of measured geodata or model results is an important task. In the near future especially VRML-like (Virtual Reality Modelling Language, see SILICON GRAPHICS 1996) specifications could offer a suitable file format for providing data via the World Wide Web. Further on viewing tools or simple visualization modules, realized under programming environments such as Java or Tcl/Tk, could be made available to Internet users. References Advanced Visual Systems, Inc. (1992): AVS - User s Guide. Release 4, Waltham (MA). Becker, L., A. Voigtmann and K. Hinrichs (1996): Temporal Extensions for an Object-Oriented Geo-Data-Model. Technical report no. 6/96-I, Department of Informatics, University of Münster. Bernard, L. and U. Streit (1996): Integration of Dynamical Models And GIS: Design of a Four-Dimensional Atmospheric GIS. 2nd Intern. Conf. on Municipal Information Systems and Urban Data Management: WELL-GIS Workshop "Environmental Information Systems for Regional and Municipal Planning", Prague. Bertin, J. (1974): Graphische Semiologie. De Gruyter, Berlin. Döllner, J. and K. Hinrichs (1995): Modellierung virtueller Umgebungen durch Spezifikation von Geometrie- und Verhaltensgraphen. In: D. W. Fellner (ed.): Modeling Virtual Worlds - Distributed Graphics, Bad Honnef (Germany), pp Döllner, J. and K. Hinrichs (1995a): The Virtual Rendering System: A Toolkit for Object-Oriented 3D Rendering. In: H. P. Santo (ed.), Proc. of Edu- Graphics/CompuGraphics, Portugal, pp Hearnshaw, H. M. and D. J. Unwin (eds.) (1994): Visualization in Geographical Information Systems. Wiley, Chichester. Silicon Graphics, Inc. (ed.) (1996): The VRML 2.0 Specification. Uhlenküken, Chr. and B. Schmidt (1996): Visualization Strategies for High- Dimensional Dynamical Spatial Data and Processes. 2nd Intern. Conf. on Municipal Information Systems and Urban Data Management: WELL-GIS Workshop "Environmental Information Systems for Regional and Municipal Planning", Prague. More information: 6

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