© ISO 2001 All rights reserved
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8.5.3.2 Scientific visualization
Scientific visualization is the use of computer graphics and image processing to present models or characteristics of processes or objects for supporting human understanding. ISO 2382-13:1996
“Until recently… the rendering of GIS results primarily has been restricted to the same set of display techniques used in manual cartography” Berry, Buckley, and Ulbricht, 1998, p. 47. These techniques, the result of hundreds of years of
cartographic experience and decades of cartographic research, have proven appropriate for conventional map products but have yet to demonstrate their efficacy in simulated environments and in modes of acquisition that include
movement of the user’s point-of-view.
from Haber and McNabb 1990, p. 75: visualization as a process composed of “transformations that convert raw simulation data into a displayable image. The goal of the transformation is to convert information into a format
amenable to understanding by the human perceptual system.”
McCormick et al. 1987, p. 3 further defines the discipline of scientific visualization as a “discipline concerned with developing the tools, techniques and systems for computer-assisted visualization. It studies those mechanisms in
humans and computers which allow them in concert to perceive, use and communicate visual information.”
See also: “An IT Roadmap to Geospatial Future”, NAS, 2003: -
Representing uncertainty -
Category-representation; ontology portrayal -
Portrayal - Urban representations -
Distributed portrayal -
Fusing CAD and GI info [geometry, coordinates]
8.5.3.3 3D visualization
Editors note: need to reference ISO standards 3D visualization and how they apply to geographic information.
8.5.3.4 Color models
Editors note: get CIE reference from Doug OBrien
9 Computational viewpoint – services for imagery
9.1 Task-oriented computation
The computational viewpoint provides a transition from the information viewpoint to the distributed deployment represented in the engineering viewpoint. The computational viewpoint enables distribution through functional
decomposition of the system into objects which interact at interfaces. For geographic imagery the computational viewpoint identifies abstract objects necessary for the process flow for acquiring, storing, processing, viewing imagery.
The key objective of the computational viewpoint is to enable interoperability. Interoperability capability to communicate, execute programs, or transfer data among various functional units in a manner that requires the user to
have little or no knowledge of the unique characteristics of those units. The next clause defines two models for developing interoperable components.
Robust computational models are needed for the reuse of remote-sensing information and services to be used by a wider community. Elements of this model are reusable service interaction patterns, e.g., service chaining, and
methods to aid analyst selection of services, e.g., taxonomy of service types.
© ISO 2001 All rights reserved
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In order that remote-sensing science yield the greatest value to society and to business, it is critical that data analysis becomes accessible to the layperson who may have the data access and the analytical ability but not necessarily the
mathematical background to delve into algorithmic minutiae.
From Knowledge to decisions through integration of the goals of multiple stakeholders. Decisions for Applications: application of knowledge and information to address the goals of multiple stakeholders.
Decision Support System: ``interactive computer programs that utilize analytical methods, such as decision analysis, optimization algorithms, program scheduling routines, and so on, for developing models to help decision makers
formulate alternatives, analyze their impacts, and interpret and select appropriate options for implementation Adelman, L., Evaluating Decision Support and Expert Systems, John Wiley and Sons, New York, 1992.
Sensing Imagery
Information Imagery
Knowledge Base
Information Predictions
Observations
Decision Support
Systems
Management Decisions
Policy Decisions
Data
Figure 19 - Imagery for decision support
Decision Support Systems DSS that operate within a spatial or spatial-temporal context represent special forms of more general decision support systems. Their intent is to permit planners and policy makers to: 1 integrate large
quantities of existing space-time data, 2 use these data as inputs to sophisticated forecasting models for predicting the results of alternative policy choices, and 3 display the model results in easily understood ways to public officials
and private citizens as well as to the scientific community. Basic to the use of the DSS is the ability to examine various what if situations within the operational context of DSS. Some of these systems are purely spatial in nature, but the
what if basis of their operations clearly calls for the incorporation of an explicit temporal component in nearly all cases.
Editors note: Review paper from IGARSS on Decision Support Systems
9.2 Computational patterns
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