Maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data are an important component of Earth science. These maps have a single value derived at every grid cell and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each grid cell. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. We identify these "distribution data sets" and present a case study to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Data for this case study consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present two general approaches for analyzing and visualizing such data sets. The first is a pixelwise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets. See www.cse.ucsc.edu/research/avis/nasa is for additional information.
The first phase of the conceptual modeling process is the acquisition of knowledge about the real world system. One issue in this phase is the need for clear communication, between the modeler, and experts on the system being examined. These domain experts may not be versed in modeling techniques or languages. Another issue is the potential benefit offered by the recording of the gathered knowledge, in a way that facilitates its reuse outside of the modeling project itself. Existing approaches to the construction of a system description have different strengths and weaknesses. Therefore a combination of different model types, in an integrated manner, could be most effective. Visual wiki software is proposed to facilitate this. Wikis are proven as a platform for incrementally growing shared knowledge bases. They are generally text-based; a wiki allowing editing of graphics as well as text would be preferable for system and process knowledge.
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