. A Process Support System provides the tools and mechanisms necessary to define, implement and control processes, i.e., complex sequences of program invocations and data exchanges. Due to the generality of the notion of process and the high demand for the functionality they provide, process support systems are starting to be used in a variety of application areas, from business re-engineering to experiment management. In particular, recent results have shown the advantages of using such systems in scientific applications and the work reported in this paper is to be interpreted as one more step in that direction. The paper describes Geo-Opera, a process support system tailored to spatial modeling and GIS engineering. Geo-Opera facilitates the task of coordinating and managing the development and execution of large, computer-based geographic models. It provides a flexible environment for experiment management, incorporating many characteristics of workflow management systems as well as ...

The characteristics of geographic data and the nature of geographic research require the participation of many agents. Data is generated by multiple sources (satellites, ground observation, weather stations, photography, etc.), accessed, processed and transformed by many users and available for use to an even larger population of users. Lack of coordination among all these different agents may render large amounts of work useless. Most existing GIS (Geographic Information Systems) do not provide any support for cooperative work, which adds to the problem. To overcome this serious limitation while still allowing users to take advantage of GIS technology, we propose GOOSE, a system implemented as a top layer for existing GIS. GOOSE provides the tools for constructing large geographic models in a cooperative environment with potentially many users and participants. Keywords: Cooperation, Modeling, Geographic Information Systems, Geographic Research, Databases, Metadata, Object-Oriented S...

Information Visualization is concerned with the art and technology of designing and implementing highly interactive, computer supported tools for knowledge discovery in large non-spatial databases. Information Visualization displays, also known as information spaces or graphic spatializations, differ from ordinary data visualization and geovisualization in that they may be explored as if they represented spatial information. Information spaces are very often based on spatial metaphors such as location, distance, region, scale, etc., thus potentially affording spatial analysis techniques and geovisualization approaches for data exploration and knowledge discovery. Two major concerns in spatialization can be identified from a GIScience/ geovisualization perspective: the use of space as a data generalization strategy, and the use of spatial representations or maps to depict these data abstractions. A range of theoretical and technical research questions needs to be addressed to assure the construction of cognitively adequate spatializations. In the first part of this chapter we propose a framework for the construction of cognitively plausible semantic information spaces. This theoretical scaffold is based on geographic information theory and includes principles of ontological modeling such as semantic generalization (spatial primitives), geometric generalization (visual variables), association (source–target domain mapping through spatial metaphors), and aggregation (hierarchical organization). In the remainder of the chapter we discuss ways in which the framework may be applied towards the design of cognitively adequate spatializations.

A number of visualization techniques have been put forward that implement a map metaphor to display abstract, non-georeferenced information. This paper refers to these as map-like information visualizations that are distinguished from other information visualization approaches in a number of ways. It interprets some of the principles underlying these techniques within a framework informed by geographic information science (GIScience). Recent geographic efforts in this research area have linked ideas about the nature of geographic information to cognitive schemata proposed by cognitive linguists. This paper draws on the arguments that have emerged from those efforts regarding the nature and usefulness of geographic metaphors. It proposes to discuss particular projection techniques, like multidimensional scaling or self-organizing maps, with reference to the geometric primitives they employ. These primitives will drive the choice of geometric and symbolic transformations that are necessary to achieve a particular visualization. Designers of map-like visualizations are thus challenged to seriously consider the implications of particular computational techniques and the consequences of symbolization choices.

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Data quality descriptions are crucial, but methods to produce and use them have not significantly improved during the past 10 years. Current quality descriptions are from the perspective of the producer of the data, not the user. Actual quality descriptions are mostly verbal and not suitable for rapid comparison with a required standard to make a decision about `fitness for use' of a certain dataset for a task. This limits business with geographic data over the net. The paper introduces the concept of a metamodel as a framework to compare data quality from a producer and a user perspective in a single model. It is based on category theory and morphisms, which link the model of reality with the model of the GIS data, and their collection and use. The achieved quality of a decision based on using the data can be derived. It is shown that data quality descriptions are dependent on the intended use of the data. A `use independent', generic data quality description is not possible. Fortunat...

A number of user interface techniques have been put forward that implement a map metaphor to visualize abstract information. In this paper, some of the principles underlying these approaches are interpreted within a framework informed by geographic information science (GIScience). Recent advances in this research area have linked ideas about the nature of geographic information to cognitive schemata proposed by cognitive linguists. This paper draws on the arguments that have emerged from those efforts regarding the nature and usefulness of geographic metaphors. Particular projection techniques, like multidimensional scaling or self-organizing maps, are discussed with a focus on the geometric primitives that they employ. The creation of map-like interfaces is interpreted as a series of transformations leading to alternative visualizations whose relative merits remains to be investigated in future work. It is argued that information about the various transformations needs to be communicated to users if they are to effectively use map-like interfaces.

The state-of-the-practice in parallel processing of geographic information binds the design of domain decomposition and task scheduling to specific conventional parallel computer architectures. This tightcoupling approach is problematic to software design for three reasons: 1.) Logic: Domain decomposition and task scheduling methods focus on the characteristics of spatial data and operations performed on them. This focus is logically inappropriate. Variability in computational intensity should be used instead. 2.) Generality: Any change to spatial data or operations requires a corresponding change in the design of domain decomposition and task scheduling methods. Consequently, generic parallel processing solutions are difficult to develop. 3.) Compatibility: Domain decomposition and task scheduling strategies depend upon specific parallel architectures. A change in architecture requires a corresponding change to the solution process even if the analysis method were held constant. These three problems are analogous to those observed in graphics programming before the advent of device-independent software. In this case, these problems hinder the development of portable Grid 1-based parallel geographic analysis methods. To eliminate these problems, we introduce a new spatial

This chapter introduces the use of high-resolution self-organizing maps (SOM) to represent a large number of geographic features on the basis of their attributes. Until now, the SOM method has been applied to geographic data for both clustering and visualization purposes. However, the granularity of the resulting attribute space representations has been far below the resolution at which geographic space is typically represented. We propose to construct SOMs consisting of several hundred thousand neurons, trained with attributes of an equally large number of geographic features, and finally visualized in standard GIS software. This is demonstrated for a data set consisting of climate attributes attached to 200,000+ U.S. census block groups. Further, overlays of point, line, and area features onto such a high-resolution SOM are shown.