Abstract: One way to increase the power of Qualitative Spatial Reasoning is to introduce proximity operators (such as close and far) that are surrogates for distance measures. These operators appear to be semi-quantitative in nature as opposed to purely qualitative. In the light of observations drawn from psychometric testing of perceived proximity, this paper discusses how a model to support proximal reasoning could be constructed. The relationships between the model and the raw data are described. Fuzzy set membership is used to reason about the degree of closeness. The formulation of queries involving proximity is presented, with the meaning of linguistic variables being instantiated within a given context at execution time.

 Subjective distance;  

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Abstract: In information systems that support knowledge-discovery applications such as scientific exploration, reliance on highly structured ontologies as data-organization aids can be limiting. With current computational aids to science work, the human knowledge that creates meaning out of analyses is often only recorded when work reaches publication-or worse, left unrecorded altogether-for lack of an ontological model for scientific concepts that can capture knowledge as it is created and used. We argue for an approach to representing scientific concepts that reflects (1) the situated processes of science work, (2) the social construction of knowledge, and (3) the emergence and evolution of understanding over time. In this model, knowledge is the result of collaboration, negotiation, and manipulation by teams of researchers. Capturing the situations in which knowledge is created and used helps these collaborators discover areas of agreement and discord, while allowing individual inquirers to maintain different perspectives on the same information. The capture of provenance information allows historical trails of reasoning to be reconstructed, allowing end users to evaluate the utility and trustworthiness of knowledge representations. We present a proof-of-concept system, called Codex, based on this situated knowledge model. Codex supports visualization of knowledge structures through concept mapping, and enables inference across those structures. The proof-of-concept is deployed in the domain of geoscience to support distributed teams of learners and researchers.

 ontology;  situated knowledge;  Codex;  

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 ConceptVista;  Perspectives;  

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Abstract: Individual user needs are subjective, perspective, and context-dependent at the same time. In this paper, I will pre-sent an adaptive multi-perspective user interface which learns the individual preferences when working with documents, i.e. the way a user categorizes documents and clusters them within directories. The system is built on dynamic conceptualizations of documents and folders based on term correlations providing the basis for an integrated and associative management of office documents, emails and bookmarks. Users get assistance in classification and retrieval by offering a wide range of techniques which may be combined in any way. The system records successful query processes for reuse and further allows users to publish respective expert profiles learned by the system.,

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Abstract: We present a rationale and method for representing the vagueness in taxonomic class definitions in cases where classes are described by a set of characteristics, such as those sometimes used as the basis for land-cover category discrimination. We further describe methods to estimate the semantic similarity between any two classes by calculating semantic similitude metrics based on such parameterized class definitions. Our working hypothesis is that a large similitude would predict categories that will be more prone to confusion and hence image or map misclassification. We use two different existing data sets to demonstrate and evaluate the method, and the results support our original hypothesis. Consequently, we argue that classification schemes that are based on parameterized definitions could be assessed for problematic categories during their construction using our approach, and thus, enabling the identification of a thematic vagueness component to supplement the more traditional statistical measures derived from the error matrix.

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Abstract: This paper describes a programmatic framework for representing, manipulating and reasoning with geographic semantics. The framework enables automating tool selection for user defined geographic problem solving, and evaluating semantic change in knowledge discovery environments. Methods, data, and human experts (our resources) uses, inputs, outputs, and semantic changes are described using ontologies. These ontological descriptions are manipulated by an expert system to select resources to solve a user-defined problem. A semantic description of the problem is compared to the services that each entity can provide to construct a graph of potential solutions. An optimal (least cost) solution is extracted from these solutions, and displayed in real-time. The semantic change(s) resulting from the interaction of resources within the optimal solution are determined via expressions of transformation semantics represented within the Java Expert System Shell. This description represents the formation history of each new information product (e.g. a map or overlay) and can be stored, indexed and searched as required. Examples are presented to show (1) the construction and visualization of information products, (2) the reasoning capabilities of the system to find alternative ways to produce information products from a set of data methods and expertise, given certain constraints and (3) the representation of the ensuing semantic changes by which an information product is synthesized.

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Abstract: The Geographic Visualization Science, Technology, and Applications (GeoVISTA) Center at Penn State University was established in fall, 1998. The Center mission is to conduct fundamental and applied research on the representation and analysis of georeferenced information, develop geographic visualization (geovisualization technologies, and apply both in science, industry, decision-making, and education. GeoVISTA builds on a long tradition of geovisualization research in the Department of Geography and a strategic planning initiative that added four new staff (two tenure-track faculty and two research scientists). The GeoVISTA team now includes faculty from the School of Information Science and Technology, the Department of Landscape Architecture, and the Center for Academic Computing as well as from Geography. We direct particular attention to research and applications that link geovisualization with other components of geographic information science (e.g., geocomputation, semantics for geospatial knowledge representation, and database representation), thus to the integration of many perspectives on geospatial information and its analysis. Here, we can highlight only a sampling of our current geovisualization projects. We focus on three multi-investigator efforts. A more complete picture of our research activities (including many recent publications) can be obtained from the Center web site at: www.geovista.psu.edu.

 geovisualization, geocomputation, geoVE, maps, KDD;  

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 geovisualization, geocomputation, maps, Java, interaction;  

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 Knowledge Representation;  agents;  

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 Visualization;  

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Abstract: This paper explores the development of categories shared in the field logging of a region by a team of geologists. Visualization, neural networks and spatial statistical tools are employed to gain insight into the complex space of attributes observed, and into the categories developed. Background material and a discussion of results examines the findings in the light of research into category development, and specifically how categories are thought to be formed and modified as part of the (geo)scientific process and the situations encountered. Results show that (1) category discrepancy exists between individuals; (2) category development or revision is evident among individuals; and (3) that some categories do not seem to be totally defined by observed data alone. The results imply that contextual factors should also be considered when adopting ontological approaches to information representation.

 classification;  categorization;  category development;  situated learning;  information ontology;  self-organizing maps;  geological fieldwork;  

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 spatial data models;  data ingest;  

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 query languages;  object-oriented gis;  

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 Data mining;  

Abstract: Although many different types of data mining tools have been developed for geographic analysis, the broader perspective of geographic knowledge discovery?the stages required and their computational support?have been largely overlooked. This paper describes the process of knowledge construction as a number of inter-related activities and the support of these activities in an integrated visual and computational environment, GeoVISTA Studio. Results are presented showing examples of each stage in the knowledge construction process and a summary of the inter-relationships between visualization, computation, representation and reasoning is provided.

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 quadtree GIS;  

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 AI, pattern recognition, visualization, maps, geospatial;  

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 geocomputation, semiotics, visualization, components;  

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 quadtree GIS;  england;  

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 geovisualization, ESDA, coordination, interaction, information visualization;  

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 Data mining;  Visualization;  

Abstract: Abstract In this paper we present an approach to conceiving of, constructing and comparing the concepts developed and used by geographers, environmental scientists and other earth science researchers to help describe, analyze and ultimately understand their subject of study. Our approach is informed by the situations under which concepts are conceived and applied, captures details of their construction, use and evolution and supports their ultimate sharing along with the means for deep exploration of conceptual similarities and differences that may arise among a distributed network of researchers. The intent here is to support different perspectives onto GIS resources that researchers may legitimately take, and to capture and compute with aspects of epistemology, to complement the ontologies that are currently receiving much attention in the GIScience community.

 situated knowledge;  

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Abstract: The research reported here contrasts the roles, methodologies and capabilities of statistical methods with those of inductive machine learning methods, as they are used inferentially in geographical analysis. To this end, various established problems with statistical inference applied in geographical settings are reviewed, based on Gould's (1970) critique. Possible solutions to the problems outlined by Gould are suggested via reviews of: ( i ) improved statistical methods, and ( ii ) recent inductive machine learning techniques. Following this, some newer problems with inference are described, emerging from the increased complexity of geographical datasets and from the analysis tasks to which we put them. Again, some solutions are suggested by pointing to newer methods. By way of results, questions are posed, and answered, relating to the changes brought about by adopting inductive machine learning methods for geographical analysis. Specifically, these questions relate to analysis capabilities, methodologies, the role of the geographer and consequences for teaching and learning. Conclusions argue that there is now a strong need, motivated from many perspectives, to give geographical data a stronger voice, thus favouring techniques that minimize the prior assumptions made of a dataset.

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 visualization, maps, EDA;  

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 knowledge discovery;  information visualization, Multiple linked views, coupling, linking, coordination, exploratory visualization.;  

Abstract: Although many different types of data mining tools have been developed for geographic analysis, the broader perspective of geographic knowledge discoverythe stages required and their computational supporthave been largely overlooked. This paper describes the process of knowledge construction as a number of inter-related activities and the support of these activities in an integrated visual and computational environment, GeoVISTA Studio. Results are presented showing examples of each stage in the knowledge construction process and a summary of the inter-relationships between visualization, computation, representation and reasoning is provided.

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Abstract: One barrier to the uptake of geocomputation is that, unlike GIS, it has no system or toolbox that provides easy access to useful functionality. This paper describes an experimental environment, GeoVISTA Studio, that attempts to address this shortcoming. Studio is a Java-based, visual programming environment that allows for the rapid development of complex data exploration and knowledge construction applications to support geographic analysis. It achieves this by leveraging advances in geocomputation, software engineering, visualization and machine learning. At the time of writing, Studio contains full 3D rendering capability and has the following functionality: interactive parallel coordinate plots, scatterplot, visual classifier, 2D map and image viewer, sophisticated colour selection (including Munsell colour-space), spreadsheet, statistics package, and supervised and unsupervised neural networks. Through examples of Studio at work, this paper demonstrates the roles that geocomputation and visualization can play throughout the scientific cycle of knowledge creation, emphasising their supportive and mutually beneficial relationship. A brief overview of different types of inference used in such knowledge creation activities is given, and related to the exploratory analysis tools described. By way of results, a detailed account of the use of these tools is presented, and various findings and insights generated are pointed out. The domain of application is the process of uncovering useful categories by which a taxonomy of landuse/landcover can be created. The proposed categories are then evaluated using a combination of neural and visual methods, to ensure their viability.

 knowledge discovery;  Visualization;  classification;  exploratory data analysis;  Geocomputation;  Inference;  

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 Data mining;