Abstract
In this research, we explore the integration of computational and visual
approaches, to contribute to the analysis of complex geospatial data.
Computational analysis based on the SOM is used in a framework for data
mining, knowledge discovery and spatial analysis, for uncovering the
structure, patterns, relationships and trends in the data. The framework
is informed
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by current understanding of the effective application of
visual variables for cartographic and information design, by developing
theories on interface metaphors for geospatial information displays, and
by previous empirical studies of map and information visualization
effectiveness. It is used to facilitate the knowledge construction
process by supporting user's exploratory tasks in a number of ways,
including a scenario for better use of the representational spaces. The
ultimate goal is to support visual data mining and exploration, and gain
insights into underlying distributions, patterns and trends, and thus
contribute to enhancing the understanding of geographic processes and
support knowledge construction.
The framework guided the initial design decisions of a prototype
exploratory geovisualization environment. The visualization environment
incorporates several graphical representations of SOM output. These
include a distance matrix representation, 2D and 3D projections, 2D and
3D surfaces, and component plane visualization with which correlations
and relationships can be easily explored. Multiple views are used to
simultaneously present interactions between several variables over the
space of the SOM, maps, and other graphics such as parallel coordinate
plots. Some applications of the method are explored with different
datasets.
A usability evaluation methodology based on a taxonomy of exploratory
tasks and visualization operations is developed to assess the
effectiveness of the proposed exploratory geovisualization environment.
A subsequent empirical usability testing is conducted and involves
different options of map-based and interactive visualizations of a SOM
output with the exploration of a socio-demographic dataset. The study
emphasizes the visual exploration and knowledge discovery processes.
The usability test results and answers to the research questions provide
some guidelines for geovisualization design that integrate different
representations such as maps, parallel coordinate plots and other
information visualization techniques. The research shows that visual
exploration can be enhanced by combining the attribute space and the
geographic space visualizations. To be effective, this integration of
visual tools needs to be done appropriately since these tools are found
to support different visual tasks. For visual grouping and clustering,
visual analysis and comparison of the patterns in the data, and for
revealing relationships, the SOM was found more effective than the map.
The usability test results suggest that the integration of map and other
representations techniques such as parallel coordinate plot and the
SOM-based visualization of the attributes space should reflect the
potential of each visual tool. The attribute space visualization is
effective as a visual data mining tool allowing the user to select,
filter, and output results. The results of this process can be viewed in
maps, since the map was generally a better representation for tasks that
involve visual attention and sequencing (locate, distinguish, rank).
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