Analytical Methods (Integrated
methods for knowledge construction)
Related Projects:
Representing, analyzing, modeling and
extracting meaning from the complex heterogeneous geospatial
datasets being assembled requires new approaches that can scale
up to current and future data complexity and data volume. Our
work addresses a wide variety of issues, including:
- the development of 'complex' spatiotemporal systems with
emergent properties,
- new techniques for data mining, knowledge discovery, visualization
(for application to geospatial and spatiotemporal information
about the past, present, and future),
- advanced and semantically aware spatial databases that
can represent and integrate both the data and the various
higher level knowledge constructs, such as categories and
relationships that emerge from the data during knowledge
construction and
- developing a geographical agent modeling environment for
investigating human activities.
These activities, when integrated, support the entire geo-scientific
process, from initial exploration of data, hypothesis generation,
concept discovery, model formulation, analysis and validation,
and, when fused together seamlessly in GeoVISTA Studio, will
form a complete Problem Solving Environment (PSE) for teams
of scientists to use, thus supporting our geocollaboration focus.
By bringing these activities together in GeoVISTA Studio we
avoid many of the integration problems that plague traditional
computational analysis. To accomplish this goal, Center affiliates
and their collaborators are working to integrate methods and
tools that span many disciplines including machine learning,
pattern recognition, agent and cellular modeling, data mining,
multivariate information visualization and spatial statistics.
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