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GeoVisual Analytics (Integrated
methods for knowledge construction):
Related Projects: HERO,
DAVE_G,
NIMA
visualization project, NSF
Knowledge Management
GEON: The Geo-Sciences Network,
Ontologies in Action,
Air Pollution and Cardiac Vulnerability to Acute Events,
GIS/Atlas Cancer Atlas Research, ARDA,
The strengths and weaknesses of
Exploratory Spatial Data Analysis (ESDA) and spatial statistical methods for Prostate
Cancer
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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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