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Research Interests: My areas of research interest are geographic information systems (GIS),
geographic knowledge discovery, and geo-visualization. These three components
of my research are integrated within the
context of environmental modeling and public health applications. Specifically, geographic information systems enable the collection, geo-coding, storage, preprocessing, and static display of environmental data and the construction of spatial databases. Geographic knowledge discovery is the extraction of spatial relations or patterns not explicitly stored in the spatial databases. This is achieved through intelligent data mining algorithms. These algorithms find relationships between variables in the environmental system, extract patterns, and make predictions of environmental dynamics. Geo-visualization involves the use of visual representations to make spatial contexts and problems visible, often with an interactive and animated visualization environment. It is an particularly effective tool to explore geographic data and communicate geographic information to private or public audiences. In this means, it plays important roles in both geographic knowledge discovery and presenting the patterns or relationships extracted through geographic knowledge discovery. Theorectical Research: My theoretical research on geographic knowledge discovery focuses on the following aspects: 1) examining
geographic knowledge discovery and Practical Applications: My current research projects cover the following areas: 1) Spatial data mining and fuzzy modeling for natural resource mapping.
2) Exploratory visualization and knowledge discovery of individual space-time behavioral patterns related to influenza trasmission A recent grant from the NIH will support my exploratory
Interactive and animated exploratory visualization methods are investigated for effective communication of complex environmental dynamics to the general public.
An integrated wireless-wired sensor network is being implemented in the vicinities of Kean Univ. to monitor and model the human-environment interactions and environmental dynamics in an urban community. Integrated data mining and visualization approach is taken to reveal patterns and generate predictions for decision making. 5) Habitat mapping in the tropical forests in Costa Rica.. A GIS database is being compiled to map natural and disturbed habitats of tropical plant species in Costa Rica to study the human impacts on natural ecosystems. Funded Projects:
2011 A novel visualization of the globalized world: implications for infectious disease transmission, Kean University through the Presidential
Scholars Challenge Award, PI, $39,268.
2010-2012 Influenza transmission: exploratory visualization and knowledge discovery of individual space-time behavioral patterns, National Insitute of Health (NIH), PI. $151,268. 2010 Community Environmental Networks for Risk
Identification and Management, Kean University through the Presidential
Scholars Challenge Award, Co-PI, $49,746.
2009-2010 Fog Occurrence using GIS: Predicting Areal & Point Estimation by
Regression. Kean University New Research Initiatives Award, Co-PI,
$15,000.
2009-2010 Modeling and Visualizing Uncertainty in
Geographic Knowledge Discovery. Funded by
2009 UrbanNet:
Urban Environment Monitoring and Modeling with a Wireless Sensor Network. Funded by
2008-2009 Analysis and visualization of
spatiotemporal changes of urban landscape pattern in
2008-2009 Integrating Geo-visualization with Spatial
Data Mining: a Case Study on Spatial Temporal Modeling of
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Last updated by
Feng Qi, 2/02/2011 |
classification from the perspective of
human cognition, 2) developing schemes for organizing knowledge used in
categorizing and mapping spatial entities with indeterminate boundaries, such
as natural resource classes, wildlife habitats, natural hazards, among
others, 3) integrating geo-visualization with geographic knowledge discovery
for extracting spatio-temporal patterns, and 4) investigating data mining
algorithms for extracting such knowledge from spatiotemporal databases.
I have
been continuing
my research efforts in spatial data mining for soil mapping, now focusing on using
the mined knowledge to update existing inventory maps, uncertainties involved
in natural resource classification using mined knowledge, and fuzziness vs.
probability in the modeling of such uncertainties.
study on using tracking technologies to capture data on invididual space-time paths and using advanced visualization and knowledge discovery methods to generate a hypothesis of individual spatiotemporal behaviors that are more susceptible to flu infection during a widespread outbreak and provide recommendations for flu prevention at the micro scale.
3) Spatio-temporal visualization of high way air quality for public
communication. 
