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Next:Introduction Up: Model-Based Mining of
Model-Based Mining of Remotely Sensed Data for Environmental and Public Health ApplicationsAbstract:
There is a growing interest in automatically identifying environmental factors that contribute to public health risks, such as epidemic disease and air pollution. Better understanding of these factors will enable
more precise prediction of the location and time of high risk events. The recent rapid advances in information and knowledge discovery technologies (e.g., object-relational database, data mining
techniques, and Internet), as well as the availability of various remotely sensed data, have made it feasible to derive better models to predict epidemic disease. In this chapter, we describe the basic
framework for combining existing environmental model heuristics with models derived from observed data through data mining techniques. Keywords:
environmental disease modeling, image databases, texture features, environmental epidimiology, satellite imagery, composite objects.
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