Spatiotemporal Mapping of Environmental Health Processes — The BME Approach
For a very large number of environmental health problems, the required outcome of the analysis is one or more spatiotemporal maps. These maps may convey visual information regarding the distribution of variables in space-time (e.g., spring water solute contents; breast cancer incidence), usually obtained from data sets. Maps may also incorporate other broad-based knowledge (general and case-specific knowledge, analytic hypotheses, models of physical laws, boundary and initial conditions, etc.; §I.5). While the first viewpoint is more descriptive, the second one is more explanatory. The Bayesian maximum entropy (BME) approach discussed in this Chapter favors a perspective that combines both: a spatiotemporal map representing the evolution of an environmental variable in space-time should be the outcome of an analysis that incorporates the set of observations available in space-time as well as other useful knowledge bases.
KeywordsMinimum Mean Square Error Hard Data Linear Minimum Mean Square Error Soft Data Bayesian Maximum Entropy
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