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Spatiotemporal Mapping of Environmental Health Processes — The BME Approach

  • George Christakos
  • Dionissios T. Hristopulos
Chapter

Abstract

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.

Keywords

Minimum Mean Square Error Hard Data Linear Minimum Mean Square Error Soft Data Bayesian Maximum Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • George Christakos
    • 1
  • Dionissios T. Hristopulos
    • 1
  1. 1.School of Public Health, Department of Environmental Sciences and EngineeringThe University of North CarolinaChapel HillUSA

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