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A BAYES LIKELIHOOD INFORMATION THEORETIC APPROACH FOR THE EXOGENOUS AGGREGATION OF REGIONAL GROUND WATER QUALITY DATA
Citation:
Faulkner*, B P. AND E. A. Greene. A BAYES LIKELIHOOD INFORMATION THEORETIC APPROACH FOR THE EXOGENOUS AGGREGATION OF REGIONAL GROUND WATER QUALITY DATA. Presented at ReVA MAIA Conf, Prussia, PA, May 13 - 16, 2003.
Description:
This work addresses a potentially serious problem in analysis or synthesis of spatially explicit data on ground water quality from wells, known to geographers as the modifiable areal unit problem (MAUP). It results from the fact that in regional aggregation of spatial data, investigators are faced with choosing a level of aggregation appropriate to answer questions at that scale, and, inferences resulting from that choice are dependent on the choice made. This poster presents a proposed solution to the MAUP for regional ground water data interpretation. This approach uses Bayesian inference, and a Bayes likelihood with the Akaike information criterion for optimal mapping of ground water quality data. An example of its use is presented by application to evaluating risk of loading of nitrate to shallow ground water for the U.S. Geological Survey's National Water Quality Assessment (NAWQA) data from rural wells in Maryland. The method facilitates informative and parsimonious reporting of risk at a scale useful to planners, and can also be used to identify areas of data deficiency.