Seung-Jae Lee presents November 4, 2005
November 02, 2005 | |
Seung-Jae Lee presents his dissertation final oral defense Friday, November 4, 2005 at 9:00 A.M. in 2304 McGavran-Greenberg. Full details are as follows:Models of Soft Data in Geostatistics and Their Application in Environmental and Health Mapping
(Under the direction of Marc L. Serre) Spatiotemporal Geostatistics provides an efficient mapping estimation method to interpolate a variable of interest at unsampled spatiotemporal locations based on sparse measured values. The simple kriging and co-kriging methods of classical Geostatistics have been applied to a wide variety of environmental mapping problems, though these linear estimation methods have well known limitations (Gaussian assumptions, restriction to exact measurements, etc.). More recently the Bayesian Maximum Entropy (BME) method of modern Geostatistics has provided a rigorous mathematical framework that overcomes these limitations, and in particular provides an efficient framework to assimilate data with uncertainty expressed in terms of soft data. The rigorous assimilation of soft data is especially attractive because it allows the integration of data from multiple sources in terms of their uncertainty. However while integrating data from multiple sources is becoming an important research topic, the development of models for soft data is still an emerging field in environmental and health applications. This dissertation is part of this emerging field. Its goal is to advance the development of models for soft data describing the uncertainty associated with existing environmental and health processes, to integrate these soft data in a BME mapping analysis, and test the resulting increase in mapping accuracy in real case studies. In this dissertation three types of data uncertainty are especially emphasized, i.e. uncertainty from measurement errors, uncertainty from stochastic empirical laws between primary and secondary variables, and uncertainty arising from the data observation scale. Each model of soft data is validated using synthetic simulations as well as real case studies that include the analysis of the uncertainty associated with arsenic measurement errors, the arsenic-pH empirical law, and the observation scale of asthma prevalence rate data. Validation analyses show that for each of these case studies, the model developed for the soft data leads to a substantial gain in mapping accuracy over methods not accounting for data uncertainty. Consequently the models of soft data developed can be applied in a variety of real exposure and health mapping situations to provide highly informative maps that will be useful to environmental and public health scientists. Committee: For further information please contact Rebecca Riggsbee Lloyd by email at Rebecca_Lloyd@unc.edu |