Models and Methods for Spatial Data: Applications in Epidemiological, Environmental, and Ecological Studies

Abstract

This thesis develops new methodologies for applied problems using smoothing techniques for spatial or spatial temporal data. We investigate Bayesian ranking methods for identifying high risk areas in disease mapping, assessing these particularly with regard their performance in isolating emerging unusual and extreme risks in small areas. We build on information obtained through mapping mutivariate outcomes by developing models which investigate if the multivariate spatial outcomes share the same underlying spatial structure.  We develop a general framework for joint modeling of multivariate spatial outcomes for count and zero-inflated count data using a common spatial factor model. 

We also study spatial exposure measures, motivated by an analysis of Comandra blister rust infection for lodgepole pine trees from British Columbia. We contrast nearest distance with other more general exposure measures and consider the impact of misspecification of exposure measures in a semiparametric generalized additive model framework including a spatial residual term modeled as thin plate regression spline. An appealing feature of the new spatial exposure measures considered is that they can be easily adapted to other problems, such as investigation of the association of asthma incidence to traffic exposures.

A common theme in the thesis is the use of functional data analysis and we specifically adapt such methods for assessing spatial and temporal variation of Cadmium concentration in Pacific oysters from British Columbia.

The methodologies developed in these projects widen the toolbox for spatial analysis in other applications in epidemiology, and in environmental and ecological studies.  

 

Supervisor: Charmaine Dean