Date of Award:
5-2002
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Biology
Committee Chair(s)
James W. Haefner
Committee
James W. Haefner
Committee
Edmund D. Brodie, Jr.
Committee
John A. Bissonette
Abstract
As geographical information systems and spatial data become more accessible, predictive spatial modeling in ecology is becoming more common. Unfortunately, not all ecologists possess the necessary skills to successfully combine statistical models and geographical information systems. In response to this problem, I wrote an extension for ArcView® GIS called StatMod. This extension interfaces ArcView GIS with SAS® and S-PLUS® statistical software and walks the user through creating and mapping logistic regression and classification and regression tree models.
StatMod was then used to run a series of analyses that would have been difficult without such a tool. A hypothetical density distribution for a nonexistent species was created, and then the effects of sample size and sampling regime on predictions of this density distribution were investigated. The effect of ground-truthed sample size on model accuracy estimates was also examined.
As expected, model accuracy generally improved as sample size increased. However, the majority of the improvement was seen as the sampled area approached one percent of the study area. Simple random sampling usually performed better than stratified random sampling, although the difference was most apparent when one density classification dominated the landscape. Estimates of model accuracy also improved as the number of ground-truthed sample points increased. Samples stratified by model predictions performed better than simple random samples at small sample sizes, although the reverse was true at large sample sizes. Again, this interaction was most apparent when one density classification dominated the landscape.
Checksum
1e79e76a3b456f25c3be4db54fa19c59
Recommended Citation
Garrard, Christine M., "StatMod: A Tool for Interfacing ArcView® GIS with Statistical Software to Facilitate Predictive Ecological Modeling" (2002). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 8310.
https://digitalcommons.usu.edu/etd/8310
Included in
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .