Date of Award
2016
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Statistics
First Advisor
Jeff Buzas
Abstract
Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the statistical learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia.
Language
en
Number of Pages
72 p.
Recommended Citation
Kriegman, Sam, "Evolving Spatially Aggregated Features for Regional Modeling and its Application to Satellite Imagery" (2016). Graduate College Dissertations and Theses. 648.
https://scholarworks.uvm.edu/graddis/648