Applying Linear Regression Models to Assess Spatial and Environmental Risk Factors of Chronic Wasting Disease Amongst Deer Populations in Montana

Authors

Presenter(s)

Aidan Burns Mornhinweg

Comments

Presentation: 9:00-10:15, Kennedy Union Ballroom

Files

Description

By applying both GIS and statistical analysis formulas, such as ordinary least squares (OLR) and geographic weighted regression (GWR), to multiple variables, I have been determining risk factors and their % of influence on spreading chronic wasting disease (CWD) amongst various deer populations throughout Montana. Upon analyzing each risk factor (primarily land use data, soil, pH levels, habitat types, carcasses, etc.), I am able to apply my linear regression model to hot spots throughout Montana to determine which areas are most at risk of spreading CWD (both how and why). GIS software, ArcGIS Pro, is my main tool and support for computing my linear regression model and takes into consideration the spatially temporal data and dimensions of this disease. As of now, all of my gathered data is open source. Thank you.

Publication Date

4-17-2024

Project Designation

Capstone Project

Primary Advisor

Shuang-Ye Wu

Primary Advisor's Department

Geology

Keywords

Stander Symposium, College of Arts and Sciences

Institutional Learning Goals

Scholarship; Practical Wisdom

Recommended Citation

"Applying Linear Regression Models to Assess Spatial and Environmental Risk Factors of Chronic Wasting Disease Amongst Deer Populations in Montana" (2024). Stander Symposium Projects. 3661.
https://ecommons.udayton.edu/stander_posters/3661