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Tuesday, January 26 • 4:40pm - 5:00pm
Bigfoot Teaches Spatial Statistics In Northwest Ohio

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AUTHORS: Emma S. Spence*, Bowling Green State University; Dr. Andrew J. Gregory*, Bowling Green State University

ABSTRACT: Spatial statistics can be a rather esoteric concept for students to grasp as they have to wrangle with the idea of covariance matrices projected across both space and time. Specifically, students have to understand that as you change the spatial resolution of the data, you have to reevaluate the strength of autocorrelation among both covariates and responses. This conceptual understanding can be confounded when students are plagued by preconceived notions of how covariates ought to impact wildlife. In the spring of 2015 while teaching a graduate level class on spatial statistics, I abstracted the problem of teaching students spatial regression and geographically weighted regression from species centric biases by using Bigfoot as a model organism based on county specific data obtained from http://www.bfro.net/GDB/state_listing.asp?state=oh. I found that this tactic worked well, students were able to allow their investigations and descriptions of results to be data driven, as opposed to conceptually driven. The results of their analyses were also somewhat humorous as we found that Bigfoot in Ohio was positively associated with the percent of the population living in trailer homes and negatively associated with the percent of the landscapes under cultivation. Several students found this to be an interesting model organism, and independently applied the analysis to Bigfoot populations in Oregon. Aside from the possible side-effect of encouraging students to abandon their thesis work for the pursuit of cryptozoology, I found Bigfoot to be an excellent instructional partner for teaching spatial statics. This presentation will feature the final results of the Bigfoot modeling exercise, presented by one the students from the class.

Tuesday January 26, 2016 4:40pm - 5:00pm EST
Emerald A