GIS

0041 - GBAC 2016 - Andrew Owens - Aging Mandibular Bison Teeth with ArcGIS

35th Great Basin Anthropological Conference, Reno, Nevada, Oct. 6 - Oct. 8

OWENS, ANDREW (UTAH STATE UNIVERSITY) Aging Mandibular Bison Teeth with ArcGIS

This presentation presents a non-destructive, empirical and replicable method for aging bison teeth. Tooth eruption, growth, and attrition can document age-at-death, which informs on hunting strategies, occupation seasonality, environmental conditions, and herd health. Previous dentition studies utilize numerous tooth metrics that commonly require specimen-destructive research methods. Also, occlusal wear age estimates rely on subjective wear patterning classifications and figures. We suggest a new approach that provides age profiles by “mapping” occlusal wear with ESRi’s AcrGIS software. Planview mandibular tooth photos from the University of Wyoming’s known-age mandible sample, and well-documented prehistoric samples including the Agate Basin, Hawken, Horner, Glenrock, and Vore sites were captured and georeferenced. Next, GIS polygons were digitized for various occlusal surface features. Digitized GIS shape files were then used to generate various occlusal surface feature areas, and multiple statistical methods were employed that explore relationships between quantified occlusal surfaces and specimen ages. 

0040 - GBAC 2016 - Meg Tracy - Modeling Human Locational Behavior

35th Great Basin Anthropological Conference, Reno, Nevada, Oct. 6 - Oct. 8

TRACY, MEG (GREAT BASIN INSTITUTE) Modeling Human Locational Behavior in Montane Settings

Models were developed to predict spatial distribution of prehistoric archaeological site potential in the Sawtooth National Forest. Archaeological data and environmental parameters were collected and processed in a GIS. Predictor variables were evaluated to discover correlates with human locational behavior & compared against a control dataset. Three modeling methods were used: Logistic Regression, Regression Tree, and Random Forest. These models were assessed for efficacy using k-fold cross-validation and gain statistics. Although observed relationships could result from biases in archaeological data and predictors, results suggest a strong correlation between environment and prehistoric site location.