Evaluating Archaeological Predictability Across the Western United States
Author(s): Paul Burnett
This is an abstract from the "Novel Statistical Techniques in Archaeology II (QUANTARCH II)" session, at the 84th annual meeting of the Society for American Archaeology.
Human behavior is patterned in relation to the environment, and these patterns are approximated by the archaeological record. Similarly, the ability to discover archaeological material is patterned in relation to the environment. Geographic Information Systems and statistical software have been used to develop multiple discovery-based spatial probability models across millions of acres in the western United States. Some models work better than others. However, why some models are better at predicting archaeological sites has not been an explicit focus of previous research. One hypothesis is that some archaeological landscapes are more conducive to modeling than others. Holding predictive methods constant, this study evaluated differences in model performance across various landscapes. Understanding the major archaeological and environmental parameters driving model success and failure is important considering the potential cost and time involved in model development. Bare ground visibility, topographic variability, and the use of categorical variables such as landform, plant communities, and soil types are major environmental parameters influencing model performance, as are the availability, accuracy, and distribution of site and survey data. By defining the underlying factors driving model success and failure while holding the statistical techniques constant, we gain new insight into the interpretation of archaeological landscapes through modeling.
Cite this Record
Evaluating Archaeological Predictability Across the Western United States. Paul Burnett. Presented at The 84th Annual Meeting of the Society for American Archaeology, Albuquerque, NM. 2019 ( tDAR id: 452316)
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min long: -168.574; min lat: 7.014 ; max long: -54.844; max lat: 74.683 ;
Abstract Id(s): 24914