Examining Archaeology, Society, and the Promise of Integrating ‘Big’ Data from Archaeological and non-Archaeological Sources.

Summary

This is an abstract from the "Archaeological Vision in the Age of Big Data" session, at the 84th annual meeting of the Society for American Archaeology.

In order for digitally published data to be useful it has to be useable, and in the case of big-data, interoperable with other data sources. This paper explores one way in which this can be accomplished through an examination of how archaeological site densities across the eastern and midwestern United States relate to social factors such as poverty, income, racial demographics, and historically disadvantaged regions over the past five decades. These analysis was accomplished through the use of data gleaned from county level summary statistics from past United States Decennial Censuses from 1960 to 2010 and the Digital Index of North American Archaeology (DINAA). (DINAA is a collaborative, multi-state effort to create an open, secure, queryable database of archaeological site data/densities across North America). Combining these datasets, while seemingly straightforward, was particularly difficult due to issues of standardization across DINAA’s various partner states. Through this analysis, we hope to demonstrate the ways in which archaeology as a discipline is a reflection of American society, and identify potential biases in archaeological practice. Additionally, this paper illustrates the need for data publishers to utilize common identifiers in the structure of their published data.

Cite this Record

Examining Archaeology, Society, and the Promise of Integrating ‘Big’ Data from Archaeological and non-Archaeological Sources.. Robert DeMuth, Joshua Wells, Kelsey Noack Meyers, Eric Kansa, Stephen Yerka. Presented at The 84th Annual Meeting of the Society for American Archaeology, Albuquerque, NM. 2019 ( tDAR id: 451907)

Spatial Coverage

min long: -168.574; min lat: 7.014 ; max long: -54.844; max lat: 74.683 ;

Record Identifiers

Abstract Id(s): 25602