Potential for Spatial "Big Data" in Historical Archaeology: A Demonstration of Methods and Results
Author(s): Sara Belkin; Daniel Plekhov
Year: 2017
Summary
Historical Archaeology has seen a steadily increasing embrace of Geographic Information Systems (GIS) for the purposes of site recording, preservation and management, but has seen little to no use of the plethora of spatial datasets already publicly available. Such datasets include census, tax, and immigration records, property and housing maps, and archived aerial and satellite imagery, which when properly integrated in a GIS, have great potential for further contextualizing historical archaeological data and analyzing them at geographically broader scales.
In this poster we present some of these publicly available data sources with a case study examining the consumer practices of an Irish-American family in early twentieth-century Milton, Massachusetts. We draw on data from excavations at the M.B. Wakefield Estate, U.S. census records, and other sources to plot where they purchased their household goods and to determine if these places are best characterized as Irish neighborhoods. Through this we can explore questions related to transnationalism, alienation practices, and the importance of community. This case study will illustrate the potential of these sources for use in archaeological studies, the ease with which they can be accessed and utilized with conventional archaeological data, and their use in answering broad anthropological questions.
Cite this Record
Potential for Spatial "Big Data" in Historical Archaeology: A Demonstration of Methods and Results. Sara Belkin, Daniel Plekhov. Presented at The 81st Annual Meeting of the Society for American Archaeology, Vancouver, British Columbia. 2017 ( tDAR id: 430718)
This Resource is Part of the Following Collections
Keywords
General
Big Data
•
Gis
•
Irish Archaeology
Geographic Keywords
North America - Northeast
Spatial Coverage
min long: -80.815; min lat: 39.3 ; max long: -66.753; max lat: 47.398 ;
Record Identifiers
Abstract Id(s): 17155