Modeling timescapes: Delineating Site Exploitation Territories (SET) by using topography derivates and the open-source statistical language R

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URI: http://hdl.handle.net/10900/146417
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1464174
http://dx.doi.org/10.15496/publikation-87758
Dokumentart: ConferencePaper
Date: 2023-10-31
Source: Human History and Digital Future : Proceedings of the 46th Annual Conference on Computer Applications and Quantitative Methods in Archaeology
Language: English
Faculty: 5 Philosophische Fakultät
Department: Archäologie
DDC Classifikation: 930 - History of ancient world to ca. 499
Keywords: Siedlungsarchäologie , Archäologie , Hang
Other Keywords: Erschließungsgebiet
Archäologische Theorie
Toblers Wanderfunktion
Settlement Archaeology
Site Exploitation Territory
Archaeological Theory
Slope
Toblers Hiking Function
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Abstract:

This paper provides a review of the history and archaeological applications of Site Exploitation Territories (SET) and presents the first seamless workflow for defining SET using the open source statistical language R. The concept of the SET was developed in the 1970s as an analytical tool to study finds from archaeological sites in relation to their geographical environment. A SET designates a time-distance based territory, which is visited on a daily basis by sedentary farmers or mobile groups as they deal with their subsistence. Therefore, the shape of a SET depends on the topography surrounding a site: In landscapes with a flat relief SET have an almost circular shape, in mountainous regions they are more distorted. Until recently, the determination of SET was performed manually using simple walking distances. Today, these results are hardly reproducible. The presented workflow is easy to use and calculates SET in a fast and reproducible way while taking into account walking speed and topography (slope) via Tobler’s Hiking Function. It is tested on four digital elevation models (DEM) using 87 settlements dating to the pre-Roman Iron Age, located in the Baar region in south-western Germany. Based on the results of the case study, we recommend the use of open source CGIAR-CSI SRTM data. The results are nearly identical to those based on LiDAR data and require significantly less computational time for processing.

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