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Point Pattern Analysis (PPA) as a tool for reproducible archaeological site distribution analyses and location processes in early iron age south-west Germany

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  • Giacomo Bilotti
  • Michael Kempf
  • Eljas Oksanen
  • Lizzie Scholtus
  • Oliver Nakoinz

Abstract

Point Pattern Analysis (PPA) has gained momentum in archaeological research, particularly in site distribution pattern recognition compared to supra-regional environmental variables. While PPA is now a statistically well-established method, most of the data necessary for the analyses are not freely accessible, complicating reproducibility and transparency. In this article, we present a fully reproducible methodical framework to PPA using an open access database of archaeological sites located in south-west Germany and open source explanatory covariates to understand site location processes and patterning. The workflow and research question are tailored to a regional case study, but the code underlying the analysis is provided as an R Markdown file and can be adjusted and manipulated to fit any archaeological database across the globe. The Early Iron Age north of the Alps and particularly in south-west Germany is marked by numerous social and cultural changes that reflect the use and inhabitation of the landscape. In this work we show that the use of quantitative methods in the study of site distribution processes is essential for a more complete understanding of archaeological and environmental dynamics. Furthermore, the use of a completely transparent and easily adaptable approach can fuel the understanding of large-scale site location preferences and catchment compositions in archaeological, geographical and ecological research.

Suggested Citation

  • Giacomo Bilotti & Michael Kempf & Eljas Oksanen & Lizzie Scholtus & Oliver Nakoinz, 2024. "Point Pattern Analysis (PPA) as a tool for reproducible archaeological site distribution analyses and location processes in early iron age south-west Germany," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-25, March.
    • Handle: RePEc:plo:pone00:0297931
    DOI: 10.1371/journal.pone.0297931
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    References listed on IDEAS

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    1. Ben Marwick & Carl Boettiger & Lincoln Mullen, 2018. "Packaging Data Analytical Work Reproducibly Using R (and Friends)," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 80-88, January.
    2. Julian Laabs & Daniel Knitter, 2021. "How Much Is Enough? First Steps to a Social Ecology of the Pergamon Microregion," Land, MDPI, vol. 10(5), pages 1-19, May.
    3. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
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