IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0297931.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297931
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297931&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0297931?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frédéric Lavancier & Ronan Le Guével, 2021. "Spatial birth–death–move processes: Basic properties and estimation of their intensity functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 798-825, September.
    2. Nicoletta D’Angelo & Marianna Siino & Antonino D’Alessandro & Giada Adelfio, 2022. "Local spatial log-Gaussian Cox processes for seismic data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 633-671, December.
    3. Giuseppe Espa & Giuseppe Arbia & Diego Giuliani, 2013. "Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan," Journal of Geographical Systems, Springer, vol. 15(1), pages 31-50, January.
    4. M. N. M. Lieshout, 2020. "Infill Asymptotics and Bandwidth Selection for Kernel Estimators of Spatial Intensity Functions," Methodology and Computing in Applied Probability, Springer, vol. 22(3), pages 995-1008, September.
    5. Christopher S Fowler, 2018. "Key assumptions in multiscale segregation measures: How zoning and strength of spatial association condition outcomes," Environment and Planning B, , vol. 45(6), pages 1055-1072, November.
    6. Roba Bairakdar & Debbie Dupuis & Melina Mailhot, 2024. "Deviance Voronoi Residuals for Space-Time Point Process Models: An Application to Earthquake Insurance Risk," Papers 2410.04369, arXiv.org.
    7. Jeffery Caroline & Ozonoff Al & White Laura Forsberg & Pagano Marcello, 2013. "Distance-Based Mapping of Disease Risk," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 265-290, May.
    8. Nicoletta D'Angelo, 2025. "Detecting Changes in Space‐Varying Parameters of Local Poisson Point Processes," Environmetrics, John Wiley & Sons, Ltd., vol. 36(5), July.
    9. Mola-Yudego, Blas & Selkimäki, Mari & González-Olabarria, José Ramón, 2014. "Spatial analysis of the wood pellet production for energy in Europe," Renewable Energy, Elsevier, vol. 63(C), pages 76-83.
    10. Jeanne-Marie R. Stacciarini & Raffaele Vacca & Liang Mao, 2018. "Who and Where: A Socio-Spatial Integrated Approach for Community-Based Health Research," IJERPH, MDPI, vol. 15(7), pages 1-17, June.
    11. Gril, Lorena & Rendtel, Ulrich, 2026. "Mapping high-income taxpayers in Berlin using kernel-smoothed proportions from aggregated georeferenced data," Discussion Papers 2026/2, Free University Berlin, School of Business & Economics.
    12. Xun Yang & Fabian Becker & Daniel Knitter & Brigitta Schütt, 2021. "An Overview of the Geomorphological Characteristics of the Pergamon Micro-Region (Bakırçay and Madra River Catchments, Aegean Region, West Turkey)," Land, MDPI, vol. 10(7), pages 1-27, June.
    13. Mohammad Ghorbani & Nafiseh Vafaei & Mari Myllymäki, 2025. "A kernel-based test for the first-order separability of spatio-temporal point processes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 34(3), pages 580-611, September.
    14. Yingqi Zhao & Donglin Zeng & Amy H. Herring & Amy Ising & Anna Waller & David Richardson & Michael R. Kosorok, 2011. "Detecting Disease Outbreaks Using Local Spatiotemporal Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1508-1517, December.
    15. François Sémécurbe & Cécile Tannier & Stéphane G. Roux, 2019. "Applying two fractal methods to characterise the local and global deviations from scale invariance of built patterns throughout mainland France," Journal of Geographical Systems, Springer, vol. 21(2), pages 271-293, June.
    16. Mishalani, Rabi G. & Koutsopoulos, Haris N., 2002. "Modeling the spatial behavior of infrastructure condition," Transportation Research Part B: Methodological, Elsevier, vol. 36(2), pages 171-194, February.
    17. Peng Hou & Xiaojian Yi & Haiping Dong, 2020. "A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network," Energies, MDPI, vol. 13(3), pages 1-16, February.
    18. Edith Gabriel, 2014. "Estimating Second-Order Characteristics of Inhomogeneous Spatio-Temporal Point Processes," Methodology and Computing in Applied Probability, Springer, vol. 16(2), pages 411-431, June.
    19. M. N. M. Lieshout, 2024. "Non-parametric adaptive bandwidth selection for kernel estimators of spatial intensity functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(2), pages 313-331, April.
    20. Ondřej Šedivý & Antti Penttinen, 2014. "Intensity estimation for inhomogeneous Gibbs point process with covariates-dependent chemical activity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(3), pages 225-249, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0297931. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.