Appendix and code for "Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age"
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2026
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| author | Bilotti, Giacomo |
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| contents | <p>This zenodo repository contains the scripts and data necessary to replicate the results presented in a book titled:</p> <h1>Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age</h1> <p><strong>Giacomo Bilotti</strong></p> <p>Sidestone Press, 2026 <a href="https://doi.org/10.59641/3t0242wf">https://doi.org/10.59641/3t0242wf</a></p> <div> <h2>Repository maintainer</h2> <p>Giacomo Bilotti (<a href="mailto:bilottigiacomo@gmail.com">bilottigiacomo@gmail.com</a>)</p> <p>Institute of Pre- and Protohistoric Archaeology, Kiel University, Kiel, Germany (former)</p> <p>Social Resilience Lab, Center for Humanities Computing, Aarhus University, Aarhus, Denmark (current)</p> </div> <div> <h2>Abstract</h2> <p>This work aims to study population dynamics and land-use patterns in the South-Western Baltic region during the Neolithic and Bronze Age (4100 to 500 BCE). Understanding demography is essential for comprehending socio-cultural transformations, as demographic patterns have long been recognised as major drivers of social change and complex dynamics. Palaeodemography has gained momentum in archaeology and become a central aspect of the discipline thanks to recent methodological and computational advances. Despite that, the discipline faces several challenges due to the patchiness of archaeological data. In order to overcome this issue, this study uses a multi-proxy and formal approach aiming to reduce chronological uncertainty and improve spatial resolution, thereby enhancing understanding of regional variations. This study produced two different estimates: labour-based carrying capacity and absolute demographic reconstructions. In none of the study regions the carrying capacity was reached, challenging the hypothesis of a correlation between population dynamics and reaching the full capacity of a region. The study also identified patterns of growth and decline in absolute demographic estimates. These patterns align with the boom and bust phases identified in the literature for many European regions. However, each sub-area exhibited distinct patterns in terms of absolute change and the timing of peaks, which cannot be observed at regional or supra-regional scales. Despite these differences, global population trends across sub-regions were identifiable, with a general demographic increase during the Early Neolithic, peaking in the Middle Neolithic, followed by a period of lower population densities during the Younger Neolithic. The patterns for the Late Neolithic and Bronze Age varied depending on the region. Comparisons with other studies show that while global trends were identified, some are either smoothed out in large-scale studies or incorrectly identified and points towards the necessity of a more localised, yet formal and fully reproducible, approach to palaeodemography.</p> </div> <div> <h2>Overview</h2> <p>This repository contains the Appendix of the book titled:</p> <p><strong>Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age</strong></p> <p>The Appendix allows the reader to reproduce the results and provides additional information to complement the content of the book. This work originated as a doctoral thesis defended at the Faculty of Mathematics and Natural Sciences of University of Kiel, and has been revised and expanded for publication.</p> <p>On GitLab, only the scripts are provided. The full dataset is available via Zenodo: (<a href="https://doi.org/10.5281/zenodo.18374080">10.5281/zenodo.18374080</a>).</p> </div> <div> <h2>Repository structure</h2> <pre><code>. ├── appendix.qmd # Main Quarto document (appendix) ├── appendix.bib # Bibliography ├── data/ # Input and derived data │ ├── derived_data/ # Outputs generated during the analysis │ │ ├── cluster_results/ # Cluster maps (PNG) │ │ ├── csv/ # csv outputs │ │ ├── rasters/ # Raster covariates (excluding soils) │ │ ├── gpkgs/ # Spatial vector data │ │ ├── ppm_plots/ # Point process model plots (PNG) │ │ ├── rds/ # Intermediate R objects │ │ ├── rhohats/ # Intensity estimates by region/phase │ │ ├── soils/ # Soil covariates │ │ └── sub_area_cluster_results/ # Cropped territory maps ├── figures/ # Figures used in the manuscript ├── scripts/ # Auxiliary R scripts (data preparation, modelling) ├── renv/ # used to manage package versions ├── renv.lock # tracking package versions └── README.md # This file</code></pre> <p>The <code>derived_data/</code> directory contains all intermediate and final outputs used in the appendix.</p> </div> <div> <h2>Reproducing the results</h2> <p>First, restore the R environment:</p> <pre><code>install.packages("renv") renv::restore()</code></pre> <p>Then, open <code>appendix.qmd</code> in RStudio or render it from the terminal:</p> <pre><code>quarto render appendix.qmd</code></pre> <p>Many chunks are set to <code>eval = false</code> as intermediate/final results and figures are provided and do not need to be recreated. However, these can be set to <code>eval = true</code> if full recomputation is desired.</p> <blockquote> <p>⚠️ <strong>Warning</strong></p> <p>Some analyses are computationally expensive. Heavy chunks can take between a few minutes and ~1 hour depending on your machine.</p> <p>Several code chunks are parallelised. Make sure your machine has enough cores available and that your system supports the parallelisation method used. The number of cores can be adjusted in the setup chunk.</p> </blockquote> <div> <h3>Computational environment</h3> <p>The analysis was developed and tested with:</p> <ul> <li>R 4.5.1</li> <li>Quarto (for rendering <code>.qmd</code> files)</li> <li>Ubuntu 22.04 LTS</li> </ul> <p>Key R packages:</p> <ul> <li>spatstat (3.4-1 and related sub-packages)</li> <li>tmap (4.2)</li> <li>ggplot2 (4.0.0)</li> <li>rcarbon (1.5.2)</li> <li>sf (1.0-24)</li> <li>terra (1.8-70)</li> </ul> <p>A full <code>sessionInfo()</code> is available in the appendix for complete reproducibility.</p> <p>To restore the exact package versions used in the analysis run the following:</p> <pre><code>install.packages("renv") renv::restore()</code></pre> <p>This will install all required packages with the versions recorded in renv.lock.</p> </div> </div> <div> <h2>Citation</h2> <p>If you use this code or its results, besides the repository please cite the book:</p> <p>Bilotti, G. 2026. <em>Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age.</em> Scales of Transformation 23. Sidestone Press. <a href="https://doi.org/10.59641/3t0242wf">https://doi.org/10.59641/3t0242wf</a></p> </div> <div> <h2>License</h2> <p>CC BY 4.0</p> </div> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18374080 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Appendix and code for "Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age" Bilotti, Giacomo <p>This zenodo repository contains the scripts and data necessary to replicate the results presented in a book titled:</p> <h1>Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age</h1> <p><strong>Giacomo Bilotti</strong></p> <p>Sidestone Press, 2026 <a href="https://doi.org/10.59641/3t0242wf">https://doi.org/10.59641/3t0242wf</a></p> <div> <h2>Repository maintainer</h2> <p>Giacomo Bilotti (<a href="mailto:bilottigiacomo@gmail.com">bilottigiacomo@gmail.com</a>)</p> <p>Institute of Pre- and Protohistoric Archaeology, Kiel University, Kiel, Germany (former)</p> <p>Social Resilience Lab, Center for Humanities Computing, Aarhus University, Aarhus, Denmark (current)</p> </div> <div> <h2>Abstract</h2> <p>This work aims to study population dynamics and land-use patterns in the South-Western Baltic region during the Neolithic and Bronze Age (4100 to 500 BCE). Understanding demography is essential for comprehending socio-cultural transformations, as demographic patterns have long been recognised as major drivers of social change and complex dynamics. Palaeodemography has gained momentum in archaeology and become a central aspect of the discipline thanks to recent methodological and computational advances. Despite that, the discipline faces several challenges due to the patchiness of archaeological data. In order to overcome this issue, this study uses a multi-proxy and formal approach aiming to reduce chronological uncertainty and improve spatial resolution, thereby enhancing understanding of regional variations. This study produced two different estimates: labour-based carrying capacity and absolute demographic reconstructions. In none of the study regions the carrying capacity was reached, challenging the hypothesis of a correlation between population dynamics and reaching the full capacity of a region. The study also identified patterns of growth and decline in absolute demographic estimates. These patterns align with the boom and bust phases identified in the literature for many European regions. However, each sub-area exhibited distinct patterns in terms of absolute change and the timing of peaks, which cannot be observed at regional or supra-regional scales. Despite these differences, global population trends across sub-regions were identifiable, with a general demographic increase during the Early Neolithic, peaking in the Middle Neolithic, followed by a period of lower population densities during the Younger Neolithic. The patterns for the Late Neolithic and Bronze Age varied depending on the region. Comparisons with other studies show that while global trends were identified, some are either smoothed out in large-scale studies or incorrectly identified and points towards the necessity of a more localised, yet formal and fully reproducible, approach to palaeodemography.</p> </div> <div> <h2>Overview</h2> <p>This repository contains the Appendix of the book titled:</p> <p><strong>Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age</strong></p> <p>The Appendix allows the reader to reproduce the results and provides additional information to complement the content of the book. This work originated as a doctoral thesis defended at the Faculty of Mathematics and Natural Sciences of University of Kiel, and has been revised and expanded for publication.</p> <p>On GitLab, only the scripts are provided. The full dataset is available via Zenodo: (<a href="https://doi.org/10.5281/zenodo.18374080">10.5281/zenodo.18374080</a>).</p> </div> <div> <h2>Repository structure</h2> <pre><code>. ├── appendix.qmd # Main Quarto document (appendix) ├── appendix.bib # Bibliography ├── data/ # Input and derived data │ ├── derived_data/ # Outputs generated during the analysis │ │ ├── cluster_results/ # Cluster maps (PNG) │ │ ├── csv/ # csv outputs │ │ ├── rasters/ # Raster covariates (excluding soils) │ │ ├── gpkgs/ # Spatial vector data │ │ ├── ppm_plots/ # Point process model plots (PNG) │ │ ├── rds/ # Intermediate R objects │ │ ├── rhohats/ # Intensity estimates by region/phase │ │ ├── soils/ # Soil covariates │ │ └── sub_area_cluster_results/ # Cropped territory maps ├── figures/ # Figures used in the manuscript ├── scripts/ # Auxiliary R scripts (data preparation, modelling) ├── renv/ # used to manage package versions ├── renv.lock # tracking package versions └── README.md # This file</code></pre> <p>The <code>derived_data/</code> directory contains all intermediate and final outputs used in the appendix.</p> </div> <div> <h2>Reproducing the results</h2> <p>First, restore the R environment:</p> <pre><code>install.packages("renv") renv::restore()</code></pre> <p>Then, open <code>appendix.qmd</code> in RStudio or render it from the terminal:</p> <pre><code>quarto render appendix.qmd</code></pre> <p>Many chunks are set to <code>eval = false</code> as intermediate/final results and figures are provided and do not need to be recreated. However, these can be set to <code>eval = true</code> if full recomputation is desired.</p> <blockquote> <p>⚠️ <strong>Warning</strong></p> <p>Some analyses are computationally expensive. Heavy chunks can take between a few minutes and ~1 hour depending on your machine.</p> <p>Several code chunks are parallelised. Make sure your machine has enough cores available and that your system supports the parallelisation method used. The number of cores can be adjusted in the setup chunk.</p> </blockquote> <div> <h3>Computational environment</h3> <p>The analysis was developed and tested with:</p> <ul> <li>R 4.5.1</li> <li>Quarto (for rendering <code>.qmd</code> files)</li> <li>Ubuntu 22.04 LTS</li> </ul> <p>Key R packages:</p> <ul> <li>spatstat (3.4-1 and related sub-packages)</li> <li>tmap (4.2)</li> <li>ggplot2 (4.0.0)</li> <li>rcarbon (1.5.2)</li> <li>sf (1.0-24)</li> <li>terra (1.8-70)</li> </ul> <p>A full <code>sessionInfo()</code> is available in the appendix for complete reproducibility.</p> <p>To restore the exact package versions used in the analysis run the following:</p> <pre><code>install.packages("renv") renv::restore()</code></pre> <p>This will install all required packages with the versions recorded in renv.lock.</p> </div> </div> <div> <h2>Citation</h2> <p>If you use this code or its results, besides the repository please cite the book:</p> <p>Bilotti, G. 2026. <em>Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age.</em> Scales of Transformation 23. Sidestone Press. <a href="https://doi.org/10.59641/3t0242wf">https://doi.org/10.59641/3t0242wf</a></p> </div> <div> <h2>License</h2> <p>CC BY 4.0</p> </div> <p> </p> |
| title | Appendix and code for "Population dynamics and land-use patterns in the southwestern Baltic region during the Neolithic and the Bronze Age" |
| url | https://doi.org/10.5281/zenodo.18374080 |