DOI: 10.5281/zenodo.14938958
title: Supplementary Information for: Points, patterns, and predictions in archaeological settlement data: Exploring site-environment relationships of Paracas and Nasca communities in the Peruvian Andes through point pattern analysis and predictive modeling

description: This repository contains the necessary instructions to carry out the analyses described in the paper titled:

Points, patterns, and predictions in archaeological settlement data: Exploring site-environment relationships of Paracas and Nasca communities in the Peruvian Andes through point pattern analysis and predictive modeling

Authors:



Giacomo Bilotti(a), Markus Reindel(b), Johny Isla(c), Christian Mader(d)





Institute of Pre- and Protohistoric Archaeology, Kiel University, Kiel, Germany




Commission for Archaeology of Non-European Cultures, German Archaeological Institute, Bonn, Germany




Nasca-Palpa Management Plan, Peruvian Ministry of Culture, Nasca, Peru




ArchDepth Research Group, Bonn Center for Dependency and Slavery Studies, University of Bonn, Bonn, Germany





Abstract

This paper provides a novel framework for studying settlement patterns during pre-Inka times in the western Andes, using Point Pattern Analysis (PPA). PPA is a very flexible statistical method widely used to investigate spatial relationships between archaeological sites, offering insights into settlement dynamics and human-environment interactions. Nevertheless, the technique has found limited application in the Andean region, partly due to data availability and historical research trajectories. This study aims to fill this gap and employs PPA to analyze settlement patterns and site-environment relationships in the Palpa valleys of the western Andes during the Paracas (800–160 BCE) and Nasca (160 BCE–620 CE) periods. Our analysis examines the spatial structure of prehispanic settlements in relation to landscape features, identifying factors influencing site location choices and their evolution over time. Furthermore, we use the results from PPA to predict site intensity in nearby regions that were only marginally investigated archaeologically in order to identify the most relevant areas for future research activity. The performance of the model proposed here is tested at different levels in order to improve our knowledge and increase the fit of the final model. The results show changes over time in the occupation of the landscape, most of which were directed towards optimizing agricultural production. However, we have also detected a strong impact of mobility during certain periods and inter-site interaction.




Structure of the repository

The repository folder is structured as follows:



README.md: This file (repository overview).

analyses.qmd: Quarto file performing all the analyses needed for the paper.

scripts/: Contains all R scripts for data preparation, analysis, and visualisation.



covariates/: Contains the R scripts to produce most of the covariates required for the analyses.



00_area_creation.R: Defines the area of the study.

01_DEM.R: (down)loads the DEM and processes it.

01_rivers.R: Calculates distance from rivers.

02_FETE.R: Computes the FETE LCPs following Bilotti et al. 2024 (it can be iterated to avoid crashes).

03_sum_LCPs.R: Sums the outputs of the previous scripts to get a single raster (only necessary if the previous step was iterated).

04_covariates.R: Computes slope, TPIs and distance from LCPs.

05_plot_covs.R: Creates and saves the plots of the covariates.



test_correlation_covariates.R: Tests the autocorrelation of the different covariates.

sites_plots.R: Creates and saves the plots for sites divided by phase.

SI_sec_5.R: Qualitatively compares Silverman 2002 results with the ones presented in the paper.

SI_sec_6.R: Generates the figure included in section 6 of the SI.

plot_predictive_models.R: Generates Fig. 6 of the paper.

Kres_intervals.R: Automatically detects the interval of the Kres function that lie outside the CI. This script is called directly from the QMD when needed.



data/: Raw and derived data necessary for the paper.



raw_data: Where raw input files should be stored.

derived_data: Contains processed or derived datasets.



csv/: Contains the predictive model results for each site and period.

Supplementary Information.pdf: Contains the SI in PDF format.


In GitLab only the scripts are available.