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

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.

A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19.

The Levant is highly vulnerable to climate change and experiences prolonged heat waves that have led to societal crises and population displacement. In addition, the region has been impacted by further socio-political turmoil at least since 2010, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and unregulated construction activity. This article uses climate data, satellite imagery, and land cover information in a multicomponent trend analysis to illustrate the substantial increase in construction activity and to highlight the intricate relationship between climate change predictions and current socio-political development in the Levant. The analyses were performed using annual and seasonal composites of MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) datasets with a spatial resolution of 250 m compared to climate indices of the GLDAS (Global Land Data Assimilation System) Noah Land Surface Model L4 dataset for the period 2001-2023. Surface reflectance and climatic parameters were then evaluated on the basis of socio-cultural factors, such as population dynamics, governmental decision-making, water withdrawal regulations, and built-up change as a result of large-scale migration processes. All analyses were conducted using R-software and can be reproduced and replicated using the code and the data provided in this article and the repository.