Digital Archaeology: Quantitative approaches, spatial statistics and socioecological modelling

Recent advances in computer and environmental science, climate modelling and other disciplines as well as the availability and processability of (openly shared) big data have triggered fundamental changes in research over the last decades and expanded the toolbox of archaeological methods. While traditional methods (i.e. typochronology, mapping sites) remain important and continue to be used to study material culture complexes and past human societies over time and space, novel quantitative approaches based on spatial analysis, however, are rapidly gaining momentum. The archaeological community has recognized their importance to support and add value to archaeological data as their contextualisation and interpretation. The development of highly specialized plugins and packages in open-source frameworks like R, QGIS and SAGA GIS has enabled researchers to process archaeological data using a much wider range of statistical methods, significantly advancing our ability to understand the spatiotemporal dynamics of past human societies. Tools like unsupervised classification (i.e. clustering and principal component analysis) and machine learning (i.e. regression trees and neural network), which few years ago were only available to statisticians and computer scientists, are rapidly adopted by archaeological researchers. This workshop will provide a forum to present innovative ideas for applying quantitative approaches to better understand the dynamic of human-human and/or human-environment relationship. The aim is also to initiate a dialogue within the archaeological community on the interaction of different approaches to spatial modelling and classification techniques. This event addresses colleagues who would like to exchange their ideas for the use of these innovative tools and demonstrate their relevance for archaeological applications in research, heritage management practice, theory building and construction of narratives/models of (pre-)history.

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