Archaeology, Environment and Human History: Examining the Spatial Links Between Human Settlements and Environmental Change in Iceland

• Main Author: Rønning, Kaja
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: The University of Edinburgh 2020
• Subjects: Self-Organising Maps, Cluster Analysis, High-Dimensional Data Visualisation, Environmental Archaeology, Human Ecodynamics, Cross-Disciplinary Research; Iceland; North Atlantic
• Online Access: https://dx.doi.org/10.7488/era/893; https://era.ed.ac.uk/handle/1842/37612

Research on how humans have interacted with a changing environment over time requires linking complex data and information from a range of disciplines and contextualise it in both time and space. In recent years such interdisciplinary research has become increasingly more frequent as a way of unveiling hidden patterns between data from a wide range of subjects. One such research initiative is dataARC, whose objective is to enable studies of human ecodynamics around the North Atlantic during the middle ages, using both archaeological, environmental and historical data. This paper describes a project developed within the wider framework of dataARC and aims to help bridge the gap between research from multiple disciplines in a spatial context by implementing a multi-dimensional approach and produce a visualising tool which effectively combines cross-disciplinary datasets and appropriately map their connections in geographic space. The study focuses on investigating spatial connections between literary, environmental and zooarchaeological data from Iceland, in order to create a visualisation prototype which can be implemented into the continuing work of dataARC. Using Self-Organising Maps (SOM), an unsupervised clustering technique, the study explores methods synthesising this information by identifying 10 cluster profiles with specific signatures related to the combination of sets of attributes or indicators. This analysis makes clear the considerable potential of a SOM approach in advancing pattern recognition between cross-disciplinary data.