Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.772237
Title: Predictive modelling and quantitative GIS-based analysis of ritual and settlement landscapes of Neolithic mainland Scotland, c 4000-2500 BC
Author: Graves, Dorothy Jonina
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2009
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Abstract:
Monument sites dominate mainland Scottish Neolithic research because so few settlements are known, less than sixty to date. Mostly found by chance, these sites are highly fragmented, difficult to recognise and resistant to interpretation. Important insights into monumentality have recently been achieved using geographic information system (GIS) based landscape studies, but these approaches have not been applied to the settlement record of the Scottish mainland. Accordingly, this research proposes a new landscape methodology, integrating both settlement and 'ritual' evidence to create the first GIS-based predictive models that identify those locations most likely to have sustained Neolithic setdement over the whole of mainland Scotland. Known settlements and monuments provide social and environmental information, which is quantified and contrasted with a control sample. Multivariate statistical analyses establish the variables that may have guided the placement of known sites, forming the backbone of the predictive models. Test samples kept separate from each model's construction were created from two case study regions - southwest Argyll and the Moray Firthlands. Moray has yielded many known settlements, which are wholly unknown in Argyll. This data was used to verify each model's performance. This research holds great promise for exploiting predictive modelling as a powerful tool in Scottish archaeological research, which should be optimised for regions where limited or no proof of archaeological remains is known.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.772237  DOI: Not available
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