A New Framework for Landscape Research in Archaeology

Informations projet

NeFLaRA

N° de convention de subvention: 101062882

DOI

10.3030/101062882

Projet clôturé

Date de signature de la CE 28 Juillet 2022

Date de début 1 Septembre 2022

Date de fin 31 Août 2024

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 230 774,40

Coordonné par

AARHUS UNIVERSITET
Denmark

Periodic Reporting for period 1 - NeFLaRA (A New Framework for Landscape Research in Archaeology)

Période du rapport: 2022-09-01 au 2024-08-31

Résumé du contexte et des objectifs généraux du projet

NeFLaRA (A New Framework for Landscape Research in Archaeology) is an interdisciplinary project that will help address landscape archaeology’s most fundamental challenges: 1) the uncertainty inherent in archaeological reconstructions of past landscapes as a result of the fragmentary nature of archaeological data, 2) the resulting uncertainty in computational models based on fragmentary data; and 3) the geo-visualization of the results. Computational and other methods in archaeological landscape research (i.e. the study of the relationship between past societies and their environments), and in the humanities more broadly, focus almost exclusively on spatial elements, although there is a clear notion that time and histories are fundamentally connected to landscape. Yet, this dominance of space in landscape research suffers from a major challenge: the fragmentary nature of archaeological data, particularly when this data comes from ‘non-systematic regional’ survey as opposed to ‘systematic total area’ survey. This project will develop a new and much-needed robust quantitative framework to address this issue, focusing in particular on three gaps in current methodology: (1) the lack of validated methods for quantifying uncertainty of non-systematic regional archaeological data and models, (2) the poorly explored field of research combining spatial analysis (statistics and geographical information systems [GIS]) and network analysis to study non-systematic regional databases, and (3) the lack of a best practice methodology for geo-visual representations that take into account uncertainty in multiscalar spatial data. The training and research offered by NeFLaRA will position the experienced researcher (ER) as an expert in landscape research, spatial analysis and computational modelling for the study of the past, and will enable him to significantly influence paradigms and practice in archaeological landscape research.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

During the time of this project, several tasks were executed that led to obtaining the desired output. These tasks consisted of carrying on a literature review, collaborations with key colleagues, following training in programming and spatial network analysis, preparing the legacy data to be used for analysis, performing analysis, modelling results, giving oral presentations and conferences, writing research papers, and building a web application for the uncertainty quantification of spatial models based on spatial statistical methods.
The main achievements are, first, the creation of a new framework to assess robustness and quantify uncertainty in computational models based on spatial statistical methods. This achievement was published in a high-impact journal. The development of this framework was presented at international conferences.
The second main achievement is the creation of an open-access web application so that archaeologists are able to use our framework in their databases. This web application was done by the Centre for Humanities Computing of Aarhus University, and will be hosted in their website. This application is open-access and free to use.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

There are two main results beyond the state of the art. The first is the robustness and uncertainty assessing framework, which includes the framework itself and the web application. The second is the proposal for a new framework for computational landscape archaeology that combines the methodological developments reached with the first result and the consideration of essential theoretical ideas that seem "forgotten" in the discipline. A paper about this framework is in the process of being written.