A New Framework for Landscape Research in Archaeology
NeFLaRA (A New Framework for Landscape Research in Archaeology) is an interdisciplinary project that will help address landscape archaeology’s most fundamental challenge: the inherent uncertainty in archaeological reconstructions...
NeFLaRA (A New Framework for Landscape Research in Archaeology) is an interdisciplinary project that will help address landscape archaeology’s most fundamental challenge: the inherent uncertainty in archaeological reconstructions of past landscapes as a result of the fragmentary nature of archaeological data. Landscape archaeology studies the relationship between past societies and their environments. Computational and other methods in archaeological landscape research, and the humanities more broadly, focus almost exclusively on spatial elements, although there is a clear notion that time and histories are fundamentally connected to the landscape. Yet, this dominance of space in landscape research suffers from the major challenge of the fragmentary nature of archaeological data, particularly when this data comes from a ‘non-systematic’ regional survey as opposed to ‘systematic total area’ survey. While landscape archaeology has benefited from computational developments, a protocol to overcome the embedded uncertainty on spatial data is absent. By defining best practices, identifying optimal methods and outlining clear protocols for different datasets and spatial analyses, this project will create a new robust quantitative framework for more accurate computational models of past landscapes, improving their reliability and enhancing their interpretative potential. To achieve this, NeFLaRA will focus on three methodological gaps: (1) the lack of validated methods for quantifying the 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 guidelines and method pipelines for geo-visual representations that take into account uncertainty in multiscalar spatial data.ver más
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