Spatial computation in Archeology and History
Though traditional methods remain important to study material culture complexes and past human societies, over time and space, novel quantitative approaches based on computational modeling are rapidly gaining momentum. In such context, this session aims at questioning the use of modelling along with the specificity of spatial approaches in the computer applications and quantitative methods applied to the study of ancient societies and at demonstrating the effectiveness of bringing together traditional archaeological questions with innovative technologies related to computational archeology.
These methods often encounter two types of difficulties. In one hand, the spatial study of past societies requires describing and explaining patterns and their dynamics (diffusion of materials, settlements organization, etc.) from incomplete datasets, which often lead to (retro)prediction. To overcome the underlying difficulties, researchers can rely nowadays on multi-modelling approaches, including conceptual models, data mining and data-driven analysis (e.g. machine learning and stochastic models), spatial analysis and computer simulations, at different stage of the work process. In the other hand, the availability of massive digital geo-referenced databases enable scientists to deal with the increasing need of elaborating predictive maps relevant for archaeological risk assessment and cultural heritage management, as decision-making and probabilistic reasoning tool.
Proposals for communication and research topics may cover two complementary subjects: 1) Predictive modelling and the use of quantitative approaches, such as 3D technologies, spatial analysis and remote sensing for archaeological risk assessment and cultural heritage management, with preservation and conservation purposes; 2) reconstructions of the past, to analyze “past-societies themselves” by answering the following questions:
The aim is also to initiate a dialogue within the archaeological and historians communities and the broader community of researchers sharing a common interest in quantitative geographical dimension.