Visible Dead: Ancestral Landscapes in Prehistory between the Mediterranean and the Near East

Although archaeological investigations have long been concerned with the above-ground burial structures that populate especially the marginal landscapes of the Near East and the Caucasus, such as kurgans, dolmens, and nawamis, attempts at their understanding within the cognitive world of ancient societies have often been hampered by the lack of archaeological data from potentially associated settlements. Thus, this research aims at investigating the primary reasons (economic, symbolic and/or cosmological-religious) for the localization and spatial configuration of the sites selected by prehistoric communities that practiced pastoralism at different mobility scales to embed their ancestral memory within the natural landscape. The main objective that it will try to achieve is the definition of a theoretical and methodological approach to the holistic and transcultural study of funerary sites lacking textual and associated settlement data in order to represent their visual impact within the original landscape, provide an interpretive paradigm of the ways groups practicing mobility at different scales model the landscape to incorporate tangible signs of their passage; and attempt to define a new understanding of the network of relationships between the examined communities and their deceased. To approach the reasons behind the choices of the places selected by ancient communities to embed their ancestral memory in the landscape, the aforementioned goals will be pursued through a phenomenological-cognitive perspective aiming at the reconstruction of ancient patterns of movements and the visual properties of selected case studies from the Early Bronze Age Caucasus and Southern Levant. The best approximation of the reality experienced by past subjects will be modeled using a semantic approach to movement, and a cache of visibility analyses within a 3D GIS to verify the visual impact of the selected necropolises from reconstructed optimal path networks.