Computational Approaches to the Study of Movement in Archaeology Theory, Practice and Interpretation of Factors and Effects of Long Term Landscape Formation and Transformation - Polla, Silvia; Verhagen, Philip (Topoi-Berlin Studies of the Ancient World, 2014)
Within the framework of the Excellence Cluster Topoi, a fruitful interdisciplinary debate on space and movement over the long term has developed. The workshop "Computational approaches to movement in archaeology" (organized on January 6 2011 in Berlin) tackled questions related to space and movement in the framework of computational archaeology, landscape archaeology, historical geography and archaeological theory. The current volume, which is the product of this meeting, brings together contributions that show how the study of settlement patterns and movement has been dramatically transformed by the use of spatial technology (GIS), in particular Cost Distance and Least Cost Paths (LCP) Analysis. The term "least cost path" is somewhat deceptive, however, since it is not just the costs of movement, but also the benefits of moving to a particular location that influenced the routes chosen and created. Archaeological theories about the way people moved in the landscape, and how they created and maintained paths and communication networks are often based on relatively abstract notions. For example, several papers in the current volume indicate that visibility may have been an important factor (co-)determining movement and path creation in the landscape. However, the exact parameters involved, and how they influenced the routes chosen, are largely within the realm of speculation. Computer-based modelling can be seen as a sophisticated approach to speculation. It allows us to experiment with the possible parameters involved, change the values and weights of each and inspect the outcome to see whether it conforms to our initial expectations and if it in some way fits the actual archaeological evidence. Most importantly, computer-based models are explicit: since all assumptions are laid out in detail, we can study the consequences of changing them, and the models can be replicated. It is through modelling that different scenarios can be explored and compared to real-world outcomes. Computer-based models are therefore in essence heuristic tools that can help to develop theory and interpretation.