This thesis investigates the application of computational methods in landscape archaeology, focusing on the Early Iron Age path network of Central Crete, particularly in the key sites of Lyktos,...Show moreThis thesis investigates the application of computational methods in landscape archaeology, focusing on the Early Iron Age path network of Central Crete, particularly in the key sites of Lyktos, Hersonissos, and other secondary ones in the same area. It addresses key methodological and theoretical issues by employing the Python programming language for geospatial analysis and incorporating phenomenological perspectives to enhance understanding of ancient human-environment interactions. Recent advancements in Landscape Archaeology have been significantly influenced by phenomenological approaches introduced by scholars like Tilley and Ingold, who influenced by the philosophers of phenomenology redefined the term “landscape” by emphasizing the embodied and experiential aspects of it. Based on their work, archaeologists like Llobera and Wheatley challenged the quantitative treatment of landscapes and further explored the dynamic relationship between humans and their environments, highlighting the importance of movement and perception in landscape archaeology. Geographic Information Systems have been instrumental in landscape studies, but often reduce landscapes to static and quantitative data. This thesis critiques these limitations and proposes a novel methodological framework using Python for Least Cost Path analysis. This approach offers greater flexibility and insight into the computational processes behind geospatial analysis, addressing issues of conventional GIS tools by providing a detailed and customizable examination of movement patterns. The main research questions are if Python-based LCP analysis can produce results comparable to those from traditional tools like QGIS and if this computational approach, enhanced by phenomenological perspectives can offer deeper insights into the social and path network of Early Iron Age Crete. The findings reveal that Python is a robust tool for geospatial analysis, producing results similar to QGIS while offering enhanced flexibility and detailed examination of computational processes. This methodology highlights the importance of understanding the underlying processes behind geospatial tools and demonstrates Python’s potential for archaeological research. By integrating phenomenological ideas, this thesis interprets the computational results within a broader archaeological context. This approach considers different parameters of how ancient people might have perceived and navigated their surrounding landscape. The analysis uncovers a potential socio-cultural network in Central Crete, with modeled paths suggesting continuity with the earlier Minoan path network of the area and offering insights into connectivity and movement patterns of the Early Iron Age. Overall, this research shows that Python-based methods provide a valuable alternative methodology to traditional GIS and a nuanced understanding of ancient human-landscape interactions.Show less
