Modeling the Rise of the City: Early Urban Networks in Southern Italy

• Main Author: Lieve Donnellan
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-09-01
• Series: Frontiers in Digital Humanities
• Subjects: modeling; ancient cities; urbanization; archaeology; Southern Italy; network theory
• Online Access: https://www.frontiersin.org/article/10.3389/fdigh.2019.00015/full

The rise of the state in Ancient Italy went hand in hand with an increase in infrastructural power, i.e., settlement centralization and urbanization. The paper discusses theoretical challenges and introduces a modeling approach to a case study, one of the earliest cities in Southern Italy, Pontecagnano, with the aim of understanding the community dynamics at the time of the earliest urbanization (ca. 900–600 BC). The model is a two-mode model that derives from social network analysis, an approach that has been fruitfully adapted to archaeological research. The model is applied to detect trends in burial contexts from the community involved. Burial was, at that time, in the region, a key instrument in the creation of memory and display of status and thus for building and consolidating state power. The analytical network model is able to detect the dynamics in the community over time very well: network Cohesion is expanding and contracting, and points to the existence of tension and a tight control of funerary behavior. The study of Centrality of selected nodes provides a good understanding of the strategies in terms of the circulation of key resources. The latter is particularly significant for studying urbanization because the appropriation of resources was not possible without centralization and the development of infrastructure, as well as an ideology. Based on the study of selected resources, it is suggested that an increase in crop storage has played a particular role in the development of state power and the urbanization process at Pontecagnano. In due course, the paper also addresses methodological challenges of working with fragmented datasets when applying models to study the past.