Archaeological predictive model of an urban area. The study case of Pisa, Italy

The Department of Archaeological Science of the University of Pisa is undertaking a research project aimed at the creation of a predictive model for urban areas. The case study is the town of Pisa, but the aim is to realize a replicable model useful for similar urban areas. Through the use of spatial and geostatistical analysis, the cooperation with geologists to analyse to ancient surrounding environment and with mathematicians to elaborate a specific algorithm, we want to realize an Archaeological Information System able to define the specific nature of archaeological practice. Pisa’s AIS was developed to manage heterogeneous data, which drawn the urban archaeological complexity, and to develop effective predictive tools, working on an intermediate scale which allows to analyse how the geographic space have influenced the economic, political and logistic choices. This has led to the need to work with both topographical (geomorphologic, hydrographical, toponymic data, etc.) and urban data (archaeological stratifications, buildings, road network, hypotheses of historians and archaeologists, etc.), combining inter-site analysis and archaeological excavation GIS’ resources. To combine multi-temporal and multi-scale data, it was necessary to provide for digital data conversion and georeferencering of archaeological excavation data acquired at different times and different scales and the integration and overlap of data obtained with different techniques and diverse topographical reliability and precision. The particular attention to the aspect of management of the archaeological raw data, that is all the excavation and fieldwork recording (planning of context, context recording sheet, photographs, findings quantification sheet), suggested the necessity to realize an open digital archive and to provide possible standardization of digital formats, metadata records and archaeological data recording, so as to allow the comparison between the data.