The last few years have witnessed an upsurge in archaeological and historical studies of the Adriatic, with the creation of a few major projects and an increase in analytical publications. Sometimes considered to be relatively marginal in the light of Roman political and economic development, the sea became ever more fundamental to late antique and medieval commercial geography with the establishment of the new capital at Constantinople and the gradual breakdown of the old Roman communication system and markets. With the eventual loss to the Empire of much of the Italian peninsula following the Lombard invasion in the late 6th century, the Adriatic developed as a preferential route linking the Aegean and the entire Byzantine world with northern Italy, the Rhine corridor, the north-western Balkans, and beyond. From the perspective of someone living and working at the entrance to the Adriatic, not far from the ports of Brindisi and Otranto, I intend to examine how the study of ceramics is helping us to understand the complex dynamics of changing relations between late antiquity and the early Middle Ages. I hope to illustrate some of the things that have been achieved in recent years, as well as to indicate directions for future research.
Between 2011 and 2013, a project for developing the archaeological information system of Verona (called SITAVR) was started by the University of Verona and the Soprintendenza per i Beni Archeologici of Veneto and with the financial support of the Regional Agency and the Bank institute Banca Popolare di Verona. The first step was determined by a collaboration with the Soprintendenza Speciale per i Beni Archeologici di Roma (SSBAR), which since 2007 has been developing an Information System for the Italian capital. Thanks to the support from the colleagues and the conventions between the public administrations involved, it was possible to start the project using the data model and databases created for Rome as a basis. The second step was to study and adapt these artefacts to a smaller town like Verona, taking into consideration the different cataloguing necessities. During this phase, a new methodology (based on GeoUML model) and its tools were used in order to analyze the database of Rome and to create the conceptual schema as a reverse engineering process. The usage of the GeoUML tools allows us to obtain automatically the physical schema and the documentation for the new database of Verona. All the data collected will be available to the general public, both for a better public comprehension of the Information System content and eventually for reuse in other similar projects.
Predictive modelling is a set of techniques, used since the 1970s, to predict the location of archaeological sites in uninvestigated areas as an aid to spatial planning, for example, in Cultural Resource Management. Predictive modelling is also used to develop and test scientific models of human locational behaviour, as it is based on either statistical extrapolation from known archaeological data, or on explanatory models of site location preference. In practice, a number of methods can be used in predictive modelling, and the resulting maps of predicted site locations or density can vary in accuracy. The main difficulties in producing accurate and precise predictive models are coupled to the resolution and representativeness of the archaeological and non-archaeological datasets used, the theoretical frameworks underlying the models, and the nature, or lack, of model testing. Nonetheless, predictive models are often found useful to provide basic protection to areas of high sensitivity, and can save costs for development projects or archaeological investigations.
rs Metrics Comments Media Coverage Abstract Introduction The study area Materials: Cabreo data and GIS Method Results Discussion Conclusions Supporting information Acknowledgments References Reader Comments Figures Abstract The present study seeks to understand the determinants of land agricultural suitability in Malta before heavy mechanization. A GIS-based Logistic Regression model is built on the basis of the data from mid-1800s cadastral maps (cabreo). This is the first time that such data are being used for the purpose of building a predictive model. The maps record the agricultural quality of parcels (ranging from good to lowest), which is represented by different colours. The study treats the agricultural quality as a depended variable with two levels: optimal (corresponding to the good class) vs. non-optimal quality (mediocre, bad, low, and lowest classes). Seventeen predictors are isolated on the basis of literature review and data availability. Logistic Regression is used to isolate the predictors that can be considered determinants of the agricultural quality. Our model has an optimal discriminatory power (AUC: 0.92). The positive effect on land agricultural quality of the following predictors is considered and discussed: sine of the aspect (odds ratio 1.42), coast distance (2.46), Brown Rendzinas (2.31), Carbonate Raw (2.62) and Xerorendzinas (9.23) soils, distance to minor roads (4.88). Predictors resulting having a negative effect are: terrain elevation (0.96), slope (0.97), distance to the nearest geological fault lines (0.09), Terra Rossa soil (0.46), distance to secondary roads (0.19) and footpaths (0.41). The model isolates a host of topographic and cultural variables, the latter related to human mobility and landscape accessibility, which differentially contributed to the agricultural suitability, providing the bases for the creation of the fragmented and extremely variegated agricultural landscape that is the hallmark of the Maltese Islands. Our findings are also useful to suggest new questions that may be posed to the more meagre evidence from earlier periods
