This work concerns the analysis of data related to Terra Sigillata, a type of fine tableware with glossy surface commonly used in the Roman Empire. The data were gathered integrating different sources and analysed during the ArchAIDE project (www.archaide.eu), an EU Horizon 2020 RIA funded project which aims to create a system for the automatic recognition of pottery. Statistical techniques were used as explorative in order to summarise main characteristics of data and identify outliers, trends or patterns. We focused on Network Analysis and on the identification of significant temporal breaks in the data. The network structure is given by linking together locations where ceramics were produced to locations where the same ceramics were retrieved, getting 3853 locations forming the vertices, throughout Europe, Middle East and North Africa. We identified communities in the network, i.e. groups of vertices (locations) being densely connected internally but poorly connected externally. Communities were identified within the four temporal periods distinguished, characterised by different production centres emerging and declining in the different phases (Italian, South-Gaulish, Rhine productions), and showing different production dynamics. Temporal breaks were identified by an algorithm minimising the variance within intervals while maximising the variance between intervals. Communities can represent commercial routes adopted by producers, or that established themselves by geographical or historical reasons. This work also underlines how the availability of high volume of data (unfortunately rare in Archaeology), joined with data analysis, allows new insight into archaeological research.

Spatio-temporal network analysis applied to Roman Terra Sigillata data.

Gabriele Gattiglia
Co-primo
;
Nevio Dubbini
Co-primo
;
Francesca Anichini
Co-primo
2019-01-01

Abstract

This work concerns the analysis of data related to Terra Sigillata, a type of fine tableware with glossy surface commonly used in the Roman Empire. The data were gathered integrating different sources and analysed during the ArchAIDE project (www.archaide.eu), an EU Horizon 2020 RIA funded project which aims to create a system for the automatic recognition of pottery. Statistical techniques were used as explorative in order to summarise main characteristics of data and identify outliers, trends or patterns. We focused on Network Analysis and on the identification of significant temporal breaks in the data. The network structure is given by linking together locations where ceramics were produced to locations where the same ceramics were retrieved, getting 3853 locations forming the vertices, throughout Europe, Middle East and North Africa. We identified communities in the network, i.e. groups of vertices (locations) being densely connected internally but poorly connected externally. Communities were identified within the four temporal periods distinguished, characterised by different production centres emerging and declining in the different phases (Italian, South-Gaulish, Rhine productions), and showing different production dynamics. Temporal breaks were identified by an algorithm minimising the variance within intervals while maximising the variance between intervals. Communities can represent commercial routes adopted by producers, or that established themselves by geographical or historical reasons. This work also underlines how the availability of high volume of data (unfortunately rare in Archaeology), joined with data analysis, allows new insight into archaeological research.
2019
978-83-948382-7-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1023244
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