A Visual-Analytics Workflow to Support the Assessment of Pottery Classes. Morphological Categorisation in Sagalassos Red Slip Ware

Abstract

Digital transformation is inevitable in archaeological research, from data collection to the formatting of rules that define fundamental research topics, such as that of typological arrangements. Digitising the archaeological record is a situated process that inherits substantial complexity when it comes to typological classification, since traditional material analyses rely strongly on visual inspection, (tacit) expert knowledge, and first-hand impressions, rather than numbers and algorithms. New numerical and digital approaches are needed that incorporate human contributions and capacities, both from a data science and a material specialist perspective. In this article, we propose a visual-analytics workflow to promote a white-box model for typological class definition of archaeological pottery that aims to align the archaeological and the data analytical rationale, and keep humans in the loop. Using technical pottery drawings as input, we build open-source processes for digitising, assessing and maintaining a typological model of this pottery. Three components of visualisation are discussed: shape representation methods, association representation methods, and label representation methods. The workflow is applied to a dataset of Sagalassos Red Slip Ware profile drawings, a tableware produced in Southwest Anatolia, akin to Italian and South Gaulish terra sigillata. In our study, we assess and map variation within five functional classes, revealing potential subclasses and identifying their mode. Our workflow enriches existing digital practices in the archaeological toolkit, providing higher granularity, integration, and replicability.