Abstract
Seriation was one of the earliest applications of computers to an archaeological problem. Despite the origins of the technique in numismatics, the vast majority of coinage studies manually sequence coin hoards and issues. For many periods, the coin designs or legends can be used to provide a date. For the Republican series, however, detailed sequences rely on the use of coin hoard data. In recent years, Correspondence Analysis has become the <em>de facto</em> seriation routine of choice. For coinage studies, however, where the period of manufacture was very short, a successful seriation would leave one triangle empty in the re-arranged matrix rather than concentrating the largest values on the diagonal as would be the aim in other situations. The aim of this paper is to assess the effectiveness of various easily-available off-the-shelf open-source seriation routines that have been used in archaeology for the analysis of this type of data. Given that we know a great deal about the pattern of production of Republican coinage, it is possible to create simulated coin hoard assemblages to test the various seriation routines and assess which technique is likely to provide the most successful results. This paper presents the results of applying 14 seriation methods to 27 simulated coin hoard data sets, and discusses the results.
Highlights
Dating artefacts, and by extension contexts, layers and assemblages, is a fundamental concern for all archaeologists
This paper presents the results of applying 14 seriation methods to 27 simulated coin hoard data sets, and discusses the results
Correspondence Analysis (CA) has become the preferred method for analysing count data since the late 1980s, Principal Components Analysis (PCA) can be used in the analysis of artefact counts (Baxter 1994, pp. 61–2), and has been used for the analysis of coinage assemblages
Summary
By extension contexts, layers and assemblages, is a fundamental concern for all archaeologists. It was possible to reduce the influence of early issues prior to the closing date of the earliest hoard without reducing the size of those hoards to the extent that they would not be viable in the analysis Using this experience as a template, and our knowledge of the pattern of coinage production, it is possible to create simulated data sets that have the same characteristics as real data, but for which we know the correct sequence of hoards and issues.. Using this experience as a template, and our knowledge of the pattern of coinage production, it is possible to create simulated data sets that have the same characteristics as real data, but for which we know the correct sequence of hoards and issues.4 By running these simulated data sets through the various seriation routines available, we can compare the true sequence with that suggested by the analyses, and are able to assess which technique is the most successful Using this experience as a template, and our knowledge of the pattern of coinage production, it is possible to create simulated data sets that have the same characteristics as real data, but for which we know the correct sequence of hoards and issues. By running these simulated data sets through the various seriation routines available, we can compare the true sequence with that suggested by the analyses, and are able to assess which technique is the most successful
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