On applications of space-time modelling with 14C age calibration

Open access On Applications of Space–Time Modelling with Open-Source 14C Age Calibration, by T. Rowan McLaughlin J Archaeol Method Theory (2018).

Abstract (emphasis mine):

In archaeology, the meta-analysis of scientific dating information plays an ever-increasing role. A common thread among many recent studies contributing to this has been the development of bespoke software for summarizing and synthesizing data, identifying significant patterns therein. These are reviewed in this paper, which also contains open-source scripts for calibrating radiocarbon dates and modelling them in space and time, using the R computer language and GRASS GIS. The case studies that undertake new analysis of archaeological data are (1) the spread of the Neolithic in Europe, (2) economic consequences of the Great Famine and Black Death in the fourteenth century Britain and Ireland and (3) the role of climate change in influencing cultural change in the Late Bronze Age/Early Iron Age Ireland. These case studies exemplify an emerging trend in archaeology, which is quickly re-defining itself as a data-driven discipline at the intersection of science and the humanities.

Interesting excerpt on Neolithization:

An enduring topic of wide interest is the neolithization of Europe, a cultural and economic development of virtually unparalleled significance given subsequent world events and the permanent environmental changes that were a result. This process began when early Neolithic culture moved from the Near East and Anatolia first through Greece around 7000 BC, then viathe Balkans and peninsular Italy to the interior and western edge of the continent. Much of central Europe was colonised by a distinctive cultural group known as the “LBK” around 5500 BC—an important few centuries that also saw the arrival of the Neolithic in Iberia and intensive activity throughout the Mediterranean region. The Neolithic eventually spread to Britain and Southern Scandinavia shortly after 4000 BC. The remarkable time depth to the process has led to a rich tradition of debate regarding the process of cultural and/or demic diffusion. Thanks to the success of recent, well-funded research projects, a large amount of spatial and radiocarbon data relevant to the phenomenon has been drawn together, much of which made freely available on the internet. A simple but visually effective animated radiocarbon map of the phenomenon can be drawn using data supplied (for example) by Pinhasi et al. (2005), who gathered together radiocarbon data addressing the earliest occurrence of Neolithic cultures in European, Anatolian and Near Eastern settings.(…)

Example frames from an animated map of the spread of farming in Early Neolithic Europe, plotted using data from Pinhasi et al. (2005) and the R code supplied in the supplementary materials

The example animation (see Fig. 4 and the supplementary animation file) illustrates the nature of the process; rarely is the progress across the landscape smooth and progressive, instead activity jumps from place to place and appears in quite distant regions rather suddenly. A particularly notable moment occurs around 5500 cal. BC, which sees sudden expansion across several fronts. The intermittent patterns of movement evident throughout the animation call into question a Neolithic “wave of advance”; something that can be modelled in terms of a wavefront moving a certain number of kilometres per year. Instead, a more realistic model is one where people move sporadically, using multiple points of entry into new regions, and travel across both land- and sea-based routes, and “appearing”, archaeologically speaking, in new areas once a certain pattern of behaviour is established—a process that may take more than a few years (Drake et al. 2016). An alternative explanation, and a clear limitation of the conclusions that may be drawn from this case study (where “only” 765 dates span a 6000-year period in Europe and the Near East), is that the sudden appearance of the Neolithic in new and distant regions could be due to the poorly powered dataset.

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