Project Outputs

Modifiable reporting unit problems and time series of long-term human activity

This paper responds to a resurgence of interest in constructing long-term time proxies of human activity, especially but not limited to models of population change over the Pleistocene and/or Holocene. While very much agreeing with the need for this increased attention, we emphasize three important issues that can all be thought of as modifiable reporting unit problems: the impact of (i) archaeological periodization, (ii) uneven event durations and (iii) geographical nucleation-dispersal phenomena.
Drawing inspiration from real-world examples from prehistoric Britain, Greece and Japan, we explore their consequences and possible mitigation via a reproducible set of tactical simulations.

This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography.

Bevan, A., Crema, E.R. 2021. Modifiable reporting unit problems and time series of long-term human activity. Philosophical Transactions of the Royal Society B. 376 :20190726   [URL] [Data and R Scripts] [pdf]

A multi-proxy inference of Jōmon population dynamics using bayesian phase models, residential data, and summed probability distribution of 14C dates

We introduce a new workflow for analysing archaeological frequency data associated with relative rather than absolute chronological time-stamps. Our approach takes into account multiple sources of uncertainty by combining Bayesian chronological models and Monte-Carlo simulation to sample possible calendar dates for each archaeological entity. We argue that when applied to settlement data, this combination of methods can bring new life to demographic proxies that are currently under-used due to their lack of chronological accuracy and pre­cision, and provide grounds for further exploring the limits and the potential of the so-called “dates as data” approach based on the temporal frequency of radiocarbon dates. Here we employ this new workflow by re-examining a legacy dataset that has been used to describe a major population rise-and-fall that occurred in central Japan during the Jomon period (16,000–2,800 cal BP), focusing on the temporal window between 8,000 and 3,000 cal BP. To achieve this goal we: 1) construct the first Bayesian model of forty-two Jomon ceramic typology based cultural phases using a sample of 2,120 radiocarbon dates; 2) apply the proposed workflow on a dataset of 9,612 Jomon pit-dwellings; and 3) compare the output to a Summed Probability Distribution (SPD) of 1,550 radiocarbon dates from the same region. Our results provide new estimates on the timing of major de­mographic fluctuations during the Jomon period and reveal a generally good correlation between the two proxies, although with some notable discrepancies potentially related to changes in settlement pattern.

Crema, E.R., Kobayashi, K., 2020. A multi-proxy inference of Jōmon population dynamics using bayesian phase models, residential data, and summed probability distribution of 14C dates. Journal of Archaeological Science 117, 105136. [URL] [Data and R Scripts] [Request a Copy

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Inference from large sets of radiocarbon dates: software and methods

The last decade has seen the development of a range of new statistical and computational techniques for analysing large collections of radiocarbon ( 14 C) dates, often but not exclusively to make inferences about human population change in the past. Here we introduce rcarbon, an open-source software package for the R statistical computing language which implements many of these techniques and looks to foster transparent future study of their strengths and weaknesses. In this paper, we review the key assumptions, limitations and potentials behind statistical analyses of summed probability distribution of 14 C dates, including Monte-Carlo simulation-based tests, permutation tests, and spatial analyses. Supplementary material provides a fully reproducible analysis with further details not covered in the main paper.

Crema, E.R., Bevan, A., 2020. Inference from large sets of radiocarbon dates. Radiocarbon. [URL] [Data and R Scripts] [pdf]