Project Outputs
[8] Crema, E.R., Bloxam, A., Stevens, C., Vander-Linden, M. (2024). Modelling diffusion of innovation curves using radiocarbon data. Journal of Archaeological Science, 165, 105962. [URL][Data and R Scripts][pdf]
[7] Kudo, Y., Sakamoto, M., Hakozaki, M., Stevens, C.J., Crema, E.R. (2023). An Archaeological Radiocarbon Database of Japan. Journal of Open Archaeology Data. 11(0), p.11 [URL][Data and R Scripts] [pdf].
[6] Crema, E. R., Stevens, C. & Shoda, S. (2022). Bayesian analyses of direct radiocarbon dates reveal geographic variations in the rate of rice farming dispersal in prehistoric Japan. Science Advances, 8(38), https://doi.org/10.1126/sciadv.adc9171 [URL] [Data and R Scripts] [pdf]
[5] Stevens, C. J., Crema, E. R., & Shoda, S. (2022). The importance of wild resources as a reflection of the resilience and changing nature of early agricultural systems in East Asia and Europe. Frontiers in Ecology and Evolution, 10 https://doi.org/10.3389/fevo.2022.1017909 [URL] [Data and R Scripts] [pdf]
[4] Crema, E.R., Bevan, A., 2021. Inference from large sets of radiocarbon dates: Software and Methods, Radiocarbon. 63(1), 23-39 [URL] [Data and R Scripts] [pdf]
[3] 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]
[2] Crema, E. R., & Shoda, S. (2021). A Bayesian approach for fitting and comparing demographic growth models of radiocarbon dates: A case study on the Jomon-Yayoi transition in Kyushu (Japan). PLOS ONE, 16(5), e0251695. https://doi.org/10.1371/journal.pone.0251695
[1] 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] [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.