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Joannes-Boyau R, Bodin T, Scheffers A, et al. Using Benford's law to investigate Natural Hazard dataset homogeneity. Sci Rep. 2015;5:12046doi: 10.1038/srep12046.
Joannes-Boyau, R., Bodin, T., Scheffers, A., Sambridge, M., & May, S. M. (2015). Using Benford's law to investigate Natural Hazard dataset homogeneity. Scientific reports, 512046. https://doi.org/10.1038/srep12046
Joannes-Boyau, Renaud, et al. "Using Benford's law to investigate Natural Hazard dataset homogeneity." Scientific reports vol. 5 (2015): 12046. doi: https://doi.org/10.1038/srep12046
Joannes-Boyau R, Bodin T, Scheffers A, Sambridge M, May SM. Using Benford's law to investigate Natural Hazard dataset homogeneity. Sci Rep. 2015 Jul 09;5:12046. doi: 10.1038/srep12046. PMID: 26156060; PMCID: PMC4496784.
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Sci Rep. 2015 Jul 09;5:12046. doi: 10.1038/srep12046.

Using Benford's law to investigate Natural Hazard dataset homogeneity.

Scientific reports

Renaud Joannes-Boyau, Thomas Bodin, Anja Scheffers, Malcolm Sambridge, Simon Matthias May

Affiliations

  1. Southern Cross GeoScience, Southern Cross University, Lismore, NSW, 2480, Australia.
  2. 1] Earth and Planetary Science, University of California Berkeley, CA, USA [2] Laboratoire de Géologie de Lyon, Ecole Normale Supérieure de Lyon, Université de Lyon-1, CNRS, 69364 Lyon Cedex 07, France.
  3. Research School of Earth Sciences, Australian National University, Canberra, ACT, 0200, Australia.
  4. Institute of Geography, University of Cologne, 50923, Cologne, Germany.

PMID: 26156060 PMCID: PMC4496784 DOI: 10.1038/srep12046

Abstract

Working with a large temporal dataset spanning several decades often represents a challenging task, especially when the record is heterogeneous and incomplete. The use of statistical laws could potentially overcome these problems. Here we apply Benford's Law (also called the "First-Digit Law") to the traveled distances of tropical cyclones since 1842. The record of tropical cyclones has been extensively impacted by improvements in detection capabilities over the past decades. We have found that, while the first-digit distribution for the entire record follows Benford's Law prediction, specific changes such as satellite detection have had serious impacts on the dataset. The least-square misfit measure is used as a proxy to observe temporal variations, allowing us to assess data quality and homogeneity over the entire record, and at the same time over specific periods. Such information is crucial when running climatic models and Benford's Law could potentially be used to overcome and correct for data heterogeneity and/or to select the most appropriate part of the record for detailed studies.

References

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