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

Joannes-Boyau R, Bodin T, Scheffers A, Sambridge M, May SM - Sci Rep (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Southern Cross GeoScience, Southern Cross University, Lismore, NSW, 2480, Australia.

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.

No MeSH data available.


Related in: MedlinePlus

Map of the world TC tracks from International Best Track Archive for Climate Stewardship (IBTrACS).(top) from 1931 to present days; (bottom) from 1841 to 1930; TC records prior to 1931 were based on only a single position estimate per day, while at the same time many parts of the globe were poorly sampled (Jarvinen et al., 1984). There are no data available prior to 1930 for the western Pacific Ocean along the North and Central American coast, while this region is prone to TC activities, especially due to the direct influence of El Niño/La Niña; (Figure made with ArcGIS® software and Corel Draw X5).
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f1: Map of the world TC tracks from International Best Track Archive for Climate Stewardship (IBTrACS).(top) from 1931 to present days; (bottom) from 1841 to 1930; TC records prior to 1931 were based on only a single position estimate per day, while at the same time many parts of the globe were poorly sampled (Jarvinen et al., 1984). There are no data available prior to 1930 for the western Pacific Ocean along the North and Central American coast, while this region is prone to TC activities, especially due to the direct influence of El Niño/La Niña; (Figure made with ArcGIS® software and Corel Draw X5).

Mentions: In this study, we test the validity of BL (1) on a natural climatic process. For this purpose we have chosen the traveled distance of tropical cyclones (TC) (Fig. 1), a large dataset available freely online via the International Best Track Archive for Climate Stewardship (IBTrACS).


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

Joannes-Boyau R, Bodin T, Scheffers A, Sambridge M, May SM - Sci Rep (2015)

Map of the world TC tracks from International Best Track Archive for Climate Stewardship (IBTrACS).(top) from 1931 to present days; (bottom) from 1841 to 1930; TC records prior to 1931 were based on only a single position estimate per day, while at the same time many parts of the globe were poorly sampled (Jarvinen et al., 1984). There are no data available prior to 1930 for the western Pacific Ocean along the North and Central American coast, while this region is prone to TC activities, especially due to the direct influence of El Niño/La Niña; (Figure made with ArcGIS® software and Corel Draw X5).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496784&req=5

f1: Map of the world TC tracks from International Best Track Archive for Climate Stewardship (IBTrACS).(top) from 1931 to present days; (bottom) from 1841 to 1930; TC records prior to 1931 were based on only a single position estimate per day, while at the same time many parts of the globe were poorly sampled (Jarvinen et al., 1984). There are no data available prior to 1930 for the western Pacific Ocean along the North and Central American coast, while this region is prone to TC activities, especially due to the direct influence of El Niño/La Niña; (Figure made with ArcGIS® software and Corel Draw X5).
Mentions: In this study, we test the validity of BL (1) on a natural climatic process. For this purpose we have chosen the traveled distance of tropical cyclones (TC) (Fig. 1), a large dataset available freely online via the International Best Track Archive for Climate Stewardship (IBTrACS).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Southern Cross GeoScience, Southern Cross University, Lismore, NSW, 2480, Australia.

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.

No MeSH data available.


Related in: MedlinePlus