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Crowdsourced earthquake early warning

Overview of attention for article published in Science Advances, April 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
32 news outlets
blogs
10 blogs
twitter
88 tweeters
facebook
9 Facebook pages
googleplus
5 Google+ users

Readers on

mendeley
134 Mendeley
citeulike
1 CiteULike
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Title
Crowdsourced earthquake early warning
Published in
Science Advances, April 2015
DOI 10.1126/sciadv.1500036
Pubmed ID
Authors

Sarah E. Minson, Benjamin A. Brooks, Craig L. Glennie, Jessica R. Murray, John O. Langbein, Susan E. Owen, Thomas H. Heaton, Robert A. Iannucci, Darren L. Hauser

Abstract

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California's Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Twitter Demographics

The data shown below were collected from the profiles of 88 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Spain 1 <1%
Israel 1 <1%
Greece 1 <1%
Brazil 1 <1%
Unknown 128 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 22%
Researcher 28 21%
Student > Master 21 16%
Student > Bachelor 11 8%
Student > Doctoral Student 9 7%
Other 17 13%
Unknown 18 13%
Readers by discipline Count As %
Earth and Planetary Sciences 43 32%
Engineering 21 16%
Computer Science 20 15%
Environmental Science 7 5%
Social Sciences 4 3%
Other 14 10%
Unknown 25 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 407. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 September 2020.
All research outputs
#47,178
of 20,029,941 outputs
Outputs from Science Advances
#497
of 7,731 outputs
Outputs of similar age
#565
of 238,578 outputs
Outputs of similar age from Science Advances
#6
of 52 outputs
Altmetric has tracked 20,029,941 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,731 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 121.2. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 238,578 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.