| Title |
An analytical framework to nowcast well-being using mobile phone data
|
|---|---|
| Published in |
arXiv, June 2016
|
| DOI | 10.1007/s41060-016-0013-2 |
| Authors |
Luca Pappalardo, Maarten Vanhoof, Lorenzo Gabrielli, Zbigniew Smoreda, Dino Pedreschi, Fosca Giannotti |
X Demographics
The data shown below were collected from the profiles of 30 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 4 | 13% |
| Switzerland | 1 | 3% |
| Netherlands | 1 | 3% |
| Belgium | 1 | 3% |
| Denmark | 1 | 3% |
| India | 1 | 3% |
| Ireland | 1 | 3% |
| Australia | 1 | 3% |
| United Kingdom | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 17 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 22 | 73% |
| Scientists | 7 | 23% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Sri Lanka | 1 | <1% |
| Italy | 1 | <1% |
| Switzerland | 1 | <1% |
| Unknown | 106 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 25% |
| Researcher | 19 | 17% |
| Student > Master | 12 | 11% |
| Student > Doctoral Student | 6 | 5% |
| Professor > Associate Professor | 4 | 4% |
| Other | 12 | 11% |
| Unknown | 29 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 27 | 25% |
| Engineering | 11 | 10% |
| Social Sciences | 10 | 9% |
| Physics and Astronomy | 6 | 5% |
| Earth and Planetary Sciences | 6 | 5% |
| Other | 16 | 15% |
| Unknown | 34 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 27 June 2017.
All research outputs
#1,950,719
of 24,635,922 outputs
Outputs from arXiv
#31,710
of 996,553 outputs
Outputs of similar age
#35,100
of 359,311 outputs
Outputs of similar age from arXiv
#457
of 13,732 outputs
Altmetric has tracked 24,635,922 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 996,553 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 96% 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 359,311 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 90% of its contemporaries.
We're also able to compare this research output to 13,732 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 96% of its contemporaries.