Evidence map›Paper›PMID 36078413›Full record

ArticleInternational journal of environmental research and public health2022

Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China.

Xinxin Guo, Benyong Wei, Gaozhong Nie, Guiwu Su

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Xinxin GuoInstitute of Geology, China Earthquake Administration, Beijing 100029, China.
Benyong WeiInstitute of Geology, China Earthquake Administration, Beijing 100029, China.ORCID 0000-0003-3510-278X
Gaozhong NieInstitute of Geology, China Earthquake Administration, Beijing 100029, China.
Guiwu SuInstitute of Geology, China Earthquake Administration, Beijing 100029, China.
Institute of Geology, China Earthquake Administration

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Seismic disasters are sudden and unpredictable, often causing massive damage, casualties and socioeconomic losses. Rapid and accurate determination of the scale and degree of destruction of the seismic influence field in an affected area can aid in timely emergency rescue work after an earthquake. In this study, the relationship between the changes in four types of mobile signaling data and the seismic influence field was explored in the 2017 Jiuzhaigou earthquake-hit area, China, by using the methods of comparative analysis, regression analysis and spatial autocorrelation analysis. The results revealed that after the earthquake, the number of mobile signaling significantly decreased. The higher the intensity, the more obvious the reduction of mobile signaling data and the later the recovery time. The Loginmac and WiFi data showed greater sensitivity than Gid and Station. There was a significant correlation between the changes in the mobile signaling numbers and the seismic intensity, which can more accurately reflect the approximate extent of the seismic influence field and the degree of actual damage. The changes in mobile signaling can provide a helpful reference for the rapid determination of seismic influence fields.

Indexed as

DisastersEarthquakesChinaRescue WorkJiuzhaigou earthquakemobile signalingrapid assessmentseismic influence field

Identifiers

PMID36078413
PMCPMC9518317
OpenAlexW4293537597

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.