Evidence map›Paper›PMID 36474961›Full record

ArticleAnnals of tourism research2023

Changes in tourist mobility after COVID-19 outbreaks.

Ling Yu, Pengjun Zhao, Junqing Tang, Liang Pang

Abstract read
In one paragraph

Article in Annals of tourism research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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.

Ling YuSchool of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
Pengjun ZhaoSchool of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
Junqing TangSchool of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
Liang PangSchool of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We comparatively examined tourist mobility changes in the entire country and explicitly covered two distinct waves of COVID-19 outbreaks, based on mobile phone data from 277.15 million tourists from 2019 to 2021 in China. The results show that domestic tourism in Beijing was even higher after the pandemic than prior to it. In addition, we found that female and elderly groups had a slower recovery after the first wave, whereas this was the opposite one year later, after the second wave. Additionally, wealthier, larger cities were notably hit the hardest. Overall, our findings provide a better understanding of tourism management in public health crises and policy-making during post-pandemic recovery and for future outbreaks.

Indexed as

Big dataChinaHuman travel behaviorThe COVID-19 pandemicTourist mobility

Identifiers

PMID36474961
PMCPMC9715491

What OpenQuestion holds

Textmetadata
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.