Evidence map›Paper›PMID 35317198›Full record

ArticleTransportation research. Part A, Policy and practice2022

Analyzing COVID-19's impact on the travel mobility of various social groups in China's Greater Bay Area via mobile phone big data.

Yu Pan, Sylvia Y He

Abstract read
In one paragraph

Article in Transportation research. Part A, Policy and practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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

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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

2 authors.

Yu PanDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
Sylvia Y HeDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 outbreak has significantly impacted people's mobility in terms of travel, which is directly related to regional economic vitality and individuals' well-being. This study conducted research on the COVID-19 epidemic's impact on travel mobility in China's Greater Bay Area, utilizing mobile phone big data. The overall influence of COVID-19 was measured by investigating the impact between different income and migration groups in three core cities: Shenzhen, Guangzhou, and Foshan. Individuals' weekly travel frequency and activity space area between December 2019 and May 2020 were calculated, and the average values between the different cities and various social groups were compared. The results showed that travel mobility declined during the epidemic's peak, followed by a recovery based on the overall trend. The start and end of strict law enforcement had a significant impact on the initial decline and subsequent recovery of travel mobility in the core cities. COVID-19 had a larger impact on core cities than peripheral areas, and on non-commute travel frequency, compared to commute travel frequency. Compared to advantaged groups, socially disadvantaged groups experienced a steeper decline in travel mobility during the epidemic's peak, but a more significant recovery afterwards. These findings indicate that discretionary activities have not yet recovered and remain below the pre-epidemic level, and that disadvantaged social groups had limited access to superior precautionary measures for avoiding infection. Based on the findings, we provide several policy suggestions regarding the recovery of travel mobility.

Indexed as

Activity spaceCOVID-19Greater Bay Area (GBA)Mobile phone dataSocial inequityTravel frequency

Identifiers

PMID35317198
PMCPMC8929529

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.