Evidence map›Paper›PMID 35002051›Full record

ArticlePhysica A2022

Spatiotemporal evolving patterns of bike-share mobility networks and their associations with land-use conditions before and after the COVID-19 outbreak.

Jie Song, Liye Zhang, Zheng Qin, Muhamad Azfar Ramli

Abstract read
In one paragraph

Article in Physica A, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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. Article
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  7. Short- and long-term effects of COVID-19 on bicycle sharing usage.Transportation research interdisciplinary perspectives · 2022
    Article
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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

4 authors.

Jie SongInstitute of High Performance Computing, Agency for Science, Technology, and Research, 138632, Singapore.
Liye ZhangCollege of Transportation, Shandong University of Science and Technology, Qingdao, Shandong, 266590, China.
Zheng QinInstitute of High Performance Computing, Agency for Science, Technology, and Research, 138632, Singapore.
Muhamad Azfar RamliInstitute of High Performance Computing, Agency for Science, Technology, and Research, 138632, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent months have seen ever-increasing levels of confirmed COVID-19 cases despite the accelerated adoption of vaccines. In the wake of the pandemic, travel patterns of individuals change as well. Understanding the changes in biking behaviors during evolving COVID-19 situations is a primary goal of this paper. It investigated usage patterns of the bike-share system in Singapore before, during, and after local authorities imposed lockdown measures. It also correlated the centrality attributes of biking mobility networks of different timestamps with land-use conditions. The results show that total ridership surprisingly climbed by 150% during the lockdown, compared with the pre-pandemic level. Biking mobility graphs became more locally clustered and polycentric as the epidemic develop. There existed a positive and sustained spatial autocorrelation between centrality measures and regions with high residential densities or levels of the land-use mixture. This study suggests that bike-share systems may serve as an alternative mode to fulfill mobility needs when public transit services are restricted due to lockdown policies. Shared-micromobility services have the potential to facilitate a disease-resilient transport system as societies may have to coexist with COVID in the future.

Indexed as

Dockless bike-share systemSARS-CoV-2Spatial couplingSpatiotemporal

Identifiers

PMID35002051
PMCPMC8719369

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.