Evidence map›Paper›PMID 35719244›Full record

ArticleComputers, environment and urban systems2022

Structural changes in intercity mobility networks of China during the COVID-19 outbreak: A weighted stochastic block modeling analysis.

Wenjia Zhang, Zhaoya Gong, Caicheng Niu, Pu Zhao, Qiwei Ma, Pengjun Zhao

Abstract read
In one paragraph

Article in Computers, environment and urban systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 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

6 authors.

Wenjia ZhangSchool of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China.
Zhaoya GongSchool of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China.
Caicheng NiuSchool of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China.
Pu ZhaoSchool of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China.
Qiwei MaSchool of Architecture, Tsinghua University, Beijing, China.
Pengjun ZhaoSchool of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study focuses on a mesoscale perspective to examine the structural and spatial changes in the intercity mobility networks of China from three phases of before, during and after the Wuhan lockdown due to the outbreak of COVID-19. Taking advantages of mobility big data from Baidu Maps, we introduce the weighted stochastic block model (WSBM) to measure and compare mesoscale structures in the three mobility networks. The results reveal significant changes to volume and structure of the intercity mobility networks. Particularly, WSBM results show that the intercity network transformed from a typical core-periphery structure in the normal phase, to a hybrid and asymmetric structure with mixing core-peripheries and local communities in the lockdown phase, and to a multi-community structure with nested core-peripheries during the post-lockdown phase. These changes suggest that the outbreak of COVID-19 and the travel restrictions deconstructed the original hierarchy of the intercity mobility network in China, making the network more locally or regionally fragmented, even at the recovery stage. This study provides new empirical and methodological insights into understanding mobility network dynamics under the impact of COVID-19, helping assess the emergency-induced impact as well as the recovery process of the mobility network.

Indexed as

ChinaCOVID-19Intercity mobility networkNetwork structureWeighted stochastic block model (WSBM)

Identifiers

PMID35719244
PMCPMC9194079

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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.