Evidence map›Paper›PMID 36713474›Full record

ArticleApplied geography (Sevenoaks, England)2023

Commercial dynamics in urban China during the COVID-19 recession: Vulnerability and short-term adaptation of commercial centers in Shanghai.

Lei Zhou, Weiye Xiao, Zhenlong Zheng, Haiping Zhang

Abstract read
In one paragraph

Article in Applied geography (Sevenoaks, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. COVID-19, Cities and Inequality.Applied geography (Sevenoaks, England) · 2023
    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.

Lei ZhouSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
Weiye XiaoNanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 214000, China.
Zhenlong ZhengSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
Haiping ZhangSchool of Geographic Science, Nanjing Normal University, Nanjing, 210023, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studying the commercial dynamics during the COVID-19 recession could help deepen our understanding of how the pandemic damages the commercial economy and how to against the pandemic. This study aims to explore the vulnerability and adaptation of commercial centers using a weekly consumption data of UnionPay cards in Shanghai. A vulnerability index and multiscale geographically weighted regressions (MGWR) are employed. Our results suggest that retail, leisure, and entertainment sectors are less vulnerable to the pandemic at the early stage, when catering, life service, and wholesale sectors are more influenced. Catering, life service, and wholesale sectors were better adapted to the second wave of the pandemic, while the retail and entertainment sectors were even more vulnerable. Further analysis using MGWR models suggests that the commercial centers with higher consumption volume are better adapted to the shock. The diversity of commercial sectors mainly reduces low-level commercial centers' vulnerability to the pandemic. The commercial centers targeting high-end consumers with wider hinterland were less adapted to the pandemic. These research outcomes reveal the disparities in commercial centers' vulnerability against COVID-19 and highlight adaptation's role during the pandemic.

Indexed as

COVID-19 pandemicMGWR modelShort-term adaptationUnionpay card dataUrban commercial centerVulnerability

Identifiers

PMID36713474
PMCPMC9860259

What OpenQuestion holds

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Registered trials

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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.