Evidence map›Paper›PMID 35095162›Full record

ArticleCities (London, England)2022

Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen.

Long Zhou, Sihong Li, Chaosu Li, Guoqiang Shen, Huajie Yang, Pengyu Zhu, Haoying Han, Bin Li

Abstract readCase Reports
In one paragraph

Article in Cities (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. 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

8 authors.

Long ZhouFaculty of Innovation and Design, City University of Macau, Macao.
Sihong LiFaculty of Innovation and Design, City University of Macau, Macao.
Chaosu LiUrban Governance and Design Thrust, Hong Kong University of Science and Technology, China.
Guoqiang ShenCollege of Civil Engineering and Architecture, Zhejiang University, China.
Huajie YangFaculty of Innovation and Design, City University of Macau, Macao.
Pengyu ZhuDivision of Public Policy, Hong Kong University of Science and Technology, Hong Kong.
Haoying HanCollege of Civil Engineering and Architecture, Zhejiang University, China.
Bin LiFaculty of Innovation and Design, City University of Macau, Macao.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Countries worldwide are reopening their businesses despite the continuing COVID-19 crisis and the emergence of new variants. In this context, knowing whether the reopening of businesses at various locations exposes higher risk to the public is essential. Whether urban density correlates with the potential infection risk as concluded by previous studies of the COVID-19 pandemic remains unknown. In this study, taking the Macau Peninsula as a testbed, we first identified business locations for daily activities according to the latest point of interest (POI) data and generated the potential risk surface for COVID-19 infection. Then, using the cellular phone network and urban footprint data, we further analyzed the spatial relationship between COVID-19 potential risk and urban density of population and morphology through visual analytics. Results show that while some degree of spatial congruency exists between medium-risk peaks and urban density hotspots, apparent spatial mismatch exists for high-risk peaks, indicating that the traditional planning control based on urban density is inadequate for mitigating public health risks. POI-based spatial layout and configuration better reflecting business services and associated human activities are recommended in future planning and policy-making for more resilient cities in the post-pandemic era.

Indexed as

Business reopenCOVID-19 riskDaily activitiesPoint of interestsSpatial relationshipUrban density

Identifiers

PMID35095162
PMCPMC8786608

What OpenQuestion holds

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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.