Evidence map›Paper›PMID 37077238›Full record

ArticleApplied geography (Sevenoaks, England)2023

Exploring non-linear built environment effects on urban vibrancy under COVID-19: The case of Hong Kong.

Longzhu Xiao, Jixiang Liu

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Longzhu XiaoDepartment of Urban Planning, Xiamen University, China.
Jixiang LiuDepartment of Urban Planning, Xiamen University, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease (COVID-19) pandemic has enormously changed the way people perceive and use urban spaces, exacerbating some pre-existing issues including urban vibrancy decline. This study aims to explore built environment effects on urban vibrancy under COVID-19, which will help recalibrate planning models and design principles. Based on multi-source geo-tagged big data of Hong Kong, this study reveals variations in urban vibrancy and employs machine learning modeling and interpretation methods to examine built environment effects on urban vibrancy before, during, and after the outbreak of COVID-19, with review volume of restaurants & food retailers as the indicator for urban vibrancy and built environment depicted from five dimensions (i.e., building form, street accessibility, public transport accessibility, functional density, and functional mixture). We found that (1) urban vibrancy concussively decreased during the outbreak and slowly recovered afterwards; (2) built environment's capability to stimulate urban vibrancy was weakened during the outbreak and restored afterwards; (3) the relationships between built environment and urban vibrancy were non-linear and moderated by the pandemic. This research enriches our understandings of the role of the pandemic in influencing urban vibrancy and its correlation with built environment, enlightening decision makers with nuanced criteria for pandemic-adaptive urban planning and design.

Identifiers

PMID37077238
PMCPMC10099149

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.