Evidence map›Paper›PMID 36721494›Full record

ArticleArchitectural intelligence2023

The evaluation of urban spatial quality and utility trade-offs for Post-COVID working preferences: a case study of Hong Kong.

Qiwei Song, Zhiyi Dou, Waishan Qiu, Wenjing Li, Jingsong Wang, Jeroen van Ameijde, Dan Luo

Abstract read
In one paragraph

Article in Architectural intelligence, 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. 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

7 authors.

Qiwei SongArcadis IBI Group, Toronto, Canada.
Zhiyi DouSchool of Geographic Information Science, University of Queensland, St.Lucia, Australia.
Waishan QiuDepartment of City and Regional Planning, Cornell University, Ithaca, USA.
Wenjing LiCenter for Spatial Information Science, The University of Tokyo, Tokyo, Japan.
Jingsong WangSchool of Architecture, Tsinghua University, Beijing, China.
Jeroen van AmeijdeSchool of Architecture, The Chinese University of Hong Kong, Hong Kong SAR, China.
Dan LuoSchool of Architecture, University of Queensland, St.Lucia, Australia.ORCID 0000-0003-1760-0451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The formation of urban districts and the appeal of densely populated areas reflect a spatial equilibrium in which workers migrate to locations with greater urban vitality but diminished environmental qualities. However, the pandemic and associated health concerns have accelerated remote and hybrid work modes, altered people's sense of place and appreciation of urban density, and transformed perceptions of desirable places to live and work. This study presents a systematic method for evaluating the trade-offs between perceived urban environmental qualities and urban amenities by analysing post-pandemic urban residence preferences. By evaluating neighbourhood Street View Imagery (SVI) and urban amenity data, such as park sizes, the study collects subjective opinions from surveys on two working conditions (work-from-office or from-home). On this basis, several Machine Learning (ML) models were trained to predict the preference scores for both work modes. In light of the complexity of work-from-home preferences, the results demonstrate that the method predicts work-from-office scores with greater precision. In the post-pandemic era, the research aims to shed light on the development of a valuable instrument for driving and evaluating urban design strategies based on the potential self-organisation of work-life patterns and social profiles in designated neighbourhoods.

Indexed as

Convolutional neural networkDeep learningPost-pandemicSustainable communitiesTrade-offUrban data

Identifiers

PMID36721494
PMCPMC9880930

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.