Evidence map›Paper›PMID 40113978›Full record

ArticleScientific reports2025

Illuminating the path to more equitable access to urban parks.

Kaihan Zhang, Wen-Long Shang, Jonas De Vos, Yuerong Zhang, Mengqiu Cao

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Kaihan ZhangBartlett School of Planning, University College London, London, UK.ORCID http://orcid.org/0009-0007-1502-571X
Wen-Long ShangCentre for Transport Studies, Imperial College London, London, UK.
Jonas De VosBartlett School of Planning, University College London, London, UK.ORCID http://orcid.org/0000-0003-4919-2157
Yuerong ZhangBartlett School of Environment, Energy and Resources, University College London, London, UK.ORCID http://orcid.org/0000-0001-7956-8640
Mengqiu CaoBartlett School of Environment, Energy and Resources, University College London, London, UK. mengqiu.cao@ucl.ac.uk.ORCID http://orcid.org/0000-0001-8670-4735

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parks are a crucial element of urban landscapes. Understanding the role of the built environment in terms of park accessibility is vital for promoting travel equity and shaping public policies to improve access to urban parks. This study employs a geospatial artificial intelligence (GeoAI) approach to examine the impact of the built environment on park accessibility at both macro and micro levels. We find that accessibility to urban parks varies significantly across different travel modes, with walking being the least equitable due to its most clustered distribution pattern. While the macro-level built environment provides substantial explanatory power for accessibility across travel modes, our GeoAI-based analysis offers a more nuanced understanding of streetscape qualities, by closely reflecting a more realistic human perception of the built environment. These qualities - street network interconnectivity, safety, openness, spaciousness, greenery, and distinctive signage - significantly correlate with enhanced park accessibility across all travel modes, thereby complementing macro-level factors. Ultimately, this study suggests that placing emphasis on multimodal travel can enhance travel equity, thus providing the relevant authorities and policymakers with cost-effective strategies for improving park accessibility.

Indexed as

GeoAIGreen justiceMultimodal travelStreetscape qualityUrban park accessibility

Identifiers

PMID40113978
PMCPMC11926098

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.