Evidence map›Paper›PMID 40630416›Full record

ArticleFrontiers in public health2025

Dynamics of street environmental features and emotional responses in urban areas: implications for public health and sustainable development.

Yangfei Huang, Chenjian Zhong, Tangtao He, Yuyang Jiang

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. 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

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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

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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

4 authors.

Yangfei HuangSchool of Civil Engineering & Architecture, Zhejiang University of Science & Technology, Hangzhou, China.
Chenjian ZhongSchool of Civil Engineering & Architecture, Zhejiang University of Science & Technology, Hangzhou, China.
Tangtao HeCollege of Architecture and Urban Planning, Guangzhou University, Guangzhou, China.
Yuyang JiangSchool of Geography and Planning, Cardiff University, Cardiff, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduce: Urban street spatial quality, as an intervenable environmental factor from the perspective of public health, significantly affects residents' mental health and emotional wellbeing. Accurately identifying emotional hot spots in urban environment and exploring the mechanism of environmental features affecting emotions are crucial for improving residents' mental health level, promoting healthy urban planning and creating a sustainable urban environment. Methods: This study employed an interdisciplinary approach, utilizing street view images from Liwan District, Guangzhou, China. A Pyramid Scene Parsing Network (PSPNet) was applied to quantify 18 key environmental features, including the Green View Index (GVI), Space Openness (SO), Enclosure Index (EI), etc. By integrating an emotion dataset assessed by 40 experts, a random forest model was constructed to predict emotional responses to different street spaces. Emotional distribution maps were generated using ArcGIS Pro to identify emotional hotspots. Subsequently, SHAP (SHapley Additive exPlanations) analysis was conducted to explore how environmental features influence emotional responses. Results: The analysis revealed the following: (1) Positive emotions were significantly associated with areas of well-vegetated, while negative emotions were predominantly concentrated in industrial zones and narrow alleys. (2) GVI, sky-green ratio, EI, and SO had a notable impact on emotional responses. (3) The optimal range for the GVI (0.27-0.3) was found to maximize positive emotional valence. Beyond this range, further increases in the GVI did not result in significant emotional changes. Discussion: This study demonstrates the feasibility of predicting public emotional responses from street view images using machine learning. Optimizing green spaces and improving pedestrian environments can promote emotional health. To effectively balance the distribution of urban green spaces and maximize public health benefits, it is recommended that governments collaborate with communities, leveraging fiscal incentives and green infrastructure investments to promote equitable and sustainable development of green spaces. These findings play a crucial role in advancing both public health and environmental sustainability.

Indexed as

EmotionsEnvironment DesignPublic HealthSustainable DevelopmentUrban PopulationChinaCitiesCity PlanningHumansMental Healthemotional responsesgreen spaceGreen View Indexhealth planningmachine learningpublic healthstreet viewsustainable development

Identifiers

PMID40630416
PMCPMC12236232

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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.