Evidence map›Paper›PMID 41420239›Full record

ArticleInternational journal of health geographics2025

Examining streetscape visuals and emotional responses through social media and street view image analysis.

Xueyan Yang, Jie Shen

Abstract read
In one paragraph

Article in International journal of health geographics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Xueyan YangCollege of Architecture and Urban Planning, Tongji University, Shanghai, 200092, China.
Jie ShenCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China. jshen@zju.edu.cn.

Funding

Center for Balance Architecture, Zhejiang University K-20212936
6 · The paper itself

Abstract

backgroundStreets are important spaces for everyday activities, and the street environment can impact quality of life. This study investigates the impact of streetscape visuals on public emotions in the Beishan Street and Nanshan Road historic districts surrounding West Lake in Hangzhou.

methodsEmotional perceptions were analyzed through a textual study of relevant posts on Sina Weibo, categorizing them into positive or negative sentiments. Simultaneously, Baidu Map Street View Images (SVIs) of the area were processed using the DeepLabV3 + Network model to semantically segment them into 19 elements, followed by the calculation of seven visual space indicators.

resultsCorrelation analysis between these street view elements, visual space indicators, and the emotional content of the posts revealed that multiple factors within the SVIs are significantly associated with public emotions. For instance, elements such as Rider and visual space indicators like Enclosure are positively correlated with emotions, while elements like Traffic Light and visual space indicators such as Openness show negative correlations. These emotional impacts vary depending on the specific building types present in the streetscape.

conclusionsThe findings underscore the association between streetscape visuals and public emotions and demonstrate that social media platforms provide substantial data for studying these effects. This research offers valuable insights for urban planners and managers to understand resident and tourist preferences, providing an objective foundation for enhancing urban streetscape quality.

Indexed as

EmotionsImage Processing, Computer-AssistedSocial MediaChinaHumansHistoric districtSemantic segmentationSina weibo microblogStreet view image (SVI)Visual space indicator

Identifiers

PMID41420239
PMCPMC12927252

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.