Evidence map›Paper›PMID 40721444›Full record

ArticleScientific reports2025

Predicting geriatric environmental safety perception assessment using LightGBM and SHAP framework.

Shengzhen Wu, Sichao Wu, Jingru Chen, Chen Pan

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

4 authors.

Shengzhen WuCollege of Arts and Design, Jimei University, Xiamen, 361000, China.
Sichao WuXiamen Academy of Arts and Design, FuZhou University, Xiamen, 361000, China. wusichao@fzu.edu.cn.
Jingru ChenCollege of Arts and Design, Jimei University, Xiamen, 361000, China.
Chen PanArchitecture and Civil Engineering Institute, Guangdong University of Petrochemical Technology, Maoming, 525000, China.

Funding

2024 Fujian Provincial education System Philosophy and Social Science Research Project JAS24049Research Project of Higher Education Science Research Laboratory of Fujian Higher Education Association in 2024 24FJSYZD038
6 · The paper itself

Abstract

Global population aging highlights the need to understand how the elderly perceive safety in urban public spaces. This study used image semantic segmentation to identify key visual elements from panoramic images. A dataset was created by combining manual scoring with deep learning to explore how pocket park environments impact older adults' safety perceptions. Analyzing 497 images from 29 pocket parks in Xiamen Island with LightGBM and SHAP tools, researchers identified visual elements that significantly affect seniors' safety perceptions. The findings indicate: (1) Elderly environmental safety perceptions in the 29 surveyed parks on Xiamen Island were generally positive, yet safety scores varied markedly across parks. (2) Pedestrian area, car, wall, person, billboard, parterre, and vegetation were identified as the seven visual elements most impactful on elderly environmental safety perceptions. (3) Interactions among visual elements were observed, with vegetation exerting a notably regulatory effect on environmental safety perceptions, significantly enhancing the elderly's perception of security. This study's empirical analysis elucidates the influence of visual elements in pocket parks on elderly environmental safety perceptions, offering practical guidance for park planners to design more inclusive and secure green spaces for the elderly, with broad application potential.

Indexed as

PerceptionSafetyAgedAged, 80 and overBoosting Machine Learning AlgorithmsFemaleHumansMaleParks, RecreationalElderly environmental safety perceptionMachine learningPanoramic imageryPocket parksSHAPUrban planning

Identifiers

PMID40721444
PMCPMC12304219

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.