An Eye-Tracking Study on Exploring Children’s Visual Attention to Streetscape Elements
Bibliographic record
Abstract
Urban street spaces play a crucial role in children’s daily commuting and social activities. Therefore, the design of these spaces must give more consideration to children’s perceptual preferences. Traditional street landscape perception studies often rely on subjective analysis, which lacks objective, data-driven insights. This study overcomes this limitation by using eye-tracking technology to evaluate children’s preferences more scientifically. We collected eye-tracking data from 57 children aged 6–12 as they naturally viewed 30 images depicting school commuting environments. Data analysis revealed that the proportions of landscape elements in different street types influenced the visual perception characteristics of children in this age group. On well-maintained main and secondary roads, elements such as minibikes, people, plants, and grass attracted significant visual attention from children. In contrast, commercial streets and residential streets, characterized by greater diversity in landscape elements, elicited more frequent gazes. Children’s eye-tracking behaviors were particularly influenced by vibrant elements like walls, plants, cars, signboards, minibikes, and trade. Furthermore, due to the developmental immaturity of children’s visual systems, no significant gender differences were observed in visual perception. Understanding children’s visual landscape preferences provides a new perspective for researching the sustainable development of child-friendly cities at the community level. These findings offer valuable insights for optimizing the design of child-friendly streets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".