Urban green spaces and mental wellbeing: A methodology for measuring structural characteristics of individual-level green space exposure and its associations with wellbeing
Bibliographic record
Abstract
The number of people living with mental disorders is increasing worldwide. Considering rapid global urbanization, research on the effects of urban environments on mental wellbeing is becoming crucial, particularly regarding the role of green spaces. This paper analyzes data collected using a smartphone-based toolkit, gathering multimodal data such as participants’ photographs, GPX tracks, and categorical emotion tags in response to urban environments. Through exploratory, semi-automated analysis, urban green spaces are identified and explored in relation to mental wellbeing using statistical methods. The proportion and distribution of green spaces are quantitatively analyzed through image analysis. This novel method of seamlessly integrating data collection and analysis allows for a distinction between the effects of the directly and consciously perceived environment and the indirect, unconsciously perceived environment. Preliminary results indicate that green spaces in everyday environments may positively impact mental wellbeing. There is potential for integrating green spaces, particularly in city centers. These findings enable numerous empirical studies to investigate the influence of green spaces on mental wellbeing and represent a valuable extension of the evidence base for urban planning and policy decisions. The key benefits of this work lie in its consideration of everyday perceptions of green spaces and its ability to overcome limitations of previous studies through more detailed data collection and efficient automated data analysis. • Efficient semi-automated method to determine green spaces from multi-modal user-generated data. • Self-reported well-being in daily urban action spaces. • New mobile diary app to collect and tag multimodal data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".