Linking human wellbeing and urban greenspaces: Applying the SoftGIS tool for analyzing human wellbeing interaction in Helsinki, Finland
Bibliographic record
Abstract
This article reviews a study into the relationships between greenspaces and the benefits to psychological, social, and physical aspects of human wellbeing achieved through interaction in the Helsinki urban region in Finland. This relationship is theorized, analyzed, and measured through the transactional paradigm of affordance theory and is operationalized through the use of a public participation geographic information system (PPGIS) questionnaire, SoftGIS, which activated the urban greenspace–human wellbeinginteraction throughits map-based data collection. Over 1800 unique place–based relationships were statistically analyzed. Findings revealed that Helsinki’s greenspaces provided, overall, mostly physical and social wellbeing benefits; the psychological benefits such as reduction in stress and mental relaxation were not as frequent in these urban greenspace interactions. The results indicate multiple aspects of human wellbeing are supported by interaction with urban greenspaces of varying characteristics within the region but the urban greenspaces which provided the most human wellbeing benefits included large size, woodland typology, moderately maintained with loose or ‘wild’ vegetation, and few amenities such as benches and structures. The study’s implications include urban planning, public policy, and human health as well as insight into the multifunctional design and strategic management of greenspaces in urbanizing regions to provide continued and improved ecosystem services and benefits to humans and nature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".