The greener, the richer, the happier?——Spatial distribution and coupling analysis of urban green space and residents’ emotion based on social media data
Bibliographic record
Abstract
• Utilizing SMD as a regional residents' emotional index, dividing into tendency and value. • Utilizing NDVI and RP quantify green space exposure and the degree of affluence in a region. • Positive emotions exhibit a greater concentration within high-resource urban regions. • A 1% rise in NDVI increases EV by 0.178%, while a 1% RP rise reduces EV by 0.109%. The emotional well-being and welfare of urban residents are intricately linked to their surrounding living environments. Urban development in China has progressively placed greater emphasis on the human settlement environment. And has introduced policies such as urban regeneration and low-carbon community construction, which are aimed at upgrading urban quality and improving the well-being of the people. An increasing amount of attention is being drawn by users, managers, and designers towards the design of urban green spaces that take into account the emotional considerations of the residents. The explosive growth of social media has presented novel opportunities to explore the correlation between residents’ emotions and urban green spaces. Research on the traditional correlation between urban green spaces and residents’ emotions has been constrained by limited individual sample sizes, resulting in a generally narrow research scope and a relatively homogeneous set of factors considered. This study, taking the urban area of Hangzhou as a case study, investigates the relationship between NDVI, residential prices, and emotional value at the city scale. Through the application of the Coupling Coordination Degree Model and the Mediation Effect Model, the study specifically focuses on the efficiency and fairness of urban green space distribution. The findings reveal that the emotional value within the study area spans from −8 to 19, with positive emotions comprising 49.73% of the total. However, these emotions exhibit a scattered spatial distribution. The mediation effect analysis reveals that an increase in NDVI by 1% leads to a 0.178% growth in emotional value, while a rise by 1% in residential prices decreases in emotional value by 0.109%. By leveraging social media data as evidence has provided a fresh research perspective on the developmental trajectory of green spaces. It has also discovered that enhancing the quality and functionality of green spaces can boost urban well-being, offering valuable guidance to planners in the context of park city.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".