The Impact of Green-Blue Infrastructure on Enhancing Cities Happiness Indicators - A Comparative Study
Bibliographic record
Abstract
Many countries have shown interest in the happiness of societies due to its significant importance to citizens.This interest has been reflected through important indicators aimed at enhancing urban happiness, including economic factors such as per capita GDP, healthcare and educational services, as well as environmental factors.The research aims to explore one aspect of the reasons for the decline in the classification of Iraqi cities in the World Happiness Index.Green spaces have been the primary focus of studies in improving urban happiness, along with blue spaces.Due to instability, Iraq has experienced a decline in the ranking of its cities globally.The research utilized the NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) calculated using the Copernicus Open Access Hub, Sentinel2L1C.The study compared the reality of Iraqi cities (Mosul, Baghdad, Basra, and Najaf) with the two happiest cities in the world according to international happiness reports for 2023: Aarhus in Denmark ranked first and Amsterdam in the Netherlands ranked second.Data analysis was conducted using Land Viewer|EOS to extract the proportions of green and blue spaces for each city.The results indicated that green spaces in Iraqi cities were limited, with little attention paid to blue spaces or their preservation, resulting in their scarcity compared to Aarhus and Amsterdam.This has contributed to Iraqi cities lagging in their classification as happy cities.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".