Greenery in urban morphology: a comparative analysis of differences in urban green space accessibility for various urban structures across European cities
Bibliographic record
Abstract
The understanding of urban social-ecological systems requires integrated and interdisciplinary methods. This paper explores differences in the accessibility of urban green spaces (UGS) based on urban morphology. In contrast to other comparative analyses that followed simplified quantification of UGS provision and/or omitted the impact of morphological properties of urban space, this study proposes three improvements. First, it uses the share of UGS in the service area of 300 m walking distance around each residential building in a city as a measure of UGS provision. Second, it includes the potential physical accessibility of UGS as warranted by key actors, such as owners or managers, who decide whether UGS are open or not to potential users. Third, it links UGS accessibility and heterogeneous urban structures. We developed a mixed-methods analysis that combines multiple data sources regarding UGS, the spatial distribution of residential buildings, and street networks. We conducted our analysis in five case-study cities (Barcelona, Halle, Lodz, Oslo, and Stockholm). Our findings suggest that the urban structures where the human–environment interaction transformed the space (such as in the core city areas) are characterized by limited UGS in the service area. Urban structures that are less transformed by human activity (especially suburbia) have the highest share of selected UGS in the service area. In addition, even if the share of UGS in the service area is high, many of them might have limited physical accessibility. In the broader sense, this highlights that social-ecological processes are linked to urban form and cannot be separated in an analysis. Therefore, social-ecological systems could be better understood through the lens of urban morphology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".