Third Spaces to Represent Urban Greenery: A Study of Informal Green Spaces in a High-Density City Using Deep Learning and Geo-Weighted Analysis
Bibliographic record
Abstract
In high-density cities like Hong Kong, green spaces are often characterized by fragmentation and uneven spatial distribution, which negatively impacts their accessibility and equity. To address this issue, studies have proposed the use of informal green spaces (IGSs) as a supplementary component to formal urban green spaces (UGSs). However, the spatial delineation and quantitative analysis of IGSs remain challenging due to the lack of standardized identification and evaluation methods. Building upon the work of urban theorists Henry Lefebvre and Edward Soja, this study explores informal green spaces as third spaces. This study employed remote sensing and GIS technologies to systematically assess the spatial distribution and benefits of IGSs, categorizing them into four types: Urban Interstitial IGSs, Transitional IGSs, Fringe IGSs, and Riparian IGSs. Subsequently, an evaluation framework was constructed across ecological, social, and economic dimensions to quantify the overall value of IGSs. The results reveal that IGS significantly contributes to ecological regulation, social interaction, and economic potential, particularly in urban areas with limited green resources. This demonstrates that IGSs can serve as a vital complement to formal urban green spaces, playing a key role in alleviating green space inequity, enhancing urban livability, and promoting sustainability. Furthermore, this study provides a scientific foundation for precise identification, benefit assessment, and optimized management of IGSs, supporting effective integration and rational utilization in future urban planning.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".