Analyzing spatial patterns of urban green infrastructure for urban cooling and social equity
Bibliographic record
Abstract
‘Greening’ urban areas is a key strategy to counteract rising urban temperatures and their negative impacts on human health and well-being. Efficient green space management is needed for optimizing ecosystem benefits in urban areas with limited space availability. Existing research often overlooks the spatial configuration of urban green infrastructure (UGI) and focuses on single scales, limiting its application. Our study evaluates how the composition and configuration of UGI impacts the supply of cooling services at two spatial scales. We also assess the equity of access to these cooling services in Montreal, considering socioeconomic factors. Using a statistical model based on three key predictors, percent cover of both high and low vegetation and the largest patch index of high vegetation, we predict the cooling supply of UGI during summer daytime with a moderately high accuracy (R 2 = 0.80). Our results showed that during the day, an increase of 10 % in the percent cover of high vegetation, percent cover of low vegetation, or largest patch index of high vegetation would lead to a decrease in average land surface temperature of 1.41 °C, 0.76 °C, or 0.20 °C, respectively. To assess social equity, we developed a cooling demand index based on the proportion of vulnerable groups, focusing on older adults and young children. Mapping mismatches in cooling supply and demand revealed that neighborhoods with higher proportions of visible minorities and low-income residents had significantly lower access to cooling services. Conversely, access was significantly positively correlated with income and post-secondary education levels. Overall, our results offer practical guidance for designing UGI layouts that maximize cooling supply and emphasize the need to prioritize underserved communities in urban greening efforts. • Percent cover and largest patch index of trees are key factors explaining vegetation cooling. • The importance of configuration of urban greening is larger at finer spatial scales. • Planting trees in more concentrated clusters can enhance daytime cooling. • Vulnerable populations in marginalized communities receive less cooling service.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".