An investigation of 15-minute neighbourhoods in Surrey, British Columbia: A community-informed social equity analysis for a fast-growing, diverse, Canadian city
Bibliographic record
Abstract
Auto-centric urban design drives health and environmental issues. Proximity-based concepts like “15-minute neighbourhoods” can reduce these harms. Most studies on 15-minute neighbourhoods have been in European centres, and few have incorporated community views. Set in a fast-growing city, this study developed a community-informed definition of 15-minute neighbourhoods and explored social equity in accessibility to amenities. Based in Surrey, British Columbia, Canada (population 580,000), this mixed-methods study involved mapping and community engagement. We created preliminary maps of 15-minute neighbourhoods by using open data for 6 amenity types (community centres, educational facilities, grocery stores, health facilities, parks, and public transit) and mapping spatial access by walking/cycling for every dissemination area using ArcGIS and r5r. We then hosted focus groups with equity-deserving residents (n = 102) to understand if these preliminary maps aligned with their experiences and gather input on what was missing and what concerns they had. We drew on participants’ input to create a community-informed definition and refined maps. With census data (2021), we conducted a social equity analysis by calculating the percentage of residents living in 15-minute neighbourhoods and assessing access for equity-deserving populations. Overall, 52% of Surrey residents lived in areas considered 15-minute neighbourhoods. Participants felt maps missed some amenities (e.g., places of worship) and that beyond amenities, supportive infrastructure, safety, and terrain were vital. We produced bivariate maps, including microscale design features, highlighting areas with many amenities but little supportive infrastructure. The social equity analysis did not highlight inequities in spatial access; rather, areas with more children/youth living in one-parent households, Indigenous peoples, low income residents, and recent immigrants were more likely to be 15-minute neighbourhoods. Community voices added insights into factors beyond amenities that matter. As proximity-based planning proceeds, care is needed to ensure that future city design meets the needs of all residents. • Community input shaped the definition of 15-min neighbourhoods. • Over ½ of Surrey's population lived in a 15-min neighbourhood. • There were no socio-spatial inequities in access to amenities in Surrey. • Residents thought factors other than amenities, such as infrastructure, were vital.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".