Operationalising the 20-minute neighbourhood
Bibliographic record
Abstract
BACKGROUND: Recent rapid growth in urban areas and the desire to create liveable neighbourhoods has brought about a renewed interest in planning for compact cities, with concepts like the 20-minute neighbourhood (20MN) becoming more popular. A 20MN broadly reflects a neighbourhood that allows residents to meet their daily (non-work) needs within a short, non-motorised, trip from home. The 20MN concept underpins the key planning strategy of Australia's second largest city, Melbourne, however the 20MN definition has not been operationalised. This study aimed to develop and operationalise a practical definition of the 20MN and apply this to two Australian state capital cities: Melbourne (Victoria) and Adelaide (South Australia). METHODS: Using the metropolitan boundaries for Melbourne and Adelaide, data were sourced for several layers related to five domains: 1) healthy food; 2) recreational resources; 3) community resources; 4) public open space; and 5) public transport. The number of layers and the access measures required for each domain differed. For example, the recreational resources domain only required a sport and fitness centre (gym) within a 1.5-km network path distance, whereas the public open space domain required a public open space within a 400-m distance along a pedestrian network and 8 ha of public open space area within a 1-km radius. Locations that met the access requirements for each of the five domains were defined as 20MNs. RESULTS: In Melbourne 5.5% and in Adelaide 7.6% of the population were considered to reside in a 20MN. Within areas classified as residential, the median number of people per square kilometre with a 20MN in Melbourne was 6429 and the median number of dwellings per square kilometre was 3211. In Adelaide's 20MNs, both population density (3062) and dwelling density (1440) were lower than in Melbourne. CONCLUSIONS: The challenge of operationalising a practical definition of the 20MN has been addressed by this study and applied to two Australian cities. The approach can be adapted to other contexts as a first step to assessing the presence of existing 20MNs and monitoring further implementation of this concept.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".