How dense are we? Another look at urban density and transport patterns in Australia, Canada and the USA
Bibliographic record
Abstract
For at least two decades, urban policy in Australia has been based on the belief that high levels of car use and poor public transport are mainly the result of low urban densities. There has been considerable debate about the evidence on which these policies are based, but until recently there has been no common data-set that allows densities and transport patterns to be compared on a consistent and rigorous basis. As a result of recent changes to data collection and publication systems by the Australian, Canadian and United States national census agencies, it is now possible to compare urban densities and transport mode shares (for the journey to work) across the three countries' urban areas on a consistent basis. This paper presents the results of this comparison. Australian cities have similar densities to those of Canadian cities and the more densely-populated US cities. There are variations in density among cities, but these show little or no relationship to transport modes share, which seems more closely related to different transport policies. These findings are very different from those on which current urban policies are based, and suggest the need for a radical rethinking of those policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".