Urban form in Canada at a small-area level: Quantifying “compactness” and “sprawl” with bayesian multivariate spatial factor analysis
Bibliographic record
Abstract
Quantifying urban forms to explore urban compactness or sprawl has become increasingly popular in multiple fields in the past decades. However, previous studies predominantly analyze the multidimensional phenomenon at large-area levels such as metropolitan areas, concealing variations that probably occur at small-area levels. Canadian studies measuring urban forms are usually conducted at the regional level with inconsistent indicators and approaches, hindering meaningful comparisons of compactness or sprawling between different regions. This study bridges a previous gap by applying Bayesian multivariate spatial factor analysis to construct a new composite urban compactness index for all Census Tracts (CT) in Canada. Nine urban form indictors representing four dimensions, density, centering, land use, and street connectivity are used in developing the index. Posterior probability is used to detect CTs that are most compact or sprawling. Results indicate that gross population and employment densities best characterize urban compactness at the CT level while land-use mix is the least central indictor to define the multi-faceted concept. Notable differences of urban compactness are detected across Canada and among different Census Metropolitan Areas (CMA). The most compact CTs usually locate in downtown or city center areas of a CMA. Larger and more populous CMAs, which also capture a larger extent of periphery areas, are not necessarily more compact and vice versa, suggesting the need to measure local variations of urban compactness. The constructed composite index allows direct urban compactness comparisons across different Canadian regions. Findings from this study can be used to guide smart and sustainable urban development in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".