Growing up, growing out: comparing spatial patterns of urban populations in Canada
Bibliographic record
Abstract
The spatial distribution of population and related density characteristics has a significant impact on urban form; a low-density urban form is typically associated with low efficiency of service delivery, poor connectivity between communities, and a high tendency of urban sprawl, whereas higher density urban form is associated with transit-oriented development, efficient service delivery, and lower overall infrastructure costs. However, an urban area is never a homogenous environment. Depending on the general community designs, natural barriers, and massive functional infrastructures (airports, large parks), the urban population be may spread out evenly or condensed into some disjointed, isolated clusters. Given the context that Canadian cities have typically low population densities, their population distributions are subject to high spatial variabilities. We use geographic information system (GIS) techniques and geostatistical approaches (Getis–Ord hot spot analysis and HDBSCAN) to visualize and compare sub-municipal level population density of the 10 most populous census subdivisions (CSDs) in Canada. Results reveal both low-density forms and density segmentations in most municipalities, especially those without a natural or political border to constrain growth. Population segmentation is sometimes unsolvable due to natural landscapes or massive infrastructures initially planned by local municipalities; however, segmentations may be mitigated if future growth strategies maximize existing population clusters.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".