A local and regional spatial index for measuring three-dimensional urban compactness growth
Bibliographic record
Abstract
The negative impacts resulting from urban sprawl are recognized as serious issues entailing environmental problems. Urban developments are moving towards a more compact form to mitigate many issues including pollution concerns, land depletion, and population growth demands. Urban compactness has been reported to be a more sustainable form of development that occurs through densification and mixed land use practices through spatial indicators that intensify the landscape. Urban modeling has been used extensively to aid in urban and regional planning as it can forecast possible scenarios of urban growth. The objective of this research is to develop and implement a spatial index for three-dimensional (3D) urban compactness to evaluate potential vertical development growth. The spatial index has two components, local and regional, and it is derived based on parameters accounting for a vertical urban growth suitability analysis, land designation, and average building height. Datasets used for this study were for the Metro Vancouver Region, Canada, a rapidly developing area with plans in place for sustainability and compact growth. The spatial index was derived for the study area for the year 2011 and projected to the year 2041 with a 10-year time interval, accounting for the spatio-temporal land use change. Results indicate concentrations of urban compactness growth near densely populated and transportation-oriented locations and also capture urban leap-frogging processes. The presented research aims to aid local governments in future planning processes related to regional sustainable development growth.
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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 | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".