Measuring and monitoring urban sprawl in Canada from 1991 to 2011
Bibliographic record
Abstract
Urban population growth and the expansion of urban areas has resulted in numerous negative environmental impacts. In Canada, built-up areas increased by more than 150% between 1971 and 2011, significantly faster than the number of inhabitants. Much of this increase took the form of dispersed, low-density urban development commonly referred to as urban sprawl. However, serious attempts to rigorously quantify and compare urban sprawl across Canada are lacking. This thesis measures the degree of sprawl for all 34 Canadian Census Metropolitan area (CMA) and the 469 Census Subdivisions (CSD) located within the boundaries of the CMAs and assesses temporal changes in urban sprawl between 1991 and 2011. This thesis uses the metrics of Weighted Urban Proliferation (WUP) and Weighted Sprawl per Capita (WSPC) to quantitatively assess the degree of urban sprawl. The value of WUP answers the question of how strongly the landscape within each reporting unit is sprawled per km2. WSPC quantifies the amount that, on average, each inhabitant or workplace contributes to urban sprawl in a reporting unit. The results demonstrate that urban sprawl increased considerably in all CMAs between 1991 and 2011. Montreal scored highest in 2011 among the CMAs (18.24 UPU/m2), followed by Victoria (17.93 UPU/m2), Kitchener-Cambridge-Waterloo (17.70 UPU/m2), Vancouver (17.24 UPU/m2), and Toronto (16.75 UPU/m2). Between 1991 and 2011, the Victoria CMA experienced the highest increase in WUP among all the CMAs. In the first decade between 1991 and 2001, urban sprawl increased in all CMAs. CMAs also showed a clear, continuous increase in urban sprawl in the second decade (2001-2011), except for Guelph and Ottawa-Gatineau-ON, where it decreased. WSPC also increased in more than half (59%) of the CMAs between 1991 and 2011. Saint John CMA (NB) obtained the highest value in 2011, followed by Thunder Bay and Greater Sudbury. The lowest WSPC values were observed in Toronto, due to the lowest land uptake per inhabitant or job, followed by Montreal, Vancouver, and Calgary. The period 1991-2001 witnessed an increase in WSPC in most of the CMAs (76%). In contrast, the value of WSPC decreased from 2001 to 2011 in most (76%) of the CMAs. The results presented here are especially useful for environmental monitoring and sustainability monitoring and to guide future planning seeking to reduce urban sprawl and its negative impacts. Although some sustainable development policies and Transit-Oriented Development (TOD) plans have been established to control urban sprawl in several cities (e.g., Vancouver, Toronto, and Montreal), progress towards controlling urban sprawl in the Canadian CMAs overall has been weak and sprawl continues to be a major threat to sustainable land use in Canada. Increased efforts are needed to more consistently and more effectively monitor and control urban sprawl and to transition to more sustainable forms of development e.g., smart growth. The insights from this study are particularly relevant to urban, regional, and land-use planning, and to the planning of future transport infrastructure. \n \nKeywords: Built-up area, Dispersion, Land up-take, Monitoring, Urban development, Urban growth, Urban permeation (UP), Urban Sprawl, Utilization density (UD), Weighted urban proliferation (WUP), Weighted Sprawl per Capita (WSPC)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".