Measuring and monitoring urban sprawl in Canada from 1991 to 2011
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,010 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».