A Place to Grow? A Comparative Content Analysis of London and Toronto Ontario and the Importance of Public Green Spaces for Community Well-Being During and After COVID-19
Notice bibliographique
Résumé
Public green spaces represent key aspects of our communities for a variety of reasons. Evidenced through decades of planning scholarship, well established, attractive, and accessible public green spaces can promote community health and wellbeing while supporting other elements of healthy cities, like climate resilience and adaptation. However, when the coronavirus pandemic caused the safety of these spaces to come into question for fear of community transmission, questions regarding the accessibility, availability, and equity aspects of their planning and design came to the surface as well. The coronavirus had profound influence on the demand for public green space as necessary amenities and services had been shuttered, and those who lacked private yard space in denser urban communities longed for an escape from prolonged stay at home orders. As our public health and safety came to odds with each other, these areas began to populate, and cities struggled to grapple with overcrowding in public parks and open spaces. This illustrated systemic gaps that have been deeply ingrained in planning policy and practise for years when it comes to the adequate balance between the dispersion of these spaces and the densification of urban areas like London and Toronto. Historically, planning has come to focus on these spaces as a luxury for white affluent communities and a selling point for prime real estate while other vulnerable communities go underserved and lack access to safe, accessible, and attractive public green space. The findings demonstrate how the urban development has continued to exacerbate inequities in cities by facilitating a disregard for the importance of public green spaces in communities. This study found that this is due to a lack of policy direction and support in addition to a rationale behind public green space planning that does not take a holistic approach to aesthetics, luxury, climate resilience, and public health. This research showed how cities like London and Toronto have not prioritized a balance between space and density while they continue to rapidly grow and urbanize. By comparing both a mid and large sized city, this study was able to draw similarities and difference across urban contexts by focusing on the priorities and strategies for public health, growth, and public green spaces employed by either location. In the final phase of research, the study looked to world renowned examples for green planning like Vancouver and Copenhagen to identify applicable strategies that could work in either location. The results of these findings give key recommendations for how municipalities address this balance in post COVID-19 recovery. These recommendations consider the reprioritization of public green space in planning and practise to support a holistic approach to urban development, the establishment of clear definitions for the varying types and sizes of these spaces, a measurement to understand how much greenspace exists at a micro level, and a need for development applications to respond to that measurement. These results indicate that the pandemic set off red flags for unbalance between space and place in dense urban centres yet provides a unique opportunity to move away from decades of poor planning decisions in the future.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,010 |
| Études des sciences et des technologies | 0,017 | 0,010 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».