Notice bibliographique
Résumé
Walkability can be broadly conceived of as an evaluation of the suitability of a built environment for pedestrian locomotion and has recently become a popular concept across a multitude of disciplines. This evaluation is often conceptualized as a measurement, index, or tool, and has thus found itself particularly applicable within the fields of urban planning and design. This seemingly simple concept has expanded over the years, and now operates on a multitude of scales and utilizes a variety of measurement techniques, from GIS models of regional walking networks, to one-on-one interviews to explore how individuals conceptualize the idea of walkable space, to machine learning systems that evaluate imagery for desirable urban characteristics. This sprawling field now faces a challenge, with several studies concluding that walkability is becoming conceptually incoherent as it is applied in more situations—a challenge exacerbated by a lack of standardization in methodologies or definitions. Further confounding concerns of conceptual incoherence is the variability of human experience across the globe, acknowledging that different groups of people may have different values for what makes space walkable. In this context, the idea of a metric that can work in diverse places to evaluate the built environment becomes troublesome.This study explores the aforementioned challenges in two ways: through an exploration of the diversity of literature around the subject and through an empirical study. A survey of available literature found that walkability has broadened beyond its initial conceptual confines to encompass more and more definitions over time, while incorporating additional methodological approaches as well. Recently, numerous authors have drawn the conclusion that walkability may carry different meanings in different research settings when used according to different disciplinary approaches. Here, an empirical study was carried out that compared two groups’ perceptions of walkable space, namely one in Montreal, Canada and one in Pune, India. By having participants from both locations rate large numbers of streetscape images based on their perceived walkability, and by comparing such ratings with machine-learning image segmentation results, aspects of the built environment that constituted walkable space for each group were evaluated. It was found that while there was a difference in how walkability is conceived of in terms of elements of the built environment, a common conception of general walkability exists between the two groups. A notable example of this pattern from this study is that Montrealers tended to view greenspace as a significantly more important component of walkability than participants from Pune viewed it, though both agreed that an area with pleasant greenery and little traffic was walkable. This scalar difference has important implications for future walkability work, implying that further research is needed to delineate universal walkability from contextualized walkability
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 | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,016 |
| Communication savante | 0,007 | 0,010 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
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 ».