Notice bibliographique
Résumé
INTRODUCTION Walkability captures the proximity between functionally complementary land uses (live, work, and play) and the directness of a route or the connectivity between destinations (Forsyth and Southworth 2008, Moudon et al. 2006). A walk score is an indicator of how friendly an area is for walking. This score is related to the benefits to society in terms of energy savings and improvement in health that a particular environment offers its residents. For example, a recently developed walk score Web site uses Google Maps, specifically Google's Local Search API (application programming interface), to find the stores, restaurants, bars, parks, and other amenities within walking distance of any address that is entered. Walk score currently includes addresses in the United States, Canada, and the United Kingdom. The algorithm behind this score indicates the walkability of a given route based on the fixed distance from one's home to nearby amenities. The number of amenities found nearby is the leading predictor of whether people will walk rather than take another travel mode. However, evaluating walkability is challenging because it requires the consideration of many subjective factors (Reid 2008). Moreover, all technical disciplines related to walkability have their own terminology and jargon (Abley 2005). During the urban and regional planning processes, the spaces and the environmental quality of neighborhoods are important factors that affect human health. Fortunately, spaces and neighborhood environmental quality can be improved through proper urban management. Thus, epidemiological studies have explored the relationship between access to nature and health. For example, a study in Sweden by Grahn and Stigsdotter (2003) demonstrated that the more often one visits areas, the less often one reports stress-related illness. One epidemiological study performed in the Netherlands (Maas et al. 2006) showed that residents of neighborhoods with abundant spaces tended, on average, to enjoy better general health. Another possible mechanism relating nature to health occurs during social interactions and social cohesion. Several studies conducted in Chicago suggest that spaces, especially trees, may facilitate positive social interactions between neighboring residents (Kweon, Sullivan, and Wiley 1998). Moreover, Pretty et al. (2007) summarized the effects of ten exercise case studies (including walking, cycling, horseback riding, fishing, canal boating, and conservation activities) in four regions of the United Kingdom on 260 participants. They determined that exercise (i.e., exercise in a area) led to significant improvements in self-esteem and in total mood. The results were not affected by the type, intensity, or duration of the exercise. Therefore, in many parts of the world, current urban planning activities are shifting toward a focus on green living. Many cities around the world now are developing integrated solutions to major environmental challenges and are transforming themselves into more sustainable and self-sufficient communities (Dizdaroglu, Yigitcanlar, and Dawes 2009). On the other hand, GIScience provides theory and methods that have the potential to facilitate the development of spatial analytical functions and various GIS data models, which improve the building of sophisticated GIS systems. Among them, the GIS road network data model is important for solving the problems in urban areas, such as transportation planning, retail market analysis, accessibility measurements, service allocation, etc. There are several network models in GIS, such as river networks, utility networks, and transportation networks or road networks. Understanding the road network patterns in urban areas is important for human mobility studies, because people live and move along the road networks. A network data model allows us to solve daily solutions, such as finding the shortest or quickest path between two locations, looking for the closest facilities within a specific distance, and estimating driving time. …
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».