Potentialities of the urban volume : mapping underground resource potential and deciphering spatial economies and configurations of multi-level urban spaces
Notice bibliographique
Résumé
This dissertation looks at the urban volume, in its natural and artificial materiality, as a source of potential for future urbanization. Underground resourcesâ for buildable space, geomaterials, groundwater and geothermal energyâ tend to be addressed only as needs arise. This has historically led to conflicts between uses: basements and tunnels flooded by rising aquifers; drinking water sources endangered by infrastructures that carry pollutants into groundwater systems. The work was carried out as part of the Deep City Project, which argues for a paradigm shift of â resources to needsâ in which the potential of underground resources is addressed prior to any urban project or plan. The work presented here further develops a methodology to map the combined potentials of resources and includes an original investigation of the spatial relationships between underground and surface urban commercial spaces. The prologue introduces the overarching problematic and concepts using a dramatization of an incident that occurred during the construction of the M2 metro line in Lausanne in 2005. This concrete example sets the stage for the first chapter, where the theoretical framework of the dissertation is laid out in detail. The resources to needs paradigm is elaborated by looking at the underground as it has been addressed in normative city models, arguing that the dominant ecological and mechanical models do not provide the adequate framework for thinking resources prior to needs. This reflection draws on concepts from information and urban theory as well as philosophy, arguing for an approach to the mass of the urban volume as an economy of communicationâ of encounter and avoidance. The second chapter specifically addresses underground space through a spatial configurational analysis of the Montreal downtown, where a network of indoor and outdoor commercial spaces comprises a unique spatial volume. Relationships between the spaces are calculated using multiple accessibility metrics on a 3D spatial network model built in GIS. Common configurational characteristics are extracted using principal component analysis and placed in a spatial econometric model, which looks at the influence of spatial configuration on rental value per square meter of food and retail spaces. The results suggest that certain accessibility metrics contribute more than others to this value, but a subsequent geographically weighted regression reveals that this impact is varied in space and does not establish a clear separation between indoor and outdoor spaces. The third chapter presents the application of the Deep City mapping method to three case study citiesâ San Antonio, Texas, Hong Kong, China, and Dakar, Senegalâ which have relatively diverse and complex relationships to their geology and surface urbanization. In each case, adjustments are made to the methodology, particularly in how the potential of the surface urban form contributes to the underground potential of the city. The results of the maps, which provide a city-wide overview of underground potential, are discussed by returning to some of the projects and problematics currently addressed by each cityâ s urban planning departments or master plans. The conclusion summarizes the research as a whole and revisits the theoretical framework in discussing future avenues for research and practical application.
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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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».