Potentialities of the urban volume : mapping underground resource potential and deciphering spatial economies and configurations of multi-level urban spaces
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".