Urban volumetrics: From vertical to volumetric urbanisation and its extensions to empirical morphological analysis
Bibliographic record
Abstract
While cities have become gradually more vertical and complex over the past century, our methods for conceptualising their characteristics and measuring their forms and functions are still largely based in a horizontal mindset. Recent work has sought to shift urban discourse towards understanding cities according to their volumetric properties. Moving the debate further, this paper approaches volumetric urbanism from a morphological perspective, setting out a research agenda that operationalises the concept as a means of better capturing the morphological characteristics of cities as volumetric entities. First, we deconstruct volumetric urbanism into the five basic building blocks that define volumetric morphologies: density, functional mix, compaction and compression, complex networks and interaction intensity. Next, we propose two methods for capturing the urban volumetrics of cities based on spatial and network interaction and apply them to a hypothetical case and a preliminary study of Hong Kong. We conclude by arguing that a volumetric approach is required to capture the complex form of compressed, multi-layered and highly connected cities. In response, urban morphological and planning discourses must move away from the horizontal analytical mindset, embrace a multi-layered three-dimensional view of cities and place greater emphasis on spatial configurations and network relations by measuring interaction.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".