Quantified Comparison of Landscape Urbanism and New Urbanism: Applying Mean Depth and Connectivity Measures in Space Syntax to Two Toronto Case Studies
Bibliographic record
Abstract
Landscape Urbanism and New Urbanism are two of the most recent and most relevant paradigms in contemporary urbanism. The two offer some major differences (such as density and approach towards urban sprawl, transportation mode choice, urban block size and arrangement, etc.) as well as some similarities (such as ecological sensibility, natural resource preservation, and connectivity of the urban fabric), causing them to become interested in similar urban contexts (such as post-industrial and brownfield sites), and making them comparable. Recently, there has been a lot of discussion around the conceived ideological, theoretical, and physical differences of these two paradigms, where proponents of each have brought forth arguments aimed at proving the superiority of their side and refuting the other. Despite the extent of these arguments, no quantitative comparison has been offered. To this date, the majority of these discussions have remained quite superficial. This paper proposes the use of Space Syntax as a methodology that can help fill this literature gap for meaningful quantitative comparison between the two paradigms. For the purpose of this study, a comparable Landscape Urbanist and a New Urbanist project were selected. The Lower Don Lands (Landscape Urbanist) and the West Don Lands (New Urbanist) projects are both located in downtown Toronto, Canada. They are both very recent projects and are of comparable sizes. A common claim between Landscape Urbanism and New Urbanism, and a relevant issue in contemporary urbanism, is the connectivity of the urban fabric. This characteristic was selected to be quantitatively compared between the two case studies through measures of the Space Syntax methodology. As such, the two case studies were compared using "connectivity” and "mean depth” measures. Results were then assessed to determine which project performed more successfully in making a connection between its site and the surrounding urban fabric. KEYWORDS: Landscape Urbanism, New Urbanism, Space Syntax, Integration, Mean Depth
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".