Are Compact Cities a Solution to Sustainability? Lessons From an Urban Morphological Analysis of Calgary and Zurich
Bibliographic record
Abstract
Urban sprawl presents significant challenges, including economic inefficiencies, public health burdens, and environmental degradation. Compact city development offers a viable solution by fostering economic resilience, improving public health outcomes—reducing the need for costly healthcare services—and lowering carbon footprints, which translates to financial savings at both municipal and individual levels. This seminar will explore these benefits through a comparative analysis of Calgary and Zurich, examining how their urban forms and policies shape development outcomes. While Zurich exemplifies dense, transit-oriented growth, Calgary's expansive layout and car-dependent infrastructure pose barriers to achieving similar efficiencies. A key method for assessing and guiding urban change is morphological analysis, which allows us to evaluate parcel sizes, building types, back alleys, and street widths to identify redevelopment opportunities. By understanding these spatial patterns, cities can better design policies that encourage sustainable, human-scaled development. Finally, effective public engagement is crucial in shaping livable communities. Case studies from Chinatown and Canmore demonstrate how participatory urban design fosters inclusive decision-making. Insights from urban design studios further illustrate the tools necessary for meaningful community involvement. This seminar bridges research and practice, equipping participants with the analytical and engagement strategies needed for shaping sustainable, inclusive, and equitable cities, as stated in the UN’s SDG 8.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".