Streetscape Design: Perceptions of Good Design and Determinants of Social Interaction
Bibliographic record
Abstract
Historically, streets have provided both a means of livelihood and social support for its inhabitants. The emergence of the car dramatically shifted planning practices from the pedestrian, to the efficient movement of automobiles, resulting in the fragmentation and dispersion of communities. Current academic streetscape design guidelines focus on creating an aesthetically pleasing and functional street; however, these guidelines alone do not appear to foster strong community ties and social networks. A review of the place making literature identified that a number of factors can play a significant role in a user’s ability to secure a strong sense of place, place attachment and sense of community. This exploratory research analyzed place making literature and employed qualitative methods with observations and interviews of users in three streetscapes located in Vancouver, British Columbia; W 41st Ave, Commercial Drive and Fraser St. The resulting feedback obtained from this multiple case study approach has provided the basis upon which a user driven streetscape design visualization was created. It was then compared to a visualization based upon current academic design guidelines. Through an examination of this research, it became apparent that the design of a streetscape does influence the social interaction of its users. It was also discovered that the academic driven urban design guidelines do not fully reflect the preferences and social needs of its users. This research has helped to close the knowledge gap between the design of the physical form of our streets and the user’s preferences. Additionally, it has illustrated the essential role that place making principles should play in the design process. Current theories and concepts of streetscape design have since been expanded and now have the potential of creating more socially sustainable, vibrant streets.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".