The Changes in Public Open Space Usage and Perceptual Urban Design Qualities After the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic that just passed has sparked widespread discussion about how the pandemic teaches new knowledge in the design of urban public spaces.Since the emergence of urban design theories for public open spaces, urban design has encouraged people to leave their homes for activities.However, the pandemic has threatened people's outdoor activities.The perception of public space is an important research area for designing quality cities that create comfortable and safe places for the community.It can determine what should be designed and how it should be designed.Therefore, this study aims to examine changes in people's perceptions of urban public open space usage and people's preference for urban design qualities for the sustainability of streets as public open spaces after the COVID-19 pandemic.Data collection applied an online survey method using questionnaires distributed among people in Malang City, Indonesia.One hundred and eight respondents participated in the survey.The questionnaire investigated the changing use of public open spaces and the most frequently visited public spaces before and after the pandemic.The study also explored the impact of the pandemic on people's preference for urban design qualities: enclosure, legibility, human scale, transparency, complexity, coherence, linkage, imageability, and social life.This study applied descriptive statistics and paired samples t-test to analyze the data.Results indicated significant changes in people's perceptions of urban public open space usage after the pandemic.The study also found significant differences in people's preference for urban design quality, especially the enclosure quality and the social life aspect of the public space.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".