Perceived impact of meteorological conditions on the use of public space in winter settlements
Bibliographic record
Abstract
This study aimed to assess the impact of meteorological conditions on the use of public space in Scandinavia and Canada. Between September 21 and December 18, 2017, a cross-sectional online survey 'EAMQ-Climate: space' was distributed via web-based platforms. Survey responses were received from 361 residents (258 people from Scandinavia and 103 from Canada). The relative impact of the meteorological determinants on the use of public space was calculated, and a factor analysis was performed. Disparities between Canada and Scandinavia as well as between the climate zones represented were analysed using ANOVA. Overall results showed that the most significant meteorological enablers for the use of outdoor public spaces in winter were solar gain, snowfall and snow-covered surfaces. The main barriers were slush-covered and icy surfaces, rainfall and darkness. Wind and cold were conditions with less influence. The impact of rain and ice, however, differed between climatic zones. It was also established that, when addressing the meteorological impact on avoiding the use of public spaces in winter, it is vital to discriminate between conditions related to a) the ground surface and b) ambient conditions, as well as the particular significance of c) snow and sun, and d) darkness. For the design of public space in winter cities, we conclude that designers need to focus on a wider range of weather conditions than sun, wind and cold, and include snow, rainfall, slushy and icy ground and poor visibility. The study suggests that winter public space has a higher climatic design requirement to be successful than streets and pathways that are mainly used for soft mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".