Thermal loads in two different urban quarters – perspectives from mobile measurements and mental maps
Bibliographic record
Abstract
Abstract The fact that different urban structures have different climatic effects and therefore differ in their thermal loads for people is well known. However, there is a lack of quantitative and qualitative surveys in specific districts that are suitable to derive accepted adaptation measures. This paper addresses the research questions where thermally stressed areas in public space are identified by mobile measurements and by mental maps and what are the causes of each, where both methods agree or disagree, and what are the benefits and the limitations of using both methods for prioritizing adaptation measures. Mobile measurements in an urban quarter over a whole day can supply needed data for determining thermal loads of urban structures and their temporal development. Mental maps give information about the perception of urban dwellers and - based on the spatial distribution of obtained data - the user frequency. Both methods provide information concerning where and when measures should be taken in order to facilitate adaptation to heat. The paper presents the results of mobile measurements and mental maps of two different urban quarters in Germany. Thermal loads were assessed by using the Universal Thermal Climate Index (UTCI) for three selected summer days. Results indicated that the different urban structures can differ by up to 7 K or by two stages of thermal stress during a hot summer day. Surface material with high albedo can overcompensate smaller sky view factors resulting in high thermal loads. Street trees caused changing thermal loads but reduced them on average. Identified hot spots based on mobile measurements mostly correlated with hot spots identified by mental maps, if they were frequently used. However, hottest spots identified by measurements were not necessarily most frequently named as hot spots in the mental maps. Most often named hot spots of mental maps coincided with major traffic routes suggesting that user frequency is important. We conclude that the combination of both methods can be valuable for identifying locations with high priority for climate adaptation in cities.
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 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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".