The Role of Maps in Neighborhood-Level Heat Vulnerability Assessment for the City of Toronto
Bibliographic record
Abstract
Extreme hot weather is a threat to public health, and it is anticipated that the number of hot days and the duration of extreme heat events will increase with climate change. Already, heat-related illness and mortality is the dominant natural hazard in many countries. While everybody is at risk to varying degrees, there are known factors relating to heat exposure and sensitivity that make some population groups more vulnerable than others. The objective of this paper is to assess carto-graphic design decisions in creating heat vulnerability maps, and how they may affect the usefulness of different map types. Spatial patterns of heat vulnerability were visualized using maps representing individual exposure and sensitivity indicators, composite vulnerability indices, and geographical hot spots of vulnerability. The composite indices were calculated using the ordered weighted averaging (OWA) multi-criteria analysis method. Hot spots were determined using local indicators of spatial association (LISA). This study is part of an ongoing project which aims to identify vulnerable populations within the City of Toronto, Canada, in order to support targeted response and mitigation. The maps were found to be a valuable addition to the hot weather planning toolkit supporting neighbor-hood-level interventions. KEYWORDS: Climate change, cluster maps, geographic information systems, heat-related illness, multi-criteria analysis, vulnerability index
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".