The Heat Exposure Integrated Deprivation Index (HEIDI): A Data-Driven Approach to Mapping Extreme Hot Weather Risk
Bibliographic record
Abstract
Background/Aim: Extreme hot weather events are increasing in intensity, frequency, and duration as a consequence of climate change. As a result, hot weather mortality is a growing concern in many urban environments, and different strategies are being explored to protect public health. A commonly implemented approach is the construction of spatial heat vulnerability indexes to identify areas at relatively higher and lower risk. Methods: Different approaches were used to generate three different indices for greater Vancouver, Canada using a pool of variables chosen to reflect social vulnerability, population density, temperature exposure, and urban form. The three different indices were: (1) unweighted; (2) weighted; and (3) the data-driven Heat Exposure Integrated Deprivation Index (HEIDI) approach. The performance of each index was assessed using mortality data from 1998-2014, and the maps were compared with respect to spatial patterns identified. Results: The spatial variables most strongly associated with mortality were the deprivation index, the density of the senior population, estimated apparent temperatures on a very hot day, and distance to the nearest major road. The population-weighted spatial correlation between the three indices ranged from 0.64-0.78. The HEIDI approach successfully detected areas of very high heat vulnerability, whereas vulnerability was more spatially smoothed by the other approaches. All indices performed best under extreme temperatures, but HEIDI provided more useful delineation of risk at lower thresholds. Conclusions: Each of the indices in isolation provides useful information for risk communication and public health protection. However, combining the HEIDI approach with standard unweighted or weighted methods provides richer information about the most vulnerable areas and populations. The methods we describe can be replicated in any other large urban environment where mortality data are available and can be georeferenced with relatively good accuracy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".