Creating an urban heat vulnerability index (HVI) in the face of climate change employing geospatial technology in Halifax, Canada
Bibliographic record
Abstract
Heat waves are one of the most common weather events happening in recent decades, posing threats to public health especially in urban built-up environments.This study employs geospatial techniques to evaluate urban heat vulnerability in the city of Halifax, Nova Scotia, Canada.The Heat Vulnerability Index (HVI) was developed through the utilization of the Geographic Information System (GIS), integrating exposure, sensitivity, and adaptive capacity measures generated using Remote Sensing (GIS) and socioeconomic datasets for four years covering : 2006, 2011, 2016, 2021.The process applies an Equal Weight Approach (EWA) to assign equal importance to the 16 normalized variables considered in creating the comprehensive HVI.The overarching goal of this study was to assess heat vulnerability at a local level by offering a detailed analysis of these 16 proposed indicators in an urban setting.The results revealed that the HVI attained its peak in the year 2021, exhibiting a variable trajectory in its scores, with all years demonstrating a significant high-risk zone encompassing the regional center.Findings may enable multiple stakeholders to understand spatial variability of temperature anomalies at local level and may identify vulnerable populations at risks.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".