Cartographic Visualization of Vulnerability to Natural Hazards
Bibliographic record
Abstract
Vulnerability to natural hazards has many components. It is about exposure to various natural disasters, but a place's vulnerability also depends on its capacity to prepare for, respond to, and recover from shocks resulting from natural extreme events. To avoid increased place vulnerability due to the anticipated negative effects of climate change, local authorities need to know which places are the most vulnerable and what makes these areas vulnerable. We have developed ViewExposed to provide this information. Knowing where the most vulnerable areas are is very useful for local stakeholders, since these places may be most in need of adaptation strategies. However, stakeholders also need to have an understanding of what makes these areas vulnerable. ViewExposed provides this information using a parallel coordinates plot, a table view, sparklines, and a profile report. Although vulnerability assessment data are complex, ViewExposed has an easy-to-use interface facilitating a high degree of user interaction through multiple and linked views. An improved understanding of the many aspects of vulnerability has a far-reaching potential to inform users efficiently about factors that influence the overall vulnerability and, as a consequence, can help raise people's awareness of what makes places vulnerable to natural threats.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".