Urban Growth and Long-Term Changes in Natural Hazard Risk
Bibliographic record
Abstract
This paper explores the question of whether natural hazard risks for urban areas are growing or diminishing over time. While trends such as population growth in hazardous areas increase the potential for loss in disaster events, other factors, such as improved building codes, tend to reduce this risk. The net effects of such urban changes are examined through the use of simulation models that estimate disaster losses. In a comparative static approach, losses are modeled for the same hypothetical hazard event striking at different points in an urban area's development history. The analysis focuses on a case study of seismic hazard for the metropolitan area of Vancouver, Canada. Results of a casualty-estimation model applied retrospectively indicate that, for the same M7.3 seismic event, human casualties would have been similar if the earthquake had struck in 2006 as in 1971, even though the population at risk had doubled in the interim. This improvement in per capita casualty rate is largely due to building-code upgrades and changes in new construction. Other dimensions of risk worsened, however: the estimated population in significantly damaged buildings increased by some 60% for the region and, in some fast-growing municipalities, more than doubled. Using a similar analytical approach, a second modeling effort focused on transportation systems and considered the risk implications of future growth. Analysis compared transportation disruption in 2004 and 2021 for a constant earthquake hazard but a forecast 21% increase in traffic. Results indicate that, for the Vancouver metropolitan area overall, disaster risk to transportation is expected to decrease slightly due to the shifting geography of residences and workplaces. The spatial distribution of risk would change, however: in particular, it would worsen for the older urban core. Moreover, the relative importance of key bridges changed over time. The analyses here indicate that in this case study, risk is not worsening significantly overall but is undergoing notable spatial redistribution; however, the extent to which this finding can be generalized to other urban areas and types of hazards remains to be investigated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".