Relating social, ecological, and technological vulnerability to future flood exposure at two spatial scales in four U.S. cities
Bibliographic record
Abstract
Flooding in cities occurs at different spatial scales and produces uneven effects on populations, infrastructure, and urban systems. In order to improve understanding of social, ecological, and technological systems (SETS) vulnerability to urban flooding, there is a need for fine spatial scale flood exposure data. In this study, we related two measures of future flood exposure—the First Street Foundation’s Flood Factor and the change in chance of extreme flood exposure by the year 2050—to SETS indicators at the parcel and census block group (CBG) scales in Portland, OR, Phoenix, AZ, Baltimore, MD, and Atlanta, GA. We used ordinary least squares and Spearman’s rank, spatial lag, and spatial error regression models to explore the relationships between variables and for spatial bias. Flood exposure was significantly correlated with redlining in three study cities at the parcel scale, yet the relationships were inconsistent; at the CBG scale, significant relationships were less common and inconsistent in their cardinality. This study indicates inequalities in flood exposure and vulnerability may continue, and possibly worsen, unless mitigating measures are taken in the immediate or near future. It also indicates that, when available, parcel-scale SETS indicators are superior to indicators at coarser scales for estimating flood vulnerability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".