Mapping Urban Flood-Prone Areas’ Spatial Structure and Their Tendencies of Change: A Network Study for Brazil’s Porto Alegre Metropolitan Region
Bibliographic record
Abstract
Historically, the main cause of urban disasters in Brazil is flooding events, which are becoming more recurrent due to climate changes and intensive urbanization, causing extensive infrastructure, economic and life losses. The formation of Brazilian Metropolitan Areas goes back to the early twentieth century, with urban expansion following river basins, as regional transportation relied on inland navigation. The transition to road-based transport structured further urban sprawl from the mid-twentieth century onward, as road-circulation axes expanded across flood-prone areas. Mapping those hydrogeological risks is important to understand their effect on the existent road-circulation network structure cohesiveness. From the hydrogeological risk assessment data, this article evaluates potential changes imposed by extreme flood events on the road infrastructure at municipal and metropolitan scales. Space Syntax methods applied to an empirical case – the Porto Alegre Metropolitan Region – allow for comparative analyses between the urban network of current and flooding-event simulations and depict (a) the urban grids’ structural transformations under flooding, (b) the road elements at risk, and (c) the system’s spatial integrity and circulation disruptions. The resulting cartography can subside governance and urban planning strategies to cope with floodings at different territorial scales, addressing changes on local–regional circulation patterns, system breaking points and tendencies of urban land parcelling on vulnerable areas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".