What drives densification and sprawl in cities? A spatially explicit assessment for Vienna, between 1984 and 2018
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The spatial arrangement of settlements constitutes a long-lasting legacy and shapes the prospects for transformations toward sustainability. Thus, understanding the drivers of changes in settlement patterns is essential. In this article, we present a spatially explicit, geostatistical analysis of settlement dynamics, and a qualitative investigation of its regulative, demographic, and economic drivers, using the example of Vienna, Austria between 1984 and 2018. Combining spatially explicit metrics of urban sprawl and cluster analysis, we analyzed high-resolution maps of buildings, population, and jobs to identify distinct settlement trajectories. Societal drivers of more or less sprawled settlement dynamics are analyzed with desk research and expert interviews. We distinguish five types of settlement dynamics: persistently dense areas with increasing use intensity, re-densification of dense areas, persistently sprawled areas, redensification of sprawled areas, and persistently isolated buildings. Urban renewal schemes have fostered the re-densification of dense areas in response to population growth and urban economic restructuring. The combination of urban renewal schemes and green space policies has successfully limited urban expansion. Challenges arise from the demand for single-family housing and corresponding zoning regulations. These factors solidify existing sprawled settlements, posing obstacles to the efficient re-densification of such areas crucial for sustainable urban development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it