Digital data to support urban planning processes to develop women safety cities: an application to the city of Naples
Bibliographic record
Abstract
Urban planning is increasingly focused on solutions for sustainable urban mobility, including the achievement of “walkability”, i.e. ease to walking, meeting criteria of neighbourhood services, street connectivity, comfort of public spaces, and others. Urban administrations in some countries, partially as a response to the Covid-19 pandemic, have adopted short and long-term plans for reassignment of vehicular space in favour of cyclist and pedestrian infrastructures, however traditional approaches to urban planning still fail to consider different categories of urban users in terms of their individual characteristics, which can significantly impact their perceptions of walkability for streets and public spaces. Women in particular face harassment, aggression and other safety concerns that can inhibit their mobility in streets and public spaces, especially when it gets dark. Despite robust research on other aspects of walkability in cities, there is a lack of knowledge regarding the intersections of mobility and gender. Addressing the need for further investment in qualitative, and particularly in quantitative analysis, the current contributions proposes and reports on the use of GIS-based methodology, with data collected directly from women in urban contexts, and from open-access location-based data, producing analyses that can support decision-making on policies for walkability. In particular, the contribution summarizes the first product of a new, replicable methodology, focused on urban planning and gender inclusion, applied to the city of Naples, Italy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".