Walkability for Women and the 15- minute City Framework: The STEP UP Project
Bibliographic record
Abstract
Advanced urban and transport planning activities are shifting towards sustainable urban mobility solutions and walkability (Buhrmann, Wefering and Rupprecht, 2019), namely referring to how friendly the urban environment is for walking in terms of proximity service availability (i.e., 15-min city), street connectivity, comfort of public spaces, and road safety. Although traditional approaches tend to focus on the spatial dimension, individual characteristics of city users are found to have a significant impact on the perceived level of walkability. In particular, the measures currently in place do not sufficiently consider population groups in vulnerable situations (i.e., SDG 11.2-Sustainable Transport for All) (United Nations, 2016), including women. The research project ‘STEP UP - Walkability for Women in Milan' (awarded by Fondazione Cariplo under the call “INEQUALITIES RESEARCH” - Grant No. 2022-1643, focuses on the needs and expectations of women while walking. As highlighted by Golan et al. (2019), women experience the city differently than men, in part because they are more concerned with security issues related to aggression and harassment. These constraints take the form of precautionary or avoidance behaviors due to fear of violence, perception of risk, and sense of vulnerability, as a major inhibitor of mobility for women in public spaces especially at nighttime. STEP UP aims to assess the level of walkability for women focusing on the case study of Milan, Italy. First, a thematic literature review will be conducted on the most relevant scientific contributions and policy guidelines about this topic. The results of the literature review will be exploited to select a series of relevant geolocated datasets, which will be retrieved, sorted, and filtered from open data repositories and geoportals. Data regarding the perceived level of safety of women while walking will be collected through ‘Wher' - a route planner application operated by Walk21 Foundation. All these gender-disaggregated data sets will be analyzed through GIS-Geographic Information Systems to design a multi-layer map of Milan focused on several walkability criteria, which will be then validated through survey questionnaires and focus groups. The results of the project will help to identify challenging areas or neighborhoods in the city of Milan, which can serve as samples of analysis to develop a set of policy recommendations aimed at enhancing the level of walkability for women in cities. References Buhrmann, S., Wefering, F., Rupprecht, S. (2019). Guidelines for Developing and implementing a sustainable urban mobility plan – 2nd edition. Rupprecht Consult-Forschung und Beratung GmbH. Available at: https://www.eltis.org/mobility-plans/sump-guidelines Golan, Y., Wilkinson, N., Henderson, J.M., and Weverka, A. (2019). Gendered walkability: Building a daytime walkability index for women. Journal of Transport and Land Use, 12(1). https://doi.org/10.5198/jtlu.2019.1472 United Nations (2016). Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations Secretariat. Available at: https://sdgs.un.org/2030agenda
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".