Spatializing sexual and gender-based violence, gendered mobilities, and the built environment
Bibliographic record
Abstract
Sexual and gender-based violence (SGBV) significantly constrains women’s and gender diverse individuals’ mobilities, especially in urban environments where perceived and actual risks shape access to public spaces, transportation, and community engagement. This scoping review synthesizes how mobility-based SGBV has been identified, mapped, and analyzed within the built environment using geographic information systems (GIS). Drawing on established scoping review frameworks, we systematically searched Google Scholar, Web of Science, and Geobase, identifying 316 relevant studies. Our findings are organized into four thematic approaches: (1) innovative and emerging technologies; (2) mainstream spatial analysis and crime mapping; (3) quantitative and mixed methods approaches; and (4) perception-based qualitative mixed methods. We identity a persistent disconnection between technological solutions (e.g. safety apps) and spatial analyses grounded in urban planning, alongside a broader gap between feminist-informed methodologies and dominant GIS practices. While GIS-based crime mapping offers valuable spatial insights, it often omits participatory and feminist-informed perspectives that better account for lived experiences of mobility injustice. We thus propose ‘feminist spatial participatory action research’ as a methodological orientation that integrates participatory mapping, qualitative GIS, and spatial analysis. We argue this approach advances interdisciplinary, survivor-centered mobilities research and offers a holistic foundation for addressing SGBV through inclusive spatial interventions.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".