Investigating the spatiotemporally heterogeneous effects of macro and micro built environment on sexual violence against women: A case study of Mumbai
Bibliographic record
Abstract
Abstract Sexual violence against women is a major threat to public safety, whereas a well-designed urban environment plays a crucial role in improving public safety and reducing crime. However, the spatiotemporal non-stationarity of the impacts of the macro-level Built Environment (BE) and micro-level Street Environment (SE) on such crimes has been underexplored. Taking Mumbai as a case study, this study employs the crime generator/detractor/facilitator theory to capture the criminogenic roles of land-use functions to describe macro-level BE, while using Street View Images (SVI) to quantify the micro-level SE. Notably, sexual violence against women is classified into four time periods, and Geographically Weighted Regression (GWR) models are developed to capture the spatial and temporal non-stationarity of criminal behavior. The results highlight the varying impacts of BE and SE variables on sexual violence and confirm their non-negligible and complementary roles. Specifically, maternity homes, casinos, cybercafes, and public toilets have been identified as potential hotspots for sexual violence. The complexity of street facades and the presence of retail stores and fire stations (which imply territoriality and surveillance) may contribute to reducing sexual violence. Moreover, the impacts of these variables on crime vary significantly between day and night, from urban centers to suburbs. These findings offer fine-grained insights for urban design and city management, providing decision-makers with evidence-based recommendations to create safer and more women-friendly public spaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".