Equity Implications of Shared Micro-Mobility in the Suburbs: A Spatial Analysis of Shared E-Scooter Use in Different Built and Social Environments
Bibliographic record
Abstract
Electric-powered standing kick scooters, otherwise known as e-scooters, have recently been introduced in hundreds of cities around the world as part of rent-to-use shared micro-mobility systems. Despite being a potentially sustainable and equitable travel mode, relatively little analysis has been done on the impact of shared micro-mobility on transportation equity, specifically in the suburban context. The shared e-scooter pilot program in Brampton, Ontario, Canada, presents an opportunity to examine this topic within the context of a Canadian suburban community. Our study explores the use of shared e-scooters (trips per sq km per day) in Brampton, in relationship to suburban built environment types and social and economic marginalization at the neighborhood level. First, a Getis-Ord G i * (local “hot spot”) analysis identified localized hot-spots of shared e-scooter demand and implied that, at least in some contexts, higher trip rates are concentrated in marginalized neighborhoods. Next, spatial regression analysis (spatial lag model) demonstrated associations between dimensions of marginalization and e-scooter use rate, where higher rates of shared e-scooter use were observed in neighborhoods that have higher household instability, and higher concentrations of racialized and immigrant populations. The findings also suggest that the benefits of shared e-scooters may not be different in neighborhoods with high concentration of economically marginalized population, or low labor force participation. In addition, no relationship was observed between built environment types and e-scooter use. Findings will inform future micro-mobility policy in North American suburban areas, while contributing to the growing body of work on e-scooter use and equity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".