Unequal Spaces: Suburbs, Apartments, and Bicycles in a Global City
Bibliographic record
Abstract
Planners promote utilitarian cycling and urban intensification to increase the health and sustainability of urban populations. North American planners and researchers focus on cycling supports in urban cores. There is little research on suburban cycling and virtually none on the relationship between cycling and housing type even though the relationship between cycling and population density is unclear. Yet, in the City of Toronto most people live in suburban neighbourhoods (68%) and 55% of Toronto’s population also lives in apartment style housing. Sixty percent of trips <5km, a distance easily cycled in 20 minutes, originate in Toronto’s inner-ring suburbs. To effectively combat the ills of automobiles, residents in suburban areas and apartment housing must be considered. My research addresses knowledge gaps related to these seemingly unrelated areas. My thinking is based in social practice theory. A scoping review of social practice theory and urban transport revealed how a new theoretical approach provides fresh strategies for change. Then, using data from an original survey of residents (n=215), principal component analysis and generalized structural equation modeling were used to develop a model of factors influencing utilitarian cycling by residents of a lower-income auto-oriented suburb. Results suggest, recreational cycling, access to bicycles, group rides, repairs, and ways to meet people who bike may be useful interventions in suburban communities. Finally, using trip data (n=223,232) from the Transportation Tomorrow Survey and controlling for 25 variables, multinomial logistic regression was used to examine the relationship between density, dwelling type and utilitarian cycling. In Toronto, dwelling type is almost as important as gender in determining who cycles. A trip originating from an apartment-based household was less than half as likely to be taken by bicycle as a similar trip originating in a house-based household in 2011 after controlling for all known variables of influence. This work points to new ways to increase sustainable travel in suburban areas and maximize its use in dense urban areas, both of which are necessary to mitigate climate change and build equitable access to healthy transportation options.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| 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".