Leisure Engagement and Travel Demand: Insights for Cultivating Toronto into a Lovable City
Bibliographic record
Abstract
The concept of a "lovable city," extending beyond the notion of a livable city, has emerged as a new paradigm for urban development, emphasizing identity, joy, and belonging. Traditional assessments of urban livability often focus on infrastructure, lacking integrating evidence of human behaviour or underlying needs. There is also a noticeable gap in activity-travel behaviour studies in capturing heterogeneity and dynamics across populations, time, and space, as well as spatial interdependencies within urban form, the motivations behind behaviours, and their implications for urban systems. This thesis bridges these gaps by proposing a comprehensive framework to evaluate leisure activity-travel demand and supply and assess their implications for building lovable cities – urban environments that holistically satisfy fundamental and higher-order human needs. The analysis focuses on three interconnected themes: (1) Motivations driving leisure behaviour, analyzed through the relationship between time use and well-being across populations; (2) Temporal rhythms of leisure participation shaped by work modalities and pre- to post-pandemic shifts; and (3) Spatial satisfaction of leisure demand through green spaces, entertainment, and long-distance travel. Using time-use surveys, activity-travel diaries, land use and business data, and census records, the dissertation investigates these themes across scales, from neighborhood amenities to regional tourism. Methodologically, it combines behavioural modelling (e.g., discrete choice, count data models), spatial analysis (e.g., 2SFCA, GWR), segmentation and structural modelling (e.g., SEM, mgLCR), and machine learning (e.g., Graph Neural Networks), enabling a multiscale, multidimensional understanding of leisure demand, population heterogeneity, spatial dynamics, and system-level interactions. This research contributes to activity-travel behaviour, transportation and urban planning, urban form theory, sociology, and systems thinking by offering theoretical and empirical insights into the interplay of human needs, leisure demand, and urban provisions. It advances behavioural modelling and understanding of heterogeneous leisure motivations and temporal rhythms; reveals spatial heterogeneity, synergy, and competition; highlights demographic diversity and socio-spatial disparities; and proposes a holistic multiscale framework across human and urban systems, informing targeted and strategic planning and investment. By centering leisure activity in discussions of lovability, the findings provide actionable strategies to design cities that enhance inclusiveness, adaptability, and well-being amid emerging hybrid work norms and post-pandemic realities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".