What makes people satisfied with their Neighbourhoods? Exploring individual characteristics beyond sociodemographics in Scarborough, Ontario
Bibliographic record
Abstract
Neighbourhood satisfaction is a key topic in urban planning due to its impact on well-being and inequality among urban dwellers. While determinants of neighbourhood satisfaction have been studied extensively, less is known about individual characteristics such as travel behaviour, political values, transport barriers, and aspirations, beyond traditional sociodemographics. Additionally, spatial modelling of neighbourhood satisfaction remains underexplored. This study utilizes the Scarborough Survey, a multidimensional dataset from Scarborough, Ontario, to investigate how travel behaviour, political values, transport barriers, and aspirations influence neighbourhood satisfaction. A spatial ordinal probit model was used, accounting for sociodemographics, subjective neighbourhood characteristics, and objective neighbourhood characteristics. Findings reveal significant impacts of these individual characteristics on neighbourhood satisfaction, with sociodemographics' effects mediated through these variables. Interestingly, no positive spatial autocorrelation was found for neighbourhood satisfaction after controlling for other factors, suggesting limited social bonds or interactions among neighbours in suburban areas. The results highlight opportunities for community events or local organizations to rebuild these connections and enhance satisfaction in suburban neighbourhoods. This work provides new insights into neighbourhood satisfaction and offers pathways for improving the living conditions of vulnerable suburban communities. • A spatial ordinal probit model was applied to assess neighbourhood satisfaction. • Individual characteristics play a crucial role in neighbourhood satisfaction analysis. • Suburban areas of Toronto exhibit limited social bonds among neighbours. • Reducing travel barriers may enhance overall neighbourhood satisfaction. • Car owners and non-owners display distinct preferences for neighbourhood amenities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".