Household Income Composition Changes with Rapid Transit Implementation: A Natural Experiment Study of SkyTrain, Metro Vancouver, 1981-2016
Bibliographic record
Abstract
Background—Rapid transit such as SkyTrain is beneficial to move people efficiently, reduce carbon emissions, and increase physical activity. However, these benefits attract new development resulting in rising housing prices that may consequently change the household income composition. Metro Vancouver has not skirted this phenomenon, with rapid population growth and signs of neighbourhood change near SkyTrain.\n \nResearch Question—Does the household income composition change in areas nearby new SkyTrain stations?\n \nHypothesis—After a new SkyTrain station opens, lower income households may initially have better access to rapid transit, but over time nearby areas shift towards higher income households. Methods and Procedures—This natural experiment study uses census data for Metro Vancouver census tracts (CTs) 1981–2016. Household income composition is measured using relative share of households (location quotient (LQ)) in three income categories. Exposed areas are within 1.6 km (20-min walk) of SkyTrain stations compared to the rest of the region. Spatial analysis visualizes geographic distributions using ArcGIS, and statistical analysis tests concepts with linear mixed effects models using R software.\n \nResults—The study assesses 374 CTs in 17 municipalities and finds areas nearby new SkyTrain stations start with a larger relative share of lower income households at baseline (1981) but shift towards more affluence over time. The areas exposed to SkyTrain changed in relative share of households faster than unexposed areas by LQ= -0.024, -0.012, and 0.026 more for very low, lower, and high income households, respectively, per census year (every five years). This means the relative share of each income group changed by 1–3% more in exposed areas than unexposed areas over every five-year period or a total change of 8–18% more over the entire study period.\n \nConclusions—Future planning must consider SkyTrain does impact who lives in areas nearby and options to protect lower income housing with access to transit are needed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".