Job-housing balance has been touted as a solution to reduce Vehicle-Miles Traveled. Additionally, Transit Oriented Development (TOD) and mixed-use development is on the rise in the Bay Area. A measure of these combined factors—job-housing balance and residential TOD development—is job containment, or the percentage of residents who also work in an area.
Job containment was measured from LEHD data as the percentage of residents who live within 1/2 mile of a BART station and also work within 1/2 mile of that BART station. We hypothesize that these individuals will rely on personal automobiles less often, and therefore stations with high contaminant rates will also have high active transportation (Walk, Bicycle, and Transit) mode shares. Our regression analysis, taking the natural log of the variable Job Containment, shows that job containment is a weak predict of active Transportation mode share, with an R-squared of 0.31.
Our analysis found that, compared to other station-area factors, job-housing balance alone is only a moderate predictor of active transportation mode share to BART stations. Active transportation is defined as those who access BART by walking, biking, or riding public transit. Stations with significantly higher Job containment levels did have very low SOV mode shares. For example, Embarcadero, Montgomery, Powell, Downtown Berkeley, and 12th Street Oakland, all have over 10% of the population living within 1/2 mile of the station also working within 1/2 mile of the station, and also have very high active transport mode shares. However, this relationship is probably due to the overall high job densities of these areas—which may be a better measurement factor to use rather than Job Containment.
In the visualization above, it is easy to quickly point out a few key locations of high job containment: Downtown Berkeley, Downtown Oakland, and San Francisco. These areas do have the highest active transport mode shares. However, there are other areas—such as Richmond and Ashby station—that have a surprising contrast between these two factors. These areas likely areas with high residential density, a measure that should not be ignored when considering station areas that are best suited to encourage active transport modes.