What struck me about the session--and in particular the discussion afterwards--is that we're getting to the point as a community of practitioners where data and analytical capabilities are no longer the barriers to implementation they once were. And so now we have the chance to really talk about which measures are most instructive--to researchers, policy makers, the public--as opposed to which are most simply possible.
Do we want aggregate indicators that take into account the needs of many different population groups? Or do we want individual analyses reproduced over and over again for small scale market segments of the population, with each telling a more specific but more compelling story? Do we want to try to match individuals to their specific needs (e.g. low wage health workers to low-wage healthcare jobs), or should we opt for more generalized but also easier to understand aggregate measures (e.g. total job accessibility from residential areas)?
One presentation even surprised me with a new twist on some very basic concepts. We all know the burden of transportation comes from actual out-of-pocket costs and from time costs. Nevertheless, most accessibility analyses are pivoted off of travel times, alone. Researchers from McGill (Presentation 16-3715) demonstrated how simply adding fare cost to travel time information can yield a considerably different picture of job accessibility:
A, on the left: job accessibility using a "1 hourly wage" threshold (a combination of fare costs and monetized travel time, using minimum wage hourly rates); B, on the right: simple 1 hr travel time thresholds. (Montreal).