Because the CTPP, and the ACS, is the lifeblood of transportation planning and modeling, Jarosz said that the transportation planning community is particularly sensitive to the potential drop in response that might accompany a voluntary ACS. From the travel modeling standpoint, the calculus is stark: Smaller sample sizes (as could occur under a voluntary ACS) would make the data unreliable, particularly at the relatively fine TAZ level of geographic aggregation. Smaller sample size would necessitate more data suppression (to protect privacy) and unreliable results. It is as simple as “nothing in, nothing out”—even if there existed a perfect model for predicting travel flows and modes, using unreliable data in a perfect model would still produce undesirable results. She said that these models, and these data, are being used to plan billions of dollars in transportation infrastructure nationwide, which argues for obtaining the best data available to spend those funds wisely. (In the discussion following the presentations, Jarosz noted her strong approval with a comment that a voluntary ACS might compromise the representativeness of the ACS sample, and that this was at least as harmful as a straight reduction in sample size that might result from a switch to voluntary methods.)

Through other work for SANDAG, Jarosz said that data users have come to expect the level of small-area detail that the ACS has been able to provide. The basic socioeconomic variables on the ACS—income and poverty, race and Hispanic origin, and age—are strengths of the data; the ACS questions that permit estimation of disability status have also been valuable for regional planning purposes. Foreshadowing a theme that would be addressed in more detail in the next presentation, Jarosz said that data users have come to expect quality small-area data on questions like the primary language spoken at home; for instance, she has been asked by transit authorities to detail the languages spoken within a half-mile radius of a particular transit stop because they need to produce documents and signage for people who might be affected by a service change. Potentially smaller sample sizes would make it harder to identify very tiny language “clusters” and address community needs. High-quality analysis and planning depends on high-quality data as the input, and that would argue for (if anything) an expansion of the sample rather than a contraction.

2–E MEETING LANGUAGE IMPLEMENTATION REQUIREMENTS IN PUBLIC TRANSIT IN HARRIS COUNTY, TEXAS

In her presentation, Jarosz briefly mentioned the important use of ACS data in establishing compliance with federal, state, and local law and guidelines on environmental justice, social equity, and public access to services. This theme was carried forward by Vincent Sanders, lead transportation systems planner for the Metropolitan Transit Authority of Harris County, Texas (hereafter, METRO),



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement