current MAF. This includes ties to 2010 census operational data—the Bu-reau’s knowledge of information added in the full Address Canvassing operation and late field operations like the Vacant/Delete Check as well as its knowledge of census mailings returned as “undeliverable as addressed.” An earlier National Research Council (2004a) panel noted that evaluating the MAF and suggesting operational improvements were severely complicated because the structure of the Bureau’s geographic sources did not readily allow for the unique contributions of individual operations (e.g., the Local Update of Census Addresses returns suggested by local and tribal governments or the regular refreshes from Postal Service data) to be disentangled and compared. Ideally, the 2020 MAF/TIGER structure is more amenable to reconstructing such operational histories for individual addresses or street features; accurate cost-benefit assessment of geographic support operations for 2020 depends vitally on the collection and analysis of these kinds of metadata.
The importance of vigorous, intensive analysis of the quality of MAF/TIGER cannot be overstated. It is tempting, but misguided, to minimize such work as simply clerical or as an exercise in fine-tuning cartographic accuracy. Spatial data quality is inextricably linked to census quality and, to the greatest extent possible, both the spatial data in MAF/TIGER and census operational data demand study at fine-grained geographic levels, not just national or other high-level aggregates. Phraseology that we invoked above is applicable here: analysis is a key first step, no matter how imprecise the source data might be or how caveated results must be. Small-scale field collection of GPS readings and independent listings may not generalize well, but modeling and small-area estimation approaches could usefully be introduced; perhaps a spatial data quality estimate for every small county is infeasible, but an estimate for “places like us” (collections of places that are similar by demographic characteristics or other stratification variables) could still usefully steer geographic updating resources. An earlier National Research Council (2009:119–128) report discussed a framework for modeling census quality using both MAF/TIGER and census operational data as inputs, and that work may suggest possible directions.
Related to another core research area, another priority for geographic work is to prepare for the possible use of administrative records data in geographic update operations. In addition to a person-level data file, the Bureau’s current StARS system also generates a listing of addresses, dubbed the Master Housing File (MHF). Just as current work with StARS on the person-level side has largely been limited to looking at gross counts, so too has the utility of the MHF as an update source for—or quality check of—the MAF/TIGER databases been largely unexplored to date. Bureau staff attempt to use the TIGER database to geocode the MHF—associate each address with a specific geographic code—but to date has not delved deeply