U.S. Census tract boundaries: how to solve an ever-changing challenge

In a previous article we explained ICE's rigorous downscaling methodology for spatially defined data from U.S. Government entities like the U.S. Census, the Federal Emergency Management Agency, the Bureau of Labor Statistics, and the Environmental Protection Agency. As that article articulated, in the municipal bond context, this approach is valuable for understanding the characteristics of issuers, because once appropriately downscaled, data can be re-aggregated up to any geospatial boundary, including but not limited to any city, school district, turnpike authority, or even any area within a 30-minute drive time from a hospital.

However, ICE's downscaling and re-aggregation approach also has broader implications for anyone interested in U.S. Census-provided population characteristics at the individual community level. As explained above, ICE can downscale and re-aggregate census data from any year to any arbitrary boundary-including census-defined boundaries from other years.

ICE's methodology of downscaling data onto a 100 meter x 100 meter grid with intelligent weighting schemes and then re-aggregating it up to new boundaries simplifies comparisons of census information across different years (Figure 2). Changes in census data over time, like the average owned housing value by tract in Williamson County between 2017 and 2020, can be examined based on fixed boundaries-whether those are defined by the 2017 census tracts, the 2020 census tracts, or census tracts from any other year.