A person searching through the massive expanse of data collected by the U.S. Census Bureau in search of details about a specific neighborhood may increasingly find statistics with colossal margins of error, such as an average income of $50,000 plus or minus $50,000.听
A geographer at the 麻豆视频, one of eight nodes of the National Science Foundation鈥檚 newly created Census Research Network, has been granted a five-year $1.4 million grant to see if he can change that.
Assistant Professor Seth Spielman, director of the CU-Boulder Census Research Node and a researcher at CU-Boulder's Institute for Behavioral Science, said the margin of error for neighborhood-level information collected by the U.S. Census Bureau hasn鈥檛 always been so dismal. The quality of the data from the American Community Survey 鈥� the portion of the census that asks residents about their age, household makeup, education levels and income, among other facts 鈥� has been limited by the Census Bureau鈥檚 budget and rigid census-reporting boundaries that have not changed in more than half a century, Spielman said.
The erosion of high-quality data affects a range of social services, since funding for those programs is often linked to Census Bureau data for metrics such as poverty.
Spielman thinks the key to reducing the margin of error lies in redefining how the boundaries are drawn around neighborhoods so that more similar people are grouped together. But to do that, he needs to dive into the highly secured data that actually bundles together information for individuals, including where they live, their race, how much they make and how many children they have.
鈥淲e want to understand what neighborhoods look like, and we think that by using individual-level data and computer algorithms we can redraw neighborhoods and get a more precise picture,鈥� Spielman said.
Census data on communities currently is available for small regions known as census tracts. When these groupings were originally made, in the 1960s, they were designed by local committees to delineate similar sections of cities so that individual neighborhoods could be studied. But as the decades have rolled by, the makeup of many of the census tracts has changed, and now some tracts encompass parts of multiple, widely varying neighborhoods. The disparity within the tracts, and the fact that fewer people are now being sampled in each tract, has inflated the margins of error.
Spielman is now using CU-Boulder鈥檚 Janus supercomputer to test an algorithm that will allow for computer-assisted redrawing of neighborhood lines in the United States. Spielman doesn鈥檛 propose that the old census tract lines be discarded, since it鈥檚 important that tracts can continue to be compared over time. But the new neighborhood lines might give people a more reliable way to understand what鈥檚 going on inside a city.
The algorithm is still a work in progress. Spielman and David Folch, a postdoctoral researcher at CU-Boulder鈥檚 Institute for Behavioral Science, are using the supercomputer to comb the ocean of government data for areas in which people are the most similar. Those similarities could include everything from race to family size to whether an individual commutes by bike or is a veteran.听
鈥淗owever we group things together, the best grouping is the grouping that results in a neighborhood that has the highest level of similarity,鈥� Spielman said. 鈥淔or all the variables, we just want to maximize how similar the neighborhoods are.鈥�
Once the algorithm is finished, Spielman will apply it to individual-level data stored on secure servers in Washington, D.C. The resulting neighborhoods, however they may look, would not provide individual-level data to the public.
Spielman is collaborating on the project with Nicholas Nagle, assistant professor of geography at the University of Tennessee Knoxville. The seven other nodes of the NSF Census Research Network are at Carnegie Mellon University, Cornell University, Duke University, University of Michigan Ann Arbor, University of Missouri Columbia, University of Nebraska Lincoln and Northwestern University.
Contact:
Seth Spielman, 303-492-4877
Seth.Spielman@colorado.edu
Laura Snider, CU media relations, 303-735-0528
Laura.Snider@colorado.edu
