Meeting Redistricting Data Requirements: Accuracy Targets
Last year, the Census Bureau’s Disclosure Avoidance System (DAS) Team made a number of important improvements to the TopDown Algorithm (TDA) that will be used to protect the privacy of our respondents’ data in the P.L. 94-171 redistricting data product. As we have shown in our most recent set of demonstration data, those algorithmic improvements have substantially improved the accuracy of the resulting data, independent of the selection and allocation of the privacy-loss budget (PLB). As we explained in a recent newsletter, we have recently been turning our attention to optimizing and tuning the parameters of the algorithm to further improve accuracy.
The parameters of the TDA can be varied in a number of ways: query strategy, allocation of PLB across geographic levels, allocation of PLB across queries, and optimization of geographic post-processing to improve accuracy of the data for “off-spine” geographic entities. Determining the optimal settings for these parameters requires empirically evaluating large numbers of TDA runs against objective accuracy metrics.
Working with the Redistricting Community to Meet Data Requirements
For the P.L. 94-171 redistricting data product, the principal statutory use cases are the redistricting process and the U.S. Department of Justice’s enforcement of the Voting Rights Act of 1965 (VRA). To facilitate this analysis, the Department of Justice supplied sample redistricting and VRA use cases for the Census Bureau to evaluate against.
Based on these use cases and additional feedback, we created an accuracy target to ensure that the largest racial or ethnic group in any geography entity with a total population of at least 500 people is accurate to within 5 percentage points of their enumerated value at least 95% of the time.
Because the redistricting and VRA use cases rely on geographic aggregations that cannot be prespecified (e.g., congressional districts that will be drawn after the data are published), for evaluation purposes the DAS Team used three already specified geographic constructs that resemble the size and composition of voting districts that will eventually be drawn: block groups (which are on the TDA geographic spine), census designated places (which are “off-spine”), and a customized set of off-spine entities that distinguished between strong minor civil division states and other states. The customized off-spine entities are similar to census designated places.
Because these accuracy targets are expressed in relative shares of the total population, tuning the TDA for accuracy of the racial/ethnic group’s share also tunes the algorithm for corresponding accuracy of the total population of those geographies.
The Census Bureau is still evaluating the empirical results of these experimental runs, but we look forward to sharing these results and how they will inform the parameter settings used for our next set of demonstration data in our next newsletter.
2021 Key Dates, Redistricting (P.L. 94-171) Data Product:
By April 30:
- Census Bureau releases new Privacy-Protected Microdata Files (PPMFs) and Detailed Summary Metrics.
- Two versions: Candidate strategy run using new PLB and old PLB.
By late May:
- Data users submit feedback.
Early June:
- The Census Bureau’s Data Stewardship Executive Policy (DSEP) Committee makes final determination of PLB, system parameters based on data user feedback for P.L. 94-171.
Late June:
- Final DAS production run and quality control analysis begins for P.L. 94-171 data.
By late August:
- Release 2020 Census P.L. 94-171 data as Legacy Format Summary File*.
September:
- Census Bureau releases PPMFs and Detailed Summary Metrics from applying the production version of the DAS to the 2010 Census data.
- Census Bureau releases production code base for P.L. 94-171 redistricting summary data file and related technical papers.
By September 30:
- Release 2020 Census P.L. 94-171 data** and Differential Privacy Handbook.
* Released via Census Bureau FTP site.
** Released via data.census.gov.
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