Esri Demographics

Compare Places Dashboard: Review the Most Recent Census 2020 Disclosure Avoidance System (v2022-03-16)

The US Census Bureau has been working diligently to refine the Disclosure Avoidance System (DAS) for the Census 2020 data releases. The DAS is an essential component of this decade’s census data releases because the Census Bureau is required by law to protect individual privacy under Title 13 of the United States Code. The DAS represents a compromise between accurate data and privacy protection. In prior censuses the Census Bureau used various forms of disclosure avoidance. These techniques historically consisted of table suppression and data swapping. Given the risk of a “reconstruction attack,” Census 2020 applies a more modern formal statistical approach of disclosure avoidance called differential privacy. Redistricting data (P.L. 94-171) from Census 2020 was released on August 12, 2021. However, DAS experimentation continues for the Census Bureau’s next product, the Demographic and Housing Characteristics (DHC) file, which is the Census 2020 partial replacement for Summary File 1 (SF1). The DHC, like the SF1, will contain more characteristics and crosstabulations. Data such as age by sex by race and household type will be included in the DHC. Refer to this blog for more information on the move to differential privacy.

Demonstration Data

To better understand what differentially privatized data look like, the Census Bureau has released a series of demonstration products that compare published Census 2010 data with incremental versions of the Census 2020 DAS applied to Census 2010 data. This allows for a comparison of the differences between the old method of data swapping and the new method of differential privacy. This comparison allows data users to test the Census 2020 DAS for usability in various workflows. The latest version of the DAS has a Privacy-Loss Budget (PLB) of 20.82 for the person tables and 22.77 for the housing tables. This version was published on March 16, 2022, and contains data planned for inclusion in the DHC. To make the differentially privatized Census 2010 demonstration data more accessible and easier to use, IPUMS NHGIS have processed and tabulated the released data for 20 geographic levels.

Compare Places

Esri used the latest version of Census demonstration data to analyze the differences between the demonstration and released data sets at the place level. Places are easy to interact with as we are all familiar with our general local areas, but not many people know what tract or block group they live in. There are 29,261 places in the Census 2010 places inventory. This includes 9,721 Census Designated Places (CDPs) and 19,540 incorporated jurisdictions. Use this application to better understand places that you are familiar with.

Are the differences between the latest version of demonstration data (DP) and the released Census 2010 data (SF1) acceptable for your use case?

Click the image below to launch the Dashboard

Differences across datasets

There are many ways to explore the differences between the two datasets. As you navigate through the application it is helpful to have some context as to how the differences in your places of interest compare to the typical differences among all places. For this context refer to the descriptive statistics below.

Descriptive Statistics For All 2010 Places

Differences (DP – SF1) Average Min Median Max SD Min %* Median %* Max %* MALPD MAPD
Households 0.0 -94 0 114 7.8 -90.0% 0.0% 600.0% -0.1% 2.7%
Family Households 3.1 -252 1 2,837 36.7 -88.9% 0.1% 766.7% 0.9% 5.1%
Household Population -0.7 -289 0 422 13.8 -75.0% 0.0% 412.5% 0.1% 0.9%
Population 18+ -0.2 -1,521 0 386 19.2 -80.0% 0.0% 300.0% 0.0% 1.1%
Median Age -0.1 -63.0 0.0 88.5 2.8 -90.2% 0.0% 148.9% 0.0% 2.9%
White -2.1 -452 -1 140 14.5 -90.6% 0.0% 700.0% -0.5% 2.1%
Black or African American 0.8 -117 0 313 10.2 -94.7% 0.0% 2000.0% 5.9% 30.4%
Amer Indian or AK Native 0.1 -104 0 96 3.9 -90.0% 0.0% 900.0% 6.0% 32.1%
Asian 0.4 -84 0 290 7.2 -94.7% 0.0% 2700.0% 8.7% 35.2%
Pacific Islander 0.0 -104 0 117 3.3 -93.3% 0.0% 1700.0% 7.2% 30.8%
Other 0.6 -105 0 256 9.3 -96.0% 0.0% 2100.0% 8.4% 34.1%
Two or More Races -0.6 -445 0 325 12.9 -94.7% 0.0% 1900.0% 21.3% 41.2%
Hispanic 0.7 -213 0 881 15.5 -94.7% 0.0% 1200.0% 10.1% 28.2%
Non-Hispanic -1.4 -887 0 231 16.6 -90.0% 0.0% 300.0% -0.1% 1.6%
Persons Per Household 0.0 -3.5 0.0 27.8 0.4 N/A N/A N/A 0.7% 3.2%
Occupancy* -0.1% -100.0% 0.0% 100.0% 4.7% N/A N/A N/A -0.1% 2.7%
Sex Ratio 0.0 -6.3 0.0 18.5 0.3 N/A N/A N/A 1.3% 6.1%

MALPD = Mean Algebraic Percent Difference, a measure of bias
MAPD = Mean Absolute Percent Difference, a measure of average percent difference

SD = Standard Deviation
* These calculations are limited to cases where DP values are > 0 and SF1 values are > 0

Looking at these descriptive statistics we can see some patterns in how the proposed DAS impacts the data:

Data Integrity

Many statistical tables from census data yield information on the interaction between people and housing (e.g., persons per household, household type, occupancy, etc.). However, differential privacy is applied separately to person-level counts and housing counts. This poses the additional challenge of data integrity across the person and housing universes. The DAS attempts to maintain integrity through various forms of post-processing, a second step of the DAS after the formal privacy protection has been applied. Faults in data integrity can be broken out into two types: impossible and improbable. For example, a place cannot have more households than household population because, by definition, a household is occupied by at least one person. Improbable results are possible but highly unlikely. For example, a place could include all population under 18 years of age if the place only contains a juvenile facility and the caretakers do not live at the facility full time. This scenario, although possible, is highly improbable, and these cases should be scrutinized.

Checks on Data Integrity

DP Data Integrity Problems – Impossible Place Count Percent
More households than household population 28 0.10%
Household population > 0 but households = 0 25 0.09%
Households > 0 but household population = 0 6 0.02%
DP Data Integrity Problems – Improbable Place Count Percent
All population under 18 years of age 3 0.01%
Persons per household greater than 10 20 0.07%
At least 5 children under age 5 and no women age 18 through 44 3 0.01%
Median age significantly different (equal or greater than 20 years) between men and women 439 1.50%
DP occupancy rate is 100% but SF1 occupancy rate is not 100% 408 1.39%
SF1 occupancy rate is 100% but DP occupancy rate is not 100% 48 0.16%
DP occupancy rate is 0% but SF1 occupancy rate is not 0% 23 0.08%
SF1 occupancy rate is 0% but DP occupancy rate is not 0% 7 0.02%
DP household population = 0 but SF1 household population > 0 2 0.01%
SF1 household population = 0 but DP household population > 0 5 0.02%

When applying the same tests to the released 2010 SF1 data only one place has persons per household greater than 10 while only 99 have large (at least 20 years) differences in median age between males and females. All other counts equal zero for the above data integrity tests; no impossible occurrences are present in the SF1 data as originally published.

This is a limited analysis of a subset of the demonstration data. These differences represent the trade-off between privacy protection and accurate data. We encourage you to perform your own analysis of the demonstration data to test your use cases.


NOTE: This article is an update to an earlier blog with a new dashboard and statistics that reflect the most recent version of the Census 2010 demonstration files.

About the authors

Kyle R. Cassal, Chief Demographer at Esri, is the lead developer for Esri’s Data Development team. His team is responsible for producing independent demographic and socioeconomic updates and forecasts for the United States. These data are leveraged across the Esri platform through web maps, infographics, data enrichment and custom applications including Business Analyst and the Living Atlas of the World. In addition to processing US Census and ACS data, his team produces unique and innovative databases such as Tapestry Segmentation, Consumer Spending and Market Potential which are now industry benchmarks.


Alex is a demographic and economic analyst on Esri's Data Development team. This team's economists, statisticians, demographers, geographers, and analysts produce independent small-area demographic and socioeconomic estimates and forecasts for the United States. The team develops exclusive demographic models and methodologies to create market-proven datasets, many of which are now industry benchmarks such as Tapestry Segmentation, Consumer Spending, Market Potential, and annual Updated Demographics.


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