| ArcGIS REST Services Directory |
| Home > services > DP_v2_20200527 (FeatureServer) | | API Reference |
Version/Sprint 2 (May 2020)
Census 2020 brings a new era of disclosure avoidance with the implementation of differential privacy. Differential privacy is a “formal privacy” approach that provides proven mathematical privacy assurances by adding uncertainty or “noise” to the released data. This technique determines the amount of noise necessary to balance privacy loss and accuracy via mathematical formulas. To better prepare data users for this shift, the Census Bureau has released 2010 Demonstration Data Products that provide the public with a sneak peek at what the 2010 raw data would look like after being pushed through the differential privacy system that is under development.
These layers contain select critical variables for both the original Census 2010 SF1 and Census 2010 with differential privacy applied. This version of the demonstration products was released on 05-27-2020. Age groups have been collapsed into five-year groupings that represent the total population (males and females). Fields with a “dp_” prefix indicate values from the differentially privatized data, and the “sf_” prefix indicates values from the original SF1 release.
Data Dictionary can be found here
Data are shown in Census 2010 boundaries for the following geographies:
· States
· Counties
· County Subdivisions
· Tracts
· Block Groups
· Places
· Congressional Districts 110th-112th (CD)
· American Indian Areas (AIA)
Census Geography are 2010 TIGER/Line Shapefiles
Tabulated data was obtained from IPUMS NHGIS. David Van Riper, Tracy Kugler,
and Jonathan Schroeder. IPUMS NHGIS Privacy-Protected 2010 Census Demonstration
Data, version 20200527 [Database]. Minneapolis, MN: IPUMS. 2020.
https://www.nhgis.org/privacy-protected-demonstration-data
Visit the Census Bureau’s website to learn more about the implementation and ongoing development of the differential privacy system for future Census Bureau data releases.
Read this Esri blog for more information on how Esri is helping data users prepare for the impacts of differential privacy.