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censusdifferential privacydisclosure avoidanceethnicitygroup quartershispanichousinghousing unitslatinonoisy measurementspopulationraceredistrictingvoting age
The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-06-30) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Char...
censusdifferential privacydisclosure avoidanceethnicitygroup quartershousinghousing unitsnoisy measurementspopulationraceredistrictingvoting age
The 2020 Census Demographic and Housing Characteristics Noisy Measurement File is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022], and implemented in primitives.py). The 2020 Census Demographic and Housing Characteristics Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] ), which added positive or negative integer-valued noise to each of the resulting counts. These ar...
censusclassificationcogdisaster responseearth observationgeosciencegeospatialglobalmappingplanetarypopulationtifftiles
This product contains the spatial raster dataset grids from 1975 to 2100 at 5 years interval representing the distribution of human population, expressed as the number of people per cell. Residential population estimates at 5 years interval between 1975 and 2020 are derived from the raw global census data harmonized by CIESIN for the Gridded Population of the World (GPWv4.11) combined with population trends obtained from the Urban Agglomeration time series of the UN World Urbanisation Prospects 2018 (UN WUP 2018 – F22). The 2025-2100 population projections are computed following the 2000-2020 ...
censusclassificationcogdisaster responseearth observationgeosciencegeospatialglobalmappingplanetarypopulationtifftiles
The spatial raster dataset depicts the distribution of residential population, expressed as the number of people per cell. Residential population estimates between 1975 and 2020 in 5-year intervals and projections to 2025 and 2030 derived from CIESIN GPWv4.11 were disaggregated from census or administrative units to grid cells, informed by the distribution, volume, and classification of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.
censuscitiesclassificationcogdisaster responseearth observationgeosciencegeospatialgloballand usemappingplanetarypopulationtifftilesurban
The layers present the application of the Degree of Urbanisation stage I methodology recommended by UN Statistical Commission to the global population grid generated by the JRC in the epochs 1975-2030 (5 years timestep). They have been generated by integration of built-up surface extracted from Landsat and Sentinel-2 image data processing (GHS-BUILT-S R2023), and population data derived from the CIESIN GPW v4.11 (GHS-POP R2023). This product (v2) is an update of the data released in 2023 (v1) based on GHS-BUILT-S, GHS-POP and uses an updated definition of Semi-Dennse Urban Clusters. The Settle...
censuscitiesclassificationcogdisaster responseearth observationgeosciencegeospatialgloballand usemappingplanetarypopulationtifftilesurban
This product contains the spatial raster dataset grids from 1975 to 2100 at 5 years interval representing the settlement classification per grid cell and, for the year 2025, the vector layers of the delineated boundaries of settlement entities (i.e. urban centres, UC, dense urban clusters, DUC, semi-dense urban clusters, SDUC, and rural clusters, RC) with main attributes, in vector files (country, area, population, built-up surface and population weighted centroid) and the list of country capitals with reference to the entity IDs.
censuscsvdisaster responseenvironmentalgeospatialjsonparquetsustainabilitywater
A normalized, address- and ZIP-level compilation of US public environmental health records: machine-parsed Consumer Confidence Reports (drinking water contaminant tables, including PFAS, for 9,700+ water systems, 2023-2024), lead service line records aggregated from 94 state and utility inventories (18.4M address-level records), a ZIP-level summary layer covering 41,000+ ZIP codes across 17 environmental risk categories, and companion layers built from EPA SDWIS, UCMR5, FEMA NRI, USGS, CDC, and Census sources. All records are machine-readable, carry per-record source provenance (source documen...
1940 censusarchivescensusdemographynara
The 1940 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1940, although some persons were missed. The 1940 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 2, 2012. The 1940 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tracts, and related boundaries and numbers used for each census. The coverage is nation wide and inclu...
1950 censusarchivescensusdemographynara
The 1950 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1950, although some persons were missed. The 1950 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 1, 2022. The 1950 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tracts, and related boundaries and numbers used for each census. The coverage is nation wide and inclu...
censusdifferential privacydisclosure avoidanceethnicitygroup quartershispanichousinghousing unitslatinonoisy measurementspopulationraceredistrictingvoting age
The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File. ...
censusdifferential privacydisclosure avoidanceethnicitygroup quartershousinghousing unitsnoisy measurementspopulationraceredistrictingvoting age
The 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File (NMF) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9, and implemented in the DAS 2020 Redistricting Production Code). The NMF was generated using the Census Bureau's implementation of the Discrete Gaussian Mechanism, calibrated to satisfy zero-Concentrated Differential Privacy with bounded neighbors.
The NMF values, called noisy measurements are the output of applying the Discrete Gaussian Mechanism to ...
censusstatisticssurvey
U.S. Census Bureau American Community Survey (ACS) Public Use Microdata Sample (PUMS) available in a linked data format using the Resource Description Framework (RDF) data model.
censusconfidence intervalsdifferential privacydisclosure avoidanceethnicitygroup quartershousinghousing unitsleast squaresnoisy measurementspopulationraceredistrictingvoting age
The 2020 Redistricting Data File Least Squares Estimates data product provides count estimates, and their standard deviations, for each tabulation that was published as part of the persons universe of the 2020 Redistricting Data File for the US, state, county, and tract geographic levels. These estimates are computed using the generalized least squares (GLS) estimator using as input the publicly available 2020 Census persons universe noisy measurement files for both the Redistricting Data File and the Demographic and Housing Characteristics File. The algorithm used to compute this estimate is desc...
ageapproximate monte carloapproximate monte carlo replicatescensusdemographic and housing characteristics filedhcdifferential privacydisclosure avoidanceethnicitygroup quartershispanichousehold typehousinghousing unitslatinomicrodatanoisy measurementspopulationraceredistrictingrelation-to-householdersingle year of agevoting age
The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF25) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Decennial Census Disclosure Avoidance System (DAS) into the Redistricting and DHC products. The PPMF0 was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential 2010 Census Edited File (CEF) as the initial input; the replicates were then created by executing the 2020 DAS TDA repeatedly with the PPMF0 as its initial input. Inspired by analogy to the use of bootstrap methods in non-private conte...
2020 censusageapproximate monte carloapproximate monte carlo replicatescensusdecennial censusdemographic and housing characteristics filedhcdifferential privacydisclosure avoidanceethnicitygroup quartershispanichousehold typehousinghousing unitslatinomicrodatanoisy measurementspopulationraceredistrictingrelation-to-householdersingle year of agevoting age
The 2020 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF50) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Census Disclosure Avoidance System (DAS) into the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile.
The PPMF0 was the source of the publicly released, official 2020 Census data products referenced above, and was cr...