2022 acs pums data  (MicroData Instrument Inc)

Journal: Scientific Data

Article Title: A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States

doi: 10.1038/s41597-024-03970-1

Figure Lengend Snippet: The Distribution of Cosine Similarity between Synthetic Population and the American Community Survey Public Use Microdata Sample (ACS PUMS) for the 2462 Public Use Microdata Areas (PUMA).

Article Snippet: In this process, we collected 2022 ACS PUMS data , because this is the only data that uses the latest 2020 Public Use Microdata Areas (PUMA) as the geographical boundaries.

Techniques:

Journal: Scientific Data

Article Title: A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States

doi: 10.1038/s41597-024-03970-1

Figure Lengend Snippet: The Distribution of Cosine Similarity between Synthetic Population and the American Community Survey Public Use Microdata Sample (ACS PUMS) for the 2462 Public Use Microdata Areas (PUMA).

Article Snippet: After comparing our synthetic population to the input census data (i.e., in tract level), we also conducted two external validation experiments by utilizing the American Community Survey Public Use Microdata Sample (ACS PUMS) and the census data at the block group level.

Techniques:

Journal: Scientific Data

Article Title: Multidimensional well-being of US households at a fine spatial scale using fused household surveys

doi: 10.1038/s41597-023-02788-7

Figure Lengend Snippet: Spatial Datasets Used.

Article Snippet: Variables in “donor” surveys are fused onto ACS Public Use Microdata Sample (PUMS) microdata to produce simulated values for variables unique to the donor.

Techniques: Blocking Assay

Journal: PLOS Global Public Health

Article Title: Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States

doi: 10.1371/journal.pgph.0002178

Figure Lengend Snippet: Details of the data analyzed using our proposed Bayesian hierarchical model for combining sources at multiple spatial resolutions.

Article Snippet: Poverty, high school graduation, owner-occupied, age, sex, race, household income, house value , Public Use Microdata Sample (PUMS) from ACS , A nationally representative sample of ACS responses, combined from years 2015–2019, which includes individual-level and household-level variables..

Techniques:

Journal: PLOS Global Public Health

Article Title: Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States

doi: 10.1371/journal.pgph.0002178

Figure Lengend Snippet: Characteristics of the study cohort. For census tract-level data, the mean and standard deviation across all US census tracts is calculated weighted by the populations in the census tracts. For county-level data, the mean and standard deviation across all US counties is calculated. For individual-level categorical data, the percent of individuals in each category is calculated. For individual-level continuous data, the mean and standard deviation across all individuals in the Public Use Microdata Sample is calculated, using accompanying sampling weights provided by American Community Survey (ACS). Other race denotes races other than White and Black.

Article Snippet: Poverty, high school graduation, owner-occupied, age, sex, race, household income, house value , Public Use Microdata Sample (PUMS) from ACS , A nationally representative sample of ACS responses, combined from years 2015–2019, which includes individual-level and household-level variables..

Techniques: Standard Deviation, Sampling