Context

The United States Census is a decennial census mandated by Article I, Section 2 of the United States Constitution, which states: "Representatives and direct Taxes shall be apportioned among the several States ... according to their respective Numbers."
Source: https://en.wikipedia.org/wiki/United_States_Census

Content

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole.

The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys.

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_usa

https://cloud.google.com/bigquery/public-data/us-census

Dataset Source: United States Census Bureau

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What are the ten most populous zip codes in the US in the 2010 census?

What are the top 10 zip codes that experienced the greatest change in population between the 2000 and 2010 censuses?

https://cloud.google.com/bigquery/images/census-population-map.png" alt="https://cloud.google.com/bigquery/images/census-population-map.png"> https://cloud.google.com/bigquery/images/census-population-map.png

These datasets contains statements about demographics and outliers of Wiki-based Communities of Interest.

Group-centric dataset (sample):

{
  "title": "winners of Priestley Medal", 
  "recorded_members": 83, 
  "topics": ["STEM.Chemistry"], 
  "demographics": [
      "occupation-chemist",
      "gender-male", 
      "citizen-U.S."
  ], 
  "outliers": [
    {
      "reason": "NOT(chemist) unlike 82 recorded members", 
      "members": [
      "Francis Garvan (lawyer, art collector)"
      ]
    }, 
    {
      "reason": "NOT(male) unlike 80 recorded members", 
      "members": [
      "Mary L. Good (female)",
      "Darleane Hoffman (female)", 
      "Jacqueline Barton (female)"
      ]
    }
  ]
}

Subject-centric dataset (sample):

{
  "subject": "Serena Williams", 
  "statements": [
    {
      "statement": "NOT(sport-basketball) but (tennis) unlike 4 recorded winners of Best Female Athlete ESPY Award.", 
      "score": 0.36
    },
  {
      "statement": "NOT(occupation-politician) but (tennis player, businessperson, autobiographer) unlike 20 recorded winners of Michigan Women's Hall of Fame.",
      "score": 0.17
    }
  ]
}

This data can be also browsed at: https://wikiknowledge.onrender.com/demographics/

Analysis of ‘Top 100 US Cities by Population’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brandonconrady/top-100-us-cities-by-population on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

Data was pulled from a table in the following Wikipedia article: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population I used Microsoft Excel's PowerQuery function to pull the table from Wikipedia. Lists each city, its rank (based on 2020 population), some data on its area, and population in both 2020 and 2010.

Banner image source: https://unsplash.com/photos/wh-7GeXxItI

--- Original source retains full ownership of the source dataset ---

• This dataset provides comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer.
• Population estimates were obtained from U.S. Census Bureau data reported in American Community Survey 5-Year tables DP05 (total U.S. populations) and S1701 (U.S. populations below poverty line), and socioeconomic status-related microdata maintained by IPUMS USA.
• See Attached Document 'AmeriCorps Demographic Analysis Procedure.pdf' for a full technical documentation of the analysis.

Abstract

A crosswalk matching US ZIP codes to corresponding CBSA (core-based statistical area)

Documentation

The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368%3E" alt="">(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)

CBSA definition

A core-based statistical area (CBSA) is a U.S. geographic area defined by the Office of Management and Budget (OMB) that consists of one or more counties (or equivalents) anchored by an urban center of at least 10,000 people plus adjacent counties that are socioeconomically tied to the urban center by commuting. Areas defined on the basis of these standards applied to Census 2000 data were announced by OMB in June 2003. These standards are used to replace the definitions of metropolitan areas that were defined in 1990. The OMB released new standards based on the 2010 Census on July 15, 2015.

Further reading

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf

Contact authors

Questions regarding these crosswalk files can be directed to Alex Din with the subject line HUD-Crosswalks.

Acknowledgement

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

This dataset includes basic data about all US cities with a population over 100.000 (333 cities)

Source: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population

Coordinates of cities have been geocoded using https://rapidapi.com/GeocodeSupport/api/forward-reverse-geocoding/

Rows description:

City: Name of city State: Name of state Latitude, Longitude, Population_estimate_2022: Estimated population in 2022 Population_2020: Population figure from 2020 census Change_population: % change in population between 2022 and 2020 Land_area: City land area in sq. mi. Population_density_2020: density of population per sq. mi. in 2020

• This dataset provides comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer.- Population estimates were obtained from U.S. Census Bureau data reported in American Community Survey 5-Year tables DP05 (total U.S. populations) and S1701 (U.S. populations below poverty line), and socioeconomic status-related microdata maintained by IPUMS USA.- See Attached Document 'AmeriCorps Demographic Analysis Procedure.pdf' for a full technical documentation of the analysis.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

place IN ('isolated_dwelling','town','village','hamlet','city')

Features may have these attributes:

• name
• is_in
• place
• population
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Preparation of this data collection was funded by grant

90-A-1279 from the United States Department of Health and Human

Services, Administration on Aging. Estimates of the population of persons 60 years old and older were received from the Census Bureau in printed form and were made machine-readable by staff at ICPSR. Other variables contained in this dataset were merged from existing machine-readable census files. The data concerning racial composition of counties were taken from the CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: P.L. 94-171 POPULATION COUNTS (ICPSR 7854). The figures concerning per capita income were taken from the Bureau of the Census, GENERAL REVENUE SHARING, 1978 POPULATION ESTIMATES (ICPSR 7840). Variables include Federal Information Processing Standard (FIPS) state and county codes, 1978 per capita income of county, and total population of county broken down by sex, race, and age (in four-year increments with a category for persons 75 years old and older).

Content

Data was pulled from a table in the following Wikipedia article: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population I used Microsoft Excel's PowerQuery function to pull the table from Wikipedia. Lists each city, its rank (based on 2020 population), some data on its area, and population in both 2020 and 2010.

Living wages are based in US Dollars per hour, assuming 2080 hours worked per year.

In addition, living wage data from http://livingwage.mit.edu I left out the minimum wage from this dataset because it appears the data is somewhat inconsistent, and often falls back on the state minimum where localities can have a higher min wage. I also omitted the poverty wage data because for the most part it seemed to be the same for most areas. One last thing to keep in mind is some cities are grouped up into metropolitan statistical areas, and as a result you will see cities that are near each other have identical data.

Banner image source: https://unsplash.com/photos/wh-7GeXxItI

Introduction

U.S. Death Statistics: The death rate in the United States reflects various factors such as health issues, lifestyle changes, and other social factors that impact people's lives. Life expectancy has generally improved due to advancements in American healthcare, but several causes of death remain significant, including heart disease, cancer, and accidents. The opioid crisis, along with mental health challenges like suicide, also adds to the national death rate.

The COVID-19 pandemic further influenced the death statistics, showing the importance of public health measures. As the population is growing enormously, thus people may pass away from age-related conditions, highlighting the need for better healthcare access and preventive measures to improve overall well-being

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city') OR tags['landuse'] IN ('residential')

Features may have these attributes:

• name
• name:en
• place
• landuse
• population
• is_in
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

place IN ('isolated_dwelling','town','village','hamlet','city')

Features may have these attributes:

• name
• is_in
• place
• population
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This dashboard provides visual representation for comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer. Note: Toggle between dashboard pages with the arrows at the bottom of the dashboard. Pages: 1) State Results, 2) National Results, 3) Key Terms and Conditions

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The data on this page was collected in September 2023. The next wave of the CEV will be administered in September 2025.

The CEV can generate reliable estimates at the national level, within states and the District of Columbia, and in the largest twelve Metropolitan Statistical Areas to support evidence-based decision making and efforts to understand how people make a difference in communities across the country.

Click on "Export" to download and review an excerpt from the 2023 CEV Analytic Codebook that shows the variables available in the analytic CEV datasets produced by AmeriCorps.

Click on "Show More" to download and review the following 2023 CEV data and resources provided as attachments:

1) 2023 CEV Dataset Fact Sheet – brief summary of technical aspects of the 2023 CEV dataset. 2) CEV FAQs – answers to frequently asked technical questions about the CEV 3) Constructs and measures in the CEV 4) 2023 CEV Analytic Data and Setup Files – analytic dataset in Stata (.dta), R (.rdata), SPSS (.sav), and Excel (.csv) formats, codebook for analytic dataset, and Stata code (.do) to convert raw dataset to analytic formatting produced by AmeriCorps. These files were updated on January 16, 2025 to correct erroneous missing values for the ssupwgt variable. 5) 2023 CEV Technical Documentation – codebook for raw dataset and full supplement documentation produced by U.S. Census Bureau 6) 2023 CEV Raw Data and Read In Files – raw dataset in Stata (.dta) format, Stata code (.do) and dictionary file (.dct) to read ASCII dataset (.dat) into Stata using layout files (.lis)

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in The Economic Benefits of Latino Immigration: How the Migrant Hispanic Population’s Demographic Characteristics Contribute to US Growth, PIIE Working Paper 19-3.

If you use the data, please cite as: Huertas, Gonzalo, and Jacob Funk Kirkegaard. (2019). The Economic Benefits of Latino Immigration: How the Migrant Hispanic Population’s Demographic Characteristics Contribute to US Growth. PIIE Working Paper 19-3. Peterson Institute for International Economics.

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The data on this page was collected in September 2021. The CEV can generate reliable estimates at the national level, within states and the District of Columbia, and in the largest twelve Metropolitan Statistical Areas to support evidence-based decision making and efforts to understand how people make a difference in communities across the country. Click on "Export" to download and review an excerpt from the 2021 CEV Analytic Codebook that shows the variables available in the analytic CEV datasets produced by AmeriCorps. Click on "Show More" to download and review the following 2021 CEV data and resources provided as attachments: 1) 2021 CEV Dataset Fact Sheet – brief summary of technical aspects of the 2021 CEV dataset. 2) CEV FAQs – answers to frequently asked technical questions about the CEV 3) Constructs and measures in the CEV 4) 2021 CEV Analytic Data and Setup Files – analytic dataset in Stata (.dta), R (.rdata), SPSS (.sav), and Excel (.csv) formats, codebook for analytic dataset, and Stata code (.do) to convert raw dataset to analytic formatting produced by AmeriCorps. These files were updated on January 16, 2025 to correct erroneous missing values for the ssupwgt variable. 5) 2021 CEV Technical Documentation – codebook for raw dataset and full supplement documentation produced by U.S. Census Bureau 6) Nonresponse Bias Analysis produced by U.S. Census Bureau 7) 2021 CEV Raw Data and Read In Files – raw dataset in Stata (.dta) format, Stata code (.do) and dictionary file (.dct) to read ASCII dataset (.dat) into Stata using layout files (.lis)

This data collection supplies annual data on the size of the prison population and the size of the general population in the United States for the period 1925 to 1986. These yearend counts include tabulations for prisons in each of the 50 states and the District of Columbia, as well as the federal prisons, and are intended to provide a measure of the overall size of the prison population. The figures were provided from a voluntary reporting program in which each state, the District of Columbia, and the Federal Bureau of Prisons reported summary statistics as part of the statistical information on prison populations in the United States.

Introduction

US Death Statistics: The figures on the death of persons in the United States are an unequivocal depiction of the challenges the health, economy, and social frameworks of the country are facing. In the year 2024, many deaths can be attributed to a range of natural as well as artificial reasons, such as old age, illnesses, and accidents. However, heart disease and cancers continue to be the malignancies causing most deaths.

This article goes in-depth on these US death Statistics with the assistance of some recent numeric updates, figures, and clear descriptions, which layer the main aspects of death occurrences in the U.S. population.