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

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 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.

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 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.

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).

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.

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

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

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

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.

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

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)

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau practices "data suppression", filtering some block groups from demographic publication because they do not meet a population threshold. This practice...

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Demographic versus Cyclical Influences on US Labor Force Participation, PIIE Working Paper 14-4. If you use the data, please cite as: Cline, William R., and Jared Nolan. (2014). Demographic versus Cyclical Influences on US Labor Force Participation. PIIE Working Paper 14-4. Peterson Institute for International Economics.

The Computer Administered Panel Study (CAPS) collected demographic, personality, attitudinal, and other social psychological data from annual samples of University of North Carolina undergraduates from 1983 through 1988. Respondents spent 60 to 90 minutes per week for 20 weeks during the academic year, answering questions via computer terminals. In their comparison of demographic and academic variables, researchers found few significant differences between respondents and the general undergraduate population. This dataset contains the American Values Scale, which is a modification of the "https://en.wikipedia.org/wiki/Rokeach_Value_Survey" Target="_blank">Rokeach Values Survey. The survey asks respondents to rank various values and concepts in on a scale of importance ranging from 1 to 9, with 1 meaning "no importance at all" and 9 meaning "supreme importance to me."

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.

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.