Key Table Information.Table Title.Business Dynamics Statistics: Establishment Age by Initial Establishment Size: 1978-2023.Table ID.BDSTIMESERIES.BDSEAGEIESIZE.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Business Dynamics Statistics.Source.U.S. Census Bureau, Economic Surveys.Release Date.2025-09-25.Release Schedule.The Business Dynamics Statistics (BDS) is updated once a year, typically in September—2 years after the reference year.The data in this file were released in September 2025.For more information about BDS releases, see BDS Updates..Dataset Universe.The dataset universe consists of all establishments and firms in the U.S. that are located in one of the 50 U.S. states or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2017 North American Industry Classification System (NAICS).The BDS data tables are compiled from the Longitudinal Business Database (LBD). The LBD is a longitudinal database of business establishments and firms with coverage starting in 1976. The LBD is constructed by linking annual snapshot files from the Census Bureau's Business Register (BR), and incorporating edits to BR data made by the County Business Patterns program. See: About This Program and BDS Methodology for complete information on the coverage, scope, and methodology of the Business Dynamics Statistics data series..Methodology.Data Items and Other Identifying Records.This file contains data classified by Establishment age and Initial Employment size of establishmentsNumber of firmsNumber of establishmentsNumber of employees(DHS) denominatorNumber of establishments born during the last 12 monthsRate of establishments born during the last 12 monthsNumber of establishments exited during the last 12 monthsRate of establishments exited during the last 12 monthsNumber of jobs created from expanding and opening establishments during the last 12 monthsNumber of jobs created from opening establishments during the last 12 monthsNumber of jobs created from expanding establishments during the last 12 monthsRate of jobs created from opening establishments during the last 12 monthsRate of jobs created from expanding and opening establishments during the last 12 monthsNumber of jobs lost from contracting and closing establishments during the last 12 monthsNumber of jobs lost from closing establishments during the last 12 monthsNumber of jobs lost from contracting establishments during the last 12 monthsRate of jobs lost from closing establishments during the last 12 monthsRate of jobs lost from contracting and closing establishments during the last 12 monthsNumber of net jobs created from expanding/contracting and opening/closing establishments during the last 12 monthsRate of net jobs created from expanding/contracting and opening/closing establishments during the last 12 monthsRate of reallocation during the last 12 monthsNumber of firms that exited during the last 12 monthsNumber of establishments associated with firm deaths during the last 12 monthsNumber of employees associated with firm deaths during the last 12 monthsDefinitions of data items can be found in the table by clicking on the column header and selecting “Column Notes” or by accessing the BDS Glossary..Unit(s) of Observation.The units for BDS are employer establishments and firms..Geography Coverage.The data are shown at the U.S. level. For more information, see About This Program..Industry Coverage.The data are shown at the NAICS 00 "Total for all Sectors" level. For more information, see About This Program..Sampling.Business Dynamics Statistics tabulations are based on a combination of administrative and survey-collected data, and therefore no sampling is done. For more information about methodology and data limitations, see BDS Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7508369, Disclosure Review Board (DRB) approval number: CBDRB-FY25-0331).In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. The BDS has adapted the disclosure avoidance method of the County Business Patterns (CBP) in using Hybrid Balanced Multiplicative Noise Infusion. CBP has been released with noise-infusion since 2007; see CBP Methodology.In addition to noise infusion, cells with fewer than three firms are not presented. For more symbolic representation, see Symbols in the Table Information section below. For more information about BDS methodology, see BDS Methodology..Technical Documentation/Methodology.For detailed information on the coverage and methodology of the Business Dynamics Statistics data series, see Technical Documentation..Weights.No weighting applied to Business Dynamics Statistics..Table Information.FTP Download.https://www2.census.gov/programs-surveys/bds/data/.AP...

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Census: Number of Households: Andhra Pradesh: Rural data was reported at 14,246,309.000 Unit in 03-01-2011. This records an increase from the previous number of 12,607,167.000 Unit for 03-01-2001. Census: Number of Households: Andhra Pradesh: Rural data is updated decadal, averaging 13,426,738.000 Unit from Mar 2001 (Median) to 03-01-2011, with 2 observations. The data reached an all-time high of 14,246,309.000 Unit in 03-01-2011 and a record low of 12,607,167.000 Unit in 03-01-2001. Census: Number of Households: Andhra Pradesh: Rural data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAF005: Census: Number of Households: by Size: Andhra Pradesh.

Context

2011 India census data. Includes population/demographic data , housing data and socio economic data for each district.

Content

• india-districts-census-2011.csv - Population enumeration data with expanded columns.
• india_census_housing-hlpca-full.csv - Housing statistics for total (rural + urban) population by district.
• gdp_AndhraPradesh1.csv , gdp_AndhraPradesh2 : Contains GDP data for state AP.
• gdp_ArunachalPradesh.csv: Contains GDP data for state ArunachalPradesh.
• gdp_Assam1.csv, gdp_Assam2.csv : Contains GDP data for state Assam.
• gdp_Bihar1.csv , gdp_Bihar2.csv : Contains GDP data for state Bihar.
• gdp_Chattisgarh.csv : Contains GDP data for state Chattisgarh.
• gdp_Haryana.csv : Contains GDP data for state Haryana.
• gdp_HimachalPradesh.csv : Contains GDP data for state HimachalPradesh.
• gdp_Jharkhand.csv : Contains GDP data for state Jharkhand.
• gdp_Karnataka1.csv , gdp_Karnataka2.csv : Contains GDP data for state Karnataka.
• gdp_Kerala1.csv , gdp_Kerala2.csv : Contains GDP data for state Kerala.
• gdp_MadhyaPradesh.csv : Contains GDP data for state MadhyaPradesh.
• gdp_Maharashtra1.csv , gdp_Maharashtra2.csv : Contains GDP data for state Maharashtra.
• gdp_Manipur.csv : Contains GDP data for state Manipur.
• gdp_Meghalaya.csv : Contains GDP data for state Meghalaya.
• gdp_Mizoram.csv : Contains GDP data for state Mizoram.
• gdp_Odisha1.csv , gdp_Odisha2.csv : Contains GDP data for state Odhisha.
• gdp_Punjab1.csv , gdp_Punjab2.csv : Contains GDP data for state Punjab.
• gdp_Rajasthan1.csv , gdp_Rajasthan2.csv : Contains GDP data for state Rajasthan.
• gdp_Sikkim.csv : Contains GDP data for state Sikkim.
• gdp_Tamilnadu.csv : Contains GDP data for state Tamilnadu.
• gdp_Uttarakhand.csv : Contains GDP data for state Uttarakhand.
• gdp_UttarPradesh1.csv , gdp_UttarPradesh2.csv : Contains GDP data for state UttarPradesh.
• gdp_WestBengal1.csv , gdp_WestBengal2.csv : Contains GDP data for state West Bengal.

Acknowledgements

https://www.kaggle.com/danofer/india-census

https://www.kaggle.com/umeshnarayanappa/explore-census-2001-india

http://udise.in/drc.htm

https://data.gov.in/catalog/district-wise-gdp-and-growth-rate-current-price2004-05

https://data.gov.in/catalog/district-wise-gdp-and-growth-rate-constant-price1999-2000

Banner photo by @ishant_mishra54 from Unsplash.

Inspiration

What are the socioeconomic trends in different parts of India?

The dataset contains 20th livestock census compiled data on year-, district-, region-, town-, ward-, block- and village-wise number of cattle, buffaloes, goats, sheeps and pigs in the State of Andhra Pradesh in the year 2019

• Context I am greatly inspired with this dataset containing geo spatial details for each zip code and contains the total wages for each area.This gave me opportunity to create a data visualisation in Tableau using HexBin chart which is added as a Kernel to this dataset.

• Content

  • About the data: 81,831 rows of data. All 41,891 active zipcodes + 634 decommisioned zipcodes from the recent past. All 80673 active Primary(41885), Acceptable(13988), and Not Acceptable(24800) placenames. Some additonal placenames for decommisioned codes. 29,971 Standard, 9465 PO BOX, 2437 Unique, and 649 Military codes.

• 50 States + 361 AA Military

• Americas 38 AE Military

• Europe 164 AP Military

• Pacific 1 AS American Samoa 290 DC Washinton DC 4 FM Federated States Micronesia 13 GU Guam 2 MH Marshall Islands 3 MP Northern Mariana Islands 176 PR Puerto Rico 2 PW Palau 16 VI Virgin Islands

• Name Type Description

• Zipcode Text 5 digit Zipcode or military postal code(FPO/APO)

• ZipCodeType Text Standard, PO BOX Only, Unique, Military(implies APO or FPO)

• City Text USPS offical city name(s)

• State Text USPS offical state, territory, or quasi-state (AA, AE, AP) abbreviation code

• LocationType Text Primary, Acceptable,Not Acceptable

• Lat Double Decimal Latitude, if available

• Long Double Decimal Longitude, if available

• Location Text Standard Display (eg Phoenix, AZ ; Pago Pago, AS ; Melbourne, AU )

• Decommissioned Text If Primary location, Yes implies historical Zipcode, No Implies current Zipcode; If not Primary, Yes implies Historical Placename

• TaxReturnsFiled Long Integer Number of Individual Tax Returns Filed in 2008

• EstimatedPopulation Long Integer Tax returns filed + Married filing jointly + Dependents

• TotalWages Long Integer Total of Wages Salaries and Tips

  • Source: http://federalgovernmentzipcodes.us/

  • Current zipcodes, placenames, zipcode type(Standard, PO, Unique, Military), placename type (Primary, Acceptable, Not Acceptable)

• : USPS Military place names (base or ship name)

• : MPSA 2008 Election Ballot information Tax returns filed, estimated population, total wages: IRS 2008 Latitude and Longitude; National Weather Service supplemented by Google Earth and Maps and occasionally other sources Decommissioned zip codes, Our old database--usually quality sources, but not verifiable.

• Acknowledgements

  Other Sources of zipcode information:

• Placenames (Cities, towns, geographic features) can be found at US Geological Survey GNIS Dataset The IRS has additional data fields for 2008 and is reviewing their publication procedures for later years.

  see http://www.irs.gov/taxstats/indtaxstats/article/0,,id=96947,00.html

• The Census publishes data, but they use Zipcode Tabulation Areas (ZCTAs) which

• 1) have changed areas between the 2000 census and the 2010 census

• 2) do not map well to USPS zipcodes well. If needed http://www.census.gov/geo/ZCTA/zcta.html Social Security recipients by zipcode http://www.ssa.gov/policy/docs/statcomps/oasdi_zip/ For economic researchers and those who want tons of background on data sources by zipcode, University of Missouri OSEDA project

  • Free Zipcode Database (8.7 MB) Updated 1/22/2012 All Locations (Multiple locations for some zipcodes)

  Inspiration I am hoping that people in the right government agencies/authorities would be able to utilize this data to focus on

  community developments where it needs immediate attention.

AP VoteCast is a survey of the American electorate conducted by NORC at the University of Chicago for Fox News, NPR, PBS NewsHour, Univision News, USA Today Network, The Wall Street Journal and The Associated Press.

AP VoteCast combines interviews with a random sample of registered voters drawn from state voter files with self-identified registered voters selected using nonprobability approaches. In general elections, it also includes interviews with self-identified registered voters conducted using NORC’s probability-based AmeriSpeak® panel, which is designed to be representative of the U.S. population.

Interviews are conducted in English and Spanish. Respondents may receive a small monetary incentive for completing the survey. Participants selected as part of the random sample can be contacted by phone and mail and can take the survey by phone or online. Participants selected as part of the nonprobability sample complete the survey online.

In the 2020 general election, the survey of 133,103 interviews with registered voters was conducted between Oct. 26 and Nov. 3, concluding as polls closed on Election Day. AP VoteCast delivered data about the presidential election in all 50 states as well as all Senate and governors’ races in 2020.

Using this Data - IMPORTANT

This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use statistical software such as R or SPSS to weight the data.

National Survey

The national AP VoteCast survey of voters and nonvoters in 2020 is based on the results of the 50 state-based surveys and a nationally representative survey of 4,141 registered voters conducted between Nov. 1 and Nov. 3 on the probability-based AmeriSpeak panel. It included 41,776 probability interviews completed online and via telephone, and 87,186 nonprobability interviews completed online. The margin of sampling error is plus or minus 0.4 percentage points for voters and 0.9 percentage points for nonvoters.

State Surveys

In 20 states in 2020, AP VoteCast is based on roughly 1,000 probability-based interviews conducted online and by phone, and roughly 3,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.3 percentage points for voters and 5.5 percentage points for nonvoters.

In an additional 20 states, AP VoteCast is based on roughly 500 probability-based interviews conducted online and by phone, and roughly 2,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.9 percentage points for voters and 6.9 percentage points for nonvoters.

In the remaining 10 states, AP VoteCast is based on about 1,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 4.5 percentage points for voters and 11.0 percentage points for nonvoters.

Although there is no statistically agreed upon approach for calculating margins of error for nonprobability samples, these margins of error were estimated using a measure of uncertainty that incorporates the variability associated with the poll estimates, as well as the variability associated with the survey weights as a result of calibration. After calibration, the nonprobability sample yields approximately unbiased estimates.

As with all surveys, AP VoteCast is subject to multiple sources of error, including from sampling, question wording and order, and nonresponse.

Sampling Details

Probability-based Registered Voter Sample

In each of the 40 states in which AP VoteCast included a probability-based sample, NORC obtained a sample of registered voters from Catalist LLC’s registered voter database. This database includes demographic information, as well as addresses and phone numbers for registered voters, allowing potential respondents to be contacted via mail and telephone. The sample is stratified by state, partisanship, and a modeled likelihood to respond to the postcard based on factors such as age, race, gender, voting history, and census block group education. In addition, NORC attempted to match sampled records to a registered voter database maintained by L2, which provided additional phone numbers and demographic information.

Prior to dialing, all probability sample records were mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Postcards were addressed by name to the sampled registered voter if that individual was under age 35; postcards were addressed to “registered voter” in all other cases. Telephone interviews were conducted with the adult that answered the phone following confirmation of registered voter status in the state.

Nonprobability Sample

Nonprobability participants include panelists from Dynata or Lucid, including members of its third-party panels. In addition, some registered voters were selected from the voter file, matched to email addresses by V12, and recruited via an email invitation to the survey. Digital fingerprint software and panel-level ID validation is used to prevent respondents from completing the AP VoteCast survey multiple times.

AmeriSpeak Sample

During the initial recruitment phase of the AmeriSpeak panel, randomly selected U.S. households were sampled with a known, non-zero probability of selection from the NORC National Sample Frame and then contacted by mail, email, telephone and field interviewers (face-to-face). The panel provides sample coverage of approximately 97% of the U.S. household population. Those excluded from the sample include people with P.O. Box-only addresses, some addresses not listed in the U.S. Postal Service Delivery Sequence File and some newly constructed dwellings. Registered voter status was confirmed in field for all sampled panelists.

Weighting Details

AP VoteCast employs a four-step weighting approach that combines the probability sample with the nonprobability sample and refines estimates at a subregional level within each state. In a general election, the 50 state surveys and the AmeriSpeak survey are weighted separately and then combined into a survey representative of voters in all 50 states.

State Surveys

First, weights are constructed separately for the probability sample (when available) and the nonprobability sample for each state survey. These weights are adjusted to population totals to correct for demographic imbalances in age, gender, education and race/ethnicity of the responding sample compared to the population of registered voters in each state. In 2020, the adjustment targets are derived from a combination of data from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, Catalist’s voter file and the Census Bureau’s 2018 American Community Survey. Prior to adjusting to population totals, the probability-based registered voter list sample weights are adjusted for differential non-response related to factors such as availability of phone numbers, age, race and partisanship.

Second, all respondents receive a calibration weight. The calibration weight is designed to ensure the nonprobability sample is similar to the probability sample in regard to variables that are predictive of vote choice, such as partisanship or direction of the country, which cannot be fully captured through the prior demographic adjustments. The calibration benchmarks are based on regional level estimates from regression models that incorporate all probability and nonprobability cases nationwide.

Third, all respondents in each state are weighted to improve estimates for substate geographic regions. This weight combines the weighted probability (if available) and nonprobability samples, and then uses a small area model to improve the estimate within subregions of a state.

Fourth, the survey results are weighted to the actual vote count following the completion of the election. This weighting is done in 10–30 subregions within each state.

National Survey

In a general election, the national survey is weighted to combine the 50 state surveys with the nationwide AmeriSpeak survey. Each of the state surveys is weighted as described. The AmeriSpeak survey receives a nonresponse-adjusted weight that is then adjusted to national totals for registered voters that in 2020 were derived from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, the Catalist voter file and the Census Bureau’s 2018 American Community Survey. The state surveys are further adjusted to represent their appropriate proportion of the registered voter population for the country and combined with the AmeriSpeak survey. After all votes are counted, the national data file is adjusted to match the national popular vote for president.

Using an innovative approach with Geographic Information Systems and Remote Sensing, ORNL’s LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available and represents an “ambient population” (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Purpose: LandScan Global was developed for the U.S. Department of Defense and is used for rapid consequence and risk assessment as well as emergency planning and management. Detailed information are to be found in cover_letter_ls13.pdf

Updates

April 29, 2020

• The AP is now providing historical time series data for testing counts and death counts from The COVID Tracking Project. The counts are provided here unaltered, along with a population column with Census ACS-1 estimates and calculated testing rate and death rate columns.

October 13, 2020

The COVID Tracking Project is releasing more precise total testing counts, and has changed the way it is distributing the data that ends up on this site. Previously, total testing had been represented by positive tests plus negative tests. As states are beginning to report more specific testing counts, The COVID Tracking Project is moving toward reporting those numbers directly.

This may make it more difficult to compare your state against others in terms of positivity rate, but the net effect is we now have more precise counts:

• Total Test Encounters: Total tests increase by one for every individual that is tested that day. Additional tests for that individual on that day (i.e., multiple swabs taken at the same time) are not included

• Total PCR Specimens: Total tests increase by one for every testing sample retrieved from an individual. Multiple samples from an individual on a single day can be included in the count

• Unique People Tested: Total tests increase by one the first time an individual is tested. The count will not increase in later days if that individual is tested again – even months later

These three totals are not all available for every state. The COVID Tracking Project prioritizes the different count types for each state in this order:

1. Total Test Encounters

2. Total PCR Specimens

3. Unique People Tested

If the state does not provide any of those totals directly, The COVID Tracking Project falls back to the initial calculation of total tests that it has provided up to this point: positive + negative tests.

One of the above total counts will be the number present in the cumulative_total_test_results and total_test_results_increase columns.

• The positivity rates provided on this site will divide confirmed cases by one of these total_test_results columns.

  • Due to these changes, we advise comparing positivity rates between states only if the states being compared have the same type of total test count.

Overview

The AP is using data collected by the COVID Tracking Project to measure COVID-19 testing across the United States.

The COVID Tracking Project data is available at the state level in the United States. The AP has paired this data with population figures and has calculated testing rates and death rates per 1,000 people.

This data is from The COVID Tracking Project API that is updated regularly throughout the day. Like all organizations dealing with data, The COVID Tracking Project is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find The COVID Tracking Project daily data reports, and a clean version of their feed.

A Note on timing: - The COVID Tracking Project updates regularly throughout the day, but state numbers will come in at different times. The entire Tracking Project dataset will be updated between 4-5pm EDT daily. Keep this time in mind when reporting on stories comparing states. At certain times of day, one state may be more up to date than another. We have included the date_modified timestamp for state-level data, which represents the last time the state updated its data. The date_checked value in the state-level data reflects the last time The COVID Tracking Project checked the state source. We have also included the last_modified timestamp for the national-level data, which marks the last time the national data was updated.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

About the data

• State-level population data is sourced from the census ACS 1-year survey for 2018.
• Historical data for each state is available on The COVID Tracking Project's main data page.

Caveats

• The total_people_tested counts do not include pending tests. They are the total number of tests that have returned positive or negative.
• The process for collecting testing data is different for each state. The COVID Tracking Project makes note of the difficulties specific to each state on their main data page.

Attribution

This data should be credited to The COVID Tracking Project

Contact

Nicky Forster — nforster@ap.org

Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data was reported at 4.900 NA in 2020. This records an increase from the previous number of 4.800 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data is updated yearly, averaging 5.400 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 6.100 NA in 1998 and a record low of 4.800 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.