This collection consists of modified records from CENSUS OF POPULATION AND HOUSING, 1960 PUBLIC USE SAMPLE [UNITED STATES]: ONE-IN-ONE HUNDRED SAMPE (ICPSR 7756). The original records consisted of 120-character household records and 120-character person records, whereas the new modified records are rectangular (each person record is combined with the corresponding household record) with a length of 188, after the deletion of some items. Additional information was added to the data records including typical educational requirement for current occupation, occupational prestige score, and group identification code. This version differs from the original public-use sample in the following ways: ages of persons 15-74 are included, 10 percent of the Black population from each file is included, and Mexican Americans (identified by a Spanish surname) from outside Arizona, California, Colorado, New Mexico, and Texas are not included. This dataset uses the 1970 equivalent occupational codes. The Census Bureau originally used two separate codes for the 1970 and 1960 files, but these have been modified and are now identical.

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

Congressional districts of the 99th Congress are matched to census geographic areas in this file. The areas used are those from the 1980 census. Each record contains geographic data, a congressional district code, and the total 1980 population count. Ten states were redistricted for the 99th Congress: California, Hawaii, Louisiana, Maine, Mississippi, Montana, New Jersey, New York, Texas, and Washington. The data for the other 40 states and the District of Columbia are identical to that for the 98th Congress. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08404.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

The Hispanic EPESE provides data on risk factors for mortality and morbidity in Mexican Americans in order to contrast how these factors operate differently in non-Hispanic White Americans, African Americans, and other major ethnic groups. The Wave 7 dataset comprises the sixth follow-up of the baseline Hispanic EPESE (HISPANIC ESTABLISHED POPULATIONS FOR THE EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1993-1994: [ARIZONA, CALIFORNIA, COLORADO, NEW MEXICO, AND TEXAS] [ICPSR 2851]). The baseline Hispanic EPESE collected data on a representative sample of community-dwelling Mexican Americans, aged 65 years and older, residing in the five southwestern states of Arizona, California, Colorado, New Mexico, and Texas. The public-use data cover demographic characteristics (age, sex, type of Hispanic race, income, education, marital status, number of children, employment, and religion), height, weight, social and physical functioning, chronic conditions, related health problems, health habits, self-reported use of dental, hospital, and nursing home services, and depression. Subsequent follow-ups provide a cross-sectional examination of the predictors of mortality, changes in health outcomes, and institutionalization, and other changes in living arrangements, as well as changes in life situations and quality of life issues. During this 7th Wave (dataset 1), 2010-2011, re-interviews were conducted either in person or by proxy, with 659 of the original respondents. This Wave also includes 419 re-interviews from the additional sample of Mexican Americans aged 75 years and over with higher average-levels of education than those of the surviving cohort who were added in Wave 5, increasing the total number of respondents to 1,078. The Wave 7 Informant Interviews dataset (dataset 2) includes data which corresponds to the sixth follow-up of the baseline Hispanic EPESE Wave 7 and included re-interviews with 1,078 Mexican Americans aged 80 years and older. During these interviews, participants were asked to provide the name and contact information of the person they are "closer to" or they "depend on the most for help." These INFORMANTS were contacted and interviewed regarding the health, function, social situation, finances, and general well-being of the ongoing Hispanic EPESE respondents. Information was also collected on the informant's health, function, and caregiver responsibilities and burden. This dataset includes information from 925 informants, more than two-thirds of whom were children of the respective respondents.

description: This data set represents the average population density, in number of people per square kilometer multiplied by 10 for the year 2000, compiled for every catchment of NHDPlus for the conterminous United States. The source data set is the 2000 Population Density by Block Group for the Conterminous United States (Hitt, 2003). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.; abstract: This data set represents the average population density, in number of people per square kilometer multiplied by 10 for the year 2000, compiled for every catchment of NHDPlus for the conterminous United States. The source data set is the 2000 Population Density by Block Group for the Conterminous United States (Hitt, 2003). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

In 2023, Texas had the highest number of forcible rape cases in the United States, with 15,097 reported rapes. Delaware had the lowest number of reported forcible rape cases at 194. Number vs. rate It is perhaps unsurprising that Texas and California reported the highest number of rapes, as these states have the highest population of states in the U.S. When looking at the rape rate, or the number of rapes per 100,000 of the population, a very different picture is painted: Alaska was the state with the highest rape rate in the country in 2023, with California ranking as 30th in the nation. The prevalence of rape Rape and sexual assault are notorious for being underreported crimes, which means that the prevalence of sex crimes is likely much higher than what is reported. Additionally, more than a third of women worry about being sexually assaulted, and most sexual assaults are perpetrated by someone the victim knew.

Context

This data was extracted from the CATS database dump released by the Justice Department under a FOIA request as part of the Pulitzer Center's Taken project.

Content

The data is available as CSV and JSON.

Acknowledgements

Data extraction was by performed by Maptian and supported by the Pulitzer Center.

Inspiration

We're hoping to encourage analysis of this data by reporters and experts in the field. If you are a newsroom interested in joining the Taken project, please get in touch.

Seizure method codes

A: Adoption F: Warrantless/PC C: Search Warrent H: Judgement I: Incident Arrest D: Seizure Warrent N: Consent O: Other B: Indictment J: Civil Complaint Q: Warrent - Federal Seizure P: Warrent - State Seizure R: Warrent - Federal Search and Seizure

Asset type codes

VH: Vehicle CA: Cash AL: Alcohol CH: Chemicals FI: Financial Instrument WE: Firearms RP: Real Property EL: Electronic Equipment AM: Ammunition OT: Other

Judicial district codes

ALM: Alabama, Middle ALN: Alabama, Northern ALS: Alabama, Southern AK: Alaska AZ: Arizona ARE: Arkansas, Eastern ARW: Arkansas, Western CAC: California, Central CAE: California, Eastern CAN: California, Northern CAS: California, Southern CO: Colorado CT: Connecticut DE: Delaware FLM: Florida, Middle FLN: Florida, Northern FLS: Florida, Southern GAM: Georgia, Middle GAN: Georgia, Northern GAS: Georgia, Southern HI: Hawaii ID: Idaho ILC: Illinois, Central ILN: Illinois, Northern ILS: Illinois, Southern INN: Indiana, Northern INS: Indiana, Southern IAN: Iowa, Northern IAS: Iowa, Southern KS: Kansas KYE: Kentucky, Eastern KYW: Kentucky, Western LAE: Louisiana, Eastern LAM: Louisiana, Middle LAW: Louisiana, Western ME: Maine MD: Maryland MA: Massachusetts MIE: Michigan, Eastern MIW: Michigan, Western MN: Minnesota MSN: Mississippi, Northern MSS: Mississippi, Southern MOE: Missouri, Eastern MOW: Missouri, Western MT: Montana NE: Nebraska NV: Nevada NH: New Hampshire NJ: New Jersey NM: New Mexico NYE: New York, Eastern NYN: New York, Northern NYS: New York, Southern NYW: New York, Western NCE: North Carolina, Eastern NCM: North Carolina, Middle NCW: North Carolina, Western ND: North Dakota OHN: Ohio, Northern OHS: Ohio, Southern OHE: Oklahoma, Eastern OHN: Oklahoma, Northern OHW: Oklahoma, Western OR: Oregon PEE: Pennsylvania, Eastern PEM: Pennsylvania, Middle PEW: Pennsylvania, Western RI: Rhode Island SC: South Carolina SD: South Dakota TNE: Tennessee, Eastern TNM: Tennessee, Middle TNW: Tennessee, Western TXE: Texas, Eastern TXN: Texas, Northern TXS: Texas, Southern TXW: Texas, Western UT: Utah VT: Vermont VAE: Virginia, Eastern VAW: Virginia, Western DC: Washington, D.C. WAW: Washington, Eastern WAW: Washington, Western WVN: West Virginia, Northern WVS: West Virginia, Southern WIE: Wisconsin, Eastern WIW: Wisconsin, Western WY: Wyoming

The table TX-Demographic-2025-03-01 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 35111382 rows across 698 variables.

Dataset - San Jose in the news

Severe burns are one of the most complex forms of traumatic injury. People with burn injuries often require long-term rehabilitation. Survivors of a burn injury often have a wide range of physical and psychosocial problems that can affect their quality of life. The Burn Model System (BMS) program began in 1994, with funding from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), in the Administration of Community Living and the U.S. Department of Education. The BMS program seeks to improve, through research, care and outcomes for people with burn injuries. Its research programs are housed in clinical burn centers that provide a coordinated and multidisciplinary system of rehabilitation care, including emergency medical, acute medical, post-acute, and long-term follow-up services. In addition, and with funding from NIDILRR, each BMS center conducts research and contributes follow-up data to the BMS National Data and Statistical Center (BMS NDSC). The four BMS centers are: Boston-Harvard Burn Injury Model System (BH-BIMS) in Boston, Massachusetts North Texas Burn Rehabilitation Model System (NTBRMS) in Dallas, Texas Northwest Regional Burn Model System (NWRBMS) in Seattle, Washington; andSouthern California Burn Model System (SCBMS) in Los Angeles, CaliforniaPast centers include the University of Texas Medical Branch Burn Injury Rehabilitation Model System in Galveston, Texas, the Johns Hopkins University Burn Model System in Baltimore, Maryland, the University of Colorado Denver National Data and Statistical Center, and the University of Colorado Denver Burn Model System Center.The BMS NDSC supports the research teams in the clinical burn centers. It also manages data collected by the BMS centers on more than 7,000 people who have received medical care for burn injuries. The data include a wide range of information—including pre-injury; injury; acute care; rehabilitation; recovery; and outcomes at 6, 12, 24 months, and every five years after the burn injury. To be included in the database, the burn injuries of participants must meet several criteria (as of 2015): ·More than 10% total body surface area (TBSA) burned, 65 years of age and older with burn surgery for wound closure;More than 20% TBSA burned, 0–64 years of age with burn surgery for wound closure; Electrical high voltage/lightning injury with burn surgery for wound closure; or Hand burn and/or face burn and/or feet burn with burn surgery for wound closure.In 2015, the BMS began a major initiative to collect data every five years after the injury and to collect new psychometrically sound, patient-reported outcome measures. On December 31, 2023, the database contained information for 4,913 adults (18 years of age and older at the time of burn) and 2,402 children (17 years of age and younger at the time of burn). The BMS program disseminates evidence-based information to patients, family members, health care providers, educators, policymakers, and the general public. The BMS centers provide information in many ways: peer-reviewed publications, presentations at national professional meetings, fact sheets about different aspects of living with a burn injury, newsletters for patients on BMS research and center events, outreach satellite clinics for patients living in rural areas, and peer-support groups. The BMS program also collaborates with the NIDILRR-funded Model Systems Knowledge Translation Center to promote the adoption of research findings by rehabilitation professionals, policymakers, and persons with burn injuries and their family members. The BMS program establishes partnerships to increase the overall impact of research; information dissemination; and training of clinicians, researchers, and policymakers. Current partners include the American Burn Association (ABA) and the Phoenix Society. Together, these partners help the BMS to ensure that NIDILRR-funded research addresses issues that are relevant to people with burn injuries.

The table TX-Voter-History-2025-03-01 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 35111382 rows across 884 variables.

Abstract (en): The School District Data Book (SDDB) is an education database and information system. It contains an extensive set of data on children, their households, and the nation's school systems. Under the sponsorship of the National Center for Education Statistics, the Bureau of the Census has produced special tabulation files using the basic record files of the 1990 Census of Population and Housing by school district. These tabulation files contain aggregated data describing attributes of children and households in school districts. Data are organized by seven types of tabulation records: (1) characteristics of all households, (2) characteristics of all persons, (3) characteristics of households with children, (4) characteristics of parents living with children, (5) children's household characteristics, (6) children's parents' characteristics, and (7) children's own characteristics. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. All public elementary and secondary education agencies in operation during 1990-1991 in the 50 states and the District of Columbia. 2006-10-27 Variable names were corrected in SAS and SPSS setup files. The processing note in the codebook was also updated to reflect the corrections.2006-01-12 All files were removed from dataset 139 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 138 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 137 and flagged as study-level files, so that they will accompany all downloads.2002-05-29 Seventeen additional datasets (Parts 140-156) were added to the collection, including data for two states previously not covered -- Vermont and Washington -- and additional data for Arkansas, California, Illinois, Massachusetts, Michigan, Minnesota, New Jersey, Pennsylvania, and Texas. (1) Some states have multiple data files because they have large numbers of cases. (2) Two data files are not included in this release. They are: Washington, Part 3, and Wisconsin, Part 4.

This data set represents the average population density, in number of people per square kilometer multiplied by 10 for the year 2000, compiled for every MRB_E2RF1 catchment of selected Major River Basins (MRBs, Crawford and others, 2006). The source data set is the 2000 Population Density by Block Group for the Conterminous United States (Hitt, 2003).

The MRB_E2RF1 catchments are based on a modified version of the U.S. Environmental Protection Agency's (USEPA) RF1_2 and include enhancements to support national and regional-scale surface-water quality modeling (Nolan and others, 2002; Brakebill and others, 2011).

Data were compiled for every MRB_E2RF1 catchment for the conterminous United States covering covering New England and Mid-Atlantic (MRB1), South Atlantic-Gulf and Tennessee (MRB2), the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy (MRB3), the Missouri (MRB4), the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf (MRB5), the Rio Grande, Colorado, and the Great basin (MRB6), the Pacific Northwest (MRB7) river basins, and California (MRB8).

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.

This service denotes the locations of colonias communities as defined in Section 916 of the Cranston-Gonzalez National Affordable Housing Act of 1990.In order to better serve colonia residents, the National Affordable Housing Act of 1990 (as amended) included Section 916 which called for the border states of Arizona, California, New Mexico and Texas to set aside a percentage of their annual State CDBG allocations for use in the colonias. The use of these set aside funds is to help meet the needs of the colonias residents in relationship to the need for potable water, adequate sewer systems, or decent, safe and sanitary housing. Therefore, the set-aside funds may be utilized for any CDBG eligible activity that is, or is in conjunction with, a potable water, sewer or housing activity.Per Section 916 of the Cranston-Gonzalez National Affordable Housing Act of 1990, a "colonia" refers to any community that meets the following criteria:(A) is in the State of Arizona, California, New Mexico, or Texas;(B) is in the area of the United States within 150 miles of the border between the United States and Mexico, except that the term does not include any standard metropolitan statistical area that has a population exceeding 1,000,000;(C) is designated by the State or county in which it is located as a colonia;(D) is determined to be a colonia on the basis of objective criteria, including lack of potable water supply, lack of adequate sewage systems, and lack of decent, safe, and sanitary housing, and;(E) was in existence and generally recognized as a colonia before the date of the enactment of the Cranston-Gonzalez National Affordable Housing Act.To learn more about the State Community Development Block Grant Colonias Set-Aside visit: https://www.hudexchange.info/programs/cdbg-colonias/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2019

This dataset comprises the third follow-up of the baseline Hispanic EPESE, HISPANIC ESTABLISHED POPULATIONS FOR THE EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1993-1994: ARIZONA, CALIFORNIA, COLORADO, NEW MEXICO, AND TEXAS, and provides information on 1,682 of the original respondents. The Hispanic EPESE collected data on a representative sample of community-dwelling Mexican-American elderly, aged 65 years and older, residing in the five southwestern states of Arizona, California, Colorado, New Mexico, and Texas. The primary purpose of the series was to provide estimates of the prevalence of key physical health conditions, mental health conditions, and functional impairments in older Mexican Americans and to compare these estimates with those for other populations. The Hispanic EPESE attempted to determine whether certain risk factors for mortality and morbidity operate differently in Mexican Americans than in non-Hispanic White Americans, African Americans, and other major ethnic groups. The public-use data cover background characteristics (age, sex, type of Hispanic race, income, education, marital status, number of children, employment, and religion), height, weight, social and physical functioning, chronic conditions, related health problems, health habits, self-reported use of dental, hospital, and nursing home services, and depression. The follow-ups provide a cross-sectional examination of the predictors of mortality, changes in health outcomes, and institutionalization and other changes in living arrangements, as well as changes in life situations and quality of life issues. The vital status of respondents from baseline to this round of the survey may be determined using the Vital Status file (Part 2). This file contains interview dates from the baseline as well as vital status at Wave IV (respondent survived, date of death if deceased, proxy-assisted, proxy-reported cause of death, proxy-true). The first follow-up of the baseline data (Hispanic EPESE Wave II, 1995-1996 [ICPSR 3385]) followed 2,438 of the original 3,050 respondents, and the second follow-up (Hispanic EPESE Wave III, 1998-1999 [ICPSR 4102]) followed 1,980 of these respondents. Hispanic EPESE, 1993-1994 (ICPSR 2851), was modeled after the design of ESTABLISHED POPULATIONS FOR EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1981-1993: EAST BOSTON, MASSACHUSETTS, IOWA AND WASHINGTON COUNTIES, IOWA, NEW HAVEN, CONNECTICUT, AND NORTH CENTRAL NORTH CAROLINA and ESTABLISHED POPULATIONS FOR EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1996-1997: PIEDMONT HEALTH SURVEY OF THE ELDERLY, FOURTH IN-PERSON SURVEY DURHAM, WARREN, VANCE, GRANVILLE, AND FRANKLIN COUNTIES, NORTH CAROLINA.

This dataset is part of the Geographical repository maintained by Opendatasoft.This dataset contains data for zip codes 5 digits in United States of America.ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery.Processors and tools are using this data.EnhancementsAdd ISO 3166-3 codes.Simplify geometries to provide better performance across the services.Add administrative hierarchy.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2010 Census.