Overall educational attainment measures the highest level of education attained by a given individual: for example, an individual counted in the percentage of the measured population with a master’s or professional degree can be assumed to also have a bachelor’s degree and a high school diploma, but they are not counted in the population percentages for those two categories. Overall educational attainment is the broadest education indicator available, providing information about the measured county population as a whole.

Only members of the population aged 25 and older are included in these educational attainment estimates, sourced from the U.S. Census Bureau American Community Survey (ACS).

Champaign County has high educational attainment: over 48 percent of the county's population aged 25 or older has a bachelor's degree or graduate or professional degree as their highest level of education. In comparison, the percentage of the population aged 25 or older in the United States and Illinois with a bachelor's degree in 2023 was 21.8% (+/-0.1) and 22.8% (+/-0.2), respectively. The population aged 25 or older in the U.S. and Illinois with a graduate or professional degree in 2022, respectively, was 14.3% (+/-0.1) and 15.5% (+/-0.2).

Educational attainment data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Educational Attainment for the Population 25 Years and Over.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (29 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (6 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018). U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This table contains data on the percent of population age 25 and up with a four-year college degree or higher for California, its regions, counties, county subdivisions, cities, towns, and census tracts. Greater educational attainment has been associated with health-promoting behaviors including consumption of fruits and vegetables and other aspects of healthy eating, engaging in regular physical activity, and refraining from excessive consumption of alcohol and from smoking. Completion of formal education (e.g., high school) is a key pathway to employment and access to healthier and higher paying jobs that can provide food, housing, transportation, health insurance, and other basic necessities for a healthy life. Education is linked with social and psychological factors, including sense of control, social standing and social support. These factors can improve health through reducing stress, influencing health-related behaviors and providing practical and emotional support. More information on the data table and a data dictionary can be found in the Data and Resources section. The educational attainment table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf

The format of the educational attainment table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

This dataset consists of a selection of variables extracted from the U.S. Department of Education's College Scorecard 2015/2016. For the original, raw data visit the College Scorecard webpage. This dataset includes variables about institution types, proportion of degree types awarded, student enrollments and demographics, and a number of price and revenue variables. For 2005-2006 data, see here.Note: Data is not uniformly available for all schools on all variables. Variables for which there is no data (NULL), or where data is suppressed for reasons of privacy, are indicated by 999999999.

ATTRIBUTE DESCRIPTION EXAMPLE

ID2 1

UNITIDUnit ID for institution 100654

OPEID 8-digit OPE ID for institution 100200

OPEID6 6-digit OPE ID for institution 1002

State FIPS

1

State

AL

Zip

35762

City

Normal

Institution Name

Alabama A & M University

Institution Type 1 Public 2 Private nonprofit 3 Private for-profit 1

Institution Level 1 4-year 2 2-year 3 Less-than-2-year 1

In Operation 1 true 0 false 1

Main Campus 1 true 0 false 1

Branches Count of the number of branches 1

Popular Degree 1 Predominantly certificate-degree granting 2 Predominantly associate's-degree granting 3 Predominantly bachelor's-degree granting 4 Entirely graduate-degree granting 3

Highest Degree 0 Non-degree-granting 1 Certificate degree 2 Associate degree 3 Bachelor's degree 4 Graduate degree 4

PCIP01 Percentage of degrees awarded in Agriculture, Agriculture Operations, And Related Sciences. 0.0446

PCIP03 Percentage of degrees awarded in Natural Resources And Conservation. 0.0023

PCIP04 Percentage of degrees awarded in Architecture And Related Services. 0.0094

PCIP05 Percentage of degrees awarded in Area, Ethnic, Cultural, Gender, And Group Studies. 0

PCIP09 Percentage of degrees awarded in Communication, Journalism, And Related Programs. 0

PCIP10 Percentage of degrees awarded in Communications Technologies/Technicians And Support Services. 0.0164

PCIP11 Percentage of degrees awarded in Computer And Information Sciences And Support Services. 0.0634

PCIP12 Percentage of degrees awarded in Personal And Culinary Services. 0

PCIP13 Percentage of degrees awarded in Education. 0.1268

PCIP14 Percentage of degrees awarded in Engineering. 0.1432

PCIP15 Percentage of degrees awarded in Engineering Technologies And Engineering-Related Fields. 0.0587

PCIP16 Percentage of degrees awarded in Foreign Languages, Literatures, And Linguistics. 0

PCIP19 Percentage of degrees awarded in Family And Consumer Sciences/Human Sciences. 0.0188

PCIP22 Percentage of degrees awarded in Legal Professions And Studies. 0

PCIP23 Percentage of degrees awarded in English Language And Literature/Letters. 0.0235

PCIP24 Percentage of degrees awarded in Liberal Arts And Sciences, General Studies And Humanities. 0.0423

PCIP25 Percentage of degrees awarded in Library Science. 0

PCIP26 Percentage of degrees awarded in Biological And Biomedical Sciences. 0.1009

PCIP27 Percentage of degrees awarded in Mathematics And Statistics. 0.0094

PCIP29 Percentage of degrees awarded in Military Technologies And Applied Sciences. 0

PCIP30 Percentage of degrees awarded in Multi/Interdisciplinary Studies. 0

PCIP31 Percentage of degrees awarded in Parks, Recreation, Leisure, And Fitness Studies. 0

PCIP38 Percentage of degrees awarded in Philosophy And Religious Studies. 0

PCIP39 Percentage of degrees awarded in Theology And Religious Vocations. 0

PCIP40 Percentage of degrees awarded in Physical Sciences. 0.0188

PCIP41 Percentage of degrees awarded in Science Technologies/Technicians. 0

PCIP42 Percentage of degrees awarded in Psychology. 0.0282

PCIP43 Percentage of degrees awarded in Homeland Security, Law Enforcement, Firefighting And Related Protective Services. 0.0282

PCIP44 Percentage of degrees awarded in Public Administration And Social Service Professions. 0.0516

PCIP45 Percentage of degrees awarded in Social Sciences. 0.0399

PCIP46 Percentage of degrees awarded in Construction Trades. 0

PCIP47 Percentage of degrees awarded in Mechanic And Repair Technologies/Technicians. 0

PCIP48 Percentage of degrees awarded in Precision Production. 0

PCIP49 Percentage of degrees awarded in Transportation And Materials Moving. 0

PCIP50 Percentage of degrees awarded in Visual And Performing Arts. 0.0258

PCIP51 Percentage of degrees awarded in Health Professions And Related Programs. 0

PCIP52 Percentage of degrees awarded in Business, Management, Marketing, And Related Support Services. 0.1479

PCIP54 Percentage of degrees awarded in History. 0

Admission Rate

0.6538

Average RetentionRate of retention averaged between full-time and part-time students. 0.4428

Retention, Full-Time Students

0.5779

Retention, Part-Time Students

0.3077

Completion Rate

0.1104

Enrollment Number of enrolled students 4505

Male Students Percentage of the student body that is male. 0.4617

Female Students Percentage of the student body that is female. 0.5383

White Percentage of the student body that identifies as white. 0.034

Black Percentage of the student body that identifies as African American. 0.9216

Hispanic Percentage of the student body that identifies as Hispanic or Latino. 0.0058

Asian Percentage of the student body that identifies as Asian. 0.0018

American Indian and Alaskan Native Percentage of the student body that identifies as American Indian or Alaskan Native. 0.0022

Native Hawaiian and Pacific Islander Percentage of the student body that identifies as Native Hawaiian or Pacific islander. 0.0018

Two or More Races Percentage of the student body that identifies as two or more races. 0

Non-Resident Aliens Percentage of the student body that are non-resident aliens. 0.0062

Race Unknown Percentage of the student body for whom racial identity is unknown. 0.0266

Percent Parents no HS Diploma Percentage of parents of students whose highest level of education is less than high school. 0.019298937

Percent Parents HS Diploma Percentage of parents of students whose highest level of education is high school 0.369436786

Percent Parents Post-Secondary Ed. Percentage of parents of students whose highest level of education is college or above. 0.611264277

Title IV Students Percentage of student body identified as Title IV 743

HCM2 Cash Monitoring Schools identified by the Department of Ed for Higher Cash Monitoring Level 2 0

Net Price

13435

Cost of Attendance

20809

In-State Tuition and Fees

9366

Out-of-State Tuition and Fees

17136

Tuition and Fees (Program) Tuition and fees for program-year schools NULL

Tution Revenue per Full-Time Student

9657

Expenditures per Full-Time Student

7941

Average Faculty Salary

7017

Percent of Students with Federal Loan

0.8159

Share of Students with Federal Loan

0.896382157

Share of Students with Pell Grant

0.860906217

Median Loan Principal Amount upon Entering Repayment

14600

Median Debt for Completed Students Median debt for student who completed a course of study 35000

Median Debt for Incompleted Students Median debt for student who did not complete a course of study 9500

Median Debt for Family Income $0K-$30K Median debt for students of families with less thank $30,000 income 14457

Median Debt for Family Income $30K-$75K Median debt for students of families with $30,000-$75,000 income 15000

Median Debt for Family Income over $75K Median debt for students of families with over $75,000 income 14250

Median Debt Female Students

16000

Median Debt Male Students

13750

Median Debt 1st Gen. Students Median debt for first generation college student 14307.5

Median Debt Not 1st Gen. Students Median debt for not first generation college students 14953

Cumulative Loan Debt Greater than 90% of Students (90th Percentile)

48750

Cumulative Loan Debt Greater than 75% of Students (75th Percentile)

32704

Cumulative Loan Debt Greater than 25% of Students (25th Percentile)

5500

Cumulative Loan Debt Greater than 10% of Students (10th Percentile)

3935.5

Accrediting Agency

Southern Association of Colleges and Schools Commission on Colleges

Website

www.aamu.edu/

Price Calculator

www2.aamu.edu/scripts/netpricecalc/npcalc.htm

Latitude

34.783368

Longitude

-86.568502

Analysis of ‘Postsecondary Completion Rates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/postsecondary-completion-ratese on 13 February 2022.

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

About this dataset

The National Center for Education Statistics (NCES) is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations. NCES is located within the U.S. Department of Education and the Institute of Education Sciences. NCES fulfills a Congressional mandate to collect, collate, analyze, and report complete statistics on the condition of American education; conduct and publish reports; and review and report on education activities internationally.

• Table 326.10. Graduation rate from first institution attended for first-time, full-time bachelor's degree-seeking students at 4-year postsecondary institutions, by race/ethnicity, time to completion, sex, control of institution, and acceptance rate: Selected cohort entry years, 1996 through 2008
• Table 326.20. Graduation rate from first institution attended within 150 percent of normal time for first-time, full-time degree/certificate-seeking students at 2-year postsecondary institutions, by race/ethnicity, sex, and control of institution: Selected cohort entry years, 2000 through 2011
• Table 326.30. Retention of first-time degree-seeking undergraduates at degree-granting postsecondary institutions, by attendance status, level and control of institution, and percentage of applications accepted: Selected years, 2006 to 2014
• Table 326.40. Percentage distribution of first-time postsecondary students starting at -2 and 4-year institutions during the 2003-04 academic year, by highest degree attained, enrollment status, and selected characteristics: Spring 2009

Source: https://nces.ed.gov/programs/digest/current_tables.asp

This dataset was created by National Center for Education Statistics and contains around 100 samples along with Unnamed: 27, Unnamed: 11, technical information and other features such as: - Unnamed: 21 - Unnamed: 5 - and more.

How to use this dataset

• Analyze Unnamed: 4 in relation to Unnamed: 34
• Study the influence of Unnamed: 6 on Unnamed: 29
• More datasets

Acknowledgements

If you use this dataset in your research, please credit National Center for Education Statistics

Start A New Notebook!

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

Context & Content

This dataset consists of the unemployment rate and education level of adults in the USA by county. That is, for each county in the USA, this dataset provides the count and percentage of unemployed adults as well as the count and percentage of adults of various educational backgrounds. Each county was been assigned one of four locale categories (City, Suburb, Town, Rural) according to its 2013 Urban Influence Code and their descriptions provided in UIC_codes.csv. From the descriptions of each of the codes and the descriptions of the locales "City", "Suburb", "Town", and "Rural" provided on page 2 of the locale user manual (locale_user_manual.pdf), each county was assigned one of four locales.

The unemployment rate data includes the count and percentage of unemployed adults for each county in the USA for each year from 2000-2020. The median household income for 2019 is also included. The education level data includes the count and percentage of adults with less than a high school diploma, a high school diploma only, some college, and a bachelor's degree/four years of college or more for the years 1970, 1980, 1990, 2000, and 2019. The Urban Influence Code data includes the UIC and locale description of each county in the USA and the locale user manual has been included as a PDF as strictly a reference file, to understand how each county was assigned a locale within the unemployment.csv and education.csv files.

Data Sources

Source for the unemployment rate and education level data by county: "County-level Data Sets." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/county-level-data-sets/

Source for Urban Influence Codes by county: "Urban Influence Codes." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/urban-influence-codes/#:~:text=The%202013%20Urban%20Influence%20Codes,to%20metro%20and%20micropolitan%20areas.&text=An%20update%20of%20the%20Urban,is%20planned%20for%20mid%2D2023.

Inspiration

This dataset was created to be used as an additional data source for the LearnPlatform COVID-19 Impact on Digital Learning Kaggle competition, but is suitable for other analyses related to unemployment rate and education level in the USA.

We know that students at elite universities tend to be from high-income families, and that graduates are more likely to end up in high-status or high-income jobs. But very little public data has been available on university admissions practices. This dataset, collected by Opportunity Insights, gives extensive detail on college application and admission rates for 139 colleges and universities across the United States, including data on the incomes of students. How do admissions practices vary by institution, and are wealthy students overrepresented?

Motivation

Education equality is one of the most contested topics in society today. It can be defined and explored in many ways, from accessible education to disabled/low-income/rural students to the cross-generational influence of doctorate degrees and tenure track positions. One aspect of equality is the institutions students attend. Consider the “Ivy Plus” universities, which are all eight Ivy League schools plus MIT, Stanford, Duke, and Chicago. Although less than half of one percent of Americans attend Ivy-Plus colleges, they account for more than 10% of Fortune 500 CEOs, a quarter of U.S. Senators, half of all Rhodes scholars, and three-fourths of Supreme Court justices appointed in the last half-century.

A 2023 study (Chetty et al, 2023) tried to understand how these elite institutions affect educational equality:

Do highly selective private colleges amplify the persistence of privilege across generations by taking students from high-income families and helping them obtain high-status, high-paying leadership positions? Conversely, to what extent could such colleges diversify the socioeconomic backgrounds of society’s leaders by changing their admissions policies?

To answer these questions, they assembled a dataset documenting the admission and attendance rate for 13 different income bins for 139 selective universities around the country. They were able to access and link not only student SAT/ACT scores and high school grades, but also parents’ income through their tax records, students’ post-college graduate school enrollment or employment (including earnings, employers, and occupations), and also for some selected colleges, their internal admission ratings for each student. This dataset covers students in the entering classes of 2010–2015, or roughly 2.4 million domestic students.

They found that children from families in the top 1% (by income) are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores, and two-thirds of this gap can be attributed to higher admission rates with similar scores, with the remaining third due to the differences in rates of application and matriculation (enrollment conditional on admission). This is not a shocking conclusion, but we can further explore elite college admissions by socioeconomic status to understand the differences between elite private colleges and public flagships admission practices, and to reflect on the privilege we have here and to envision what a fairer higher education system could look like.

Data

The data has been aggregated by university and by parental income level, grouped into 13 income brackets. The income brackets are grouped by percentile relative to the US national income distribution, so for instance the 75.0 bin represents parents whose incomes are between the 70th and 80th percentile. The top two bins overlap: the 99.4 bin represents parents between the 99 and 99.9th percentiles, while the 99.5 bin represents parents in the top 1%.

Each row represents students’ admission and matriculation outcomes from one income bracket at a given university. There are 139 colleges covered in this dataset.

The variables include an array of different college-level-income-binned estimates for things including attendance rate (both raw and reweighted by SAT/ACT scores), application rate, and relative attendance rate conditional on application, also with respect to specific test score bands for each college and in/out-of state. Colleges are categorized into six tiers: Ivy Plus, other elite schools (public and private), highly selective public/private, and selective public/private, with selectivity generally in descending order. It also notes whether a college is public and/or flagship, where “flagship” means public flagship universities. Furthermore, they also report the relative application rate for each income bin within specific test bands, which are 50-point bands that had the most attendees in each school tier/category.

Several values are reported in “test-score-reweighted” form. These values control for SAT score: they are calculated separately for each SAT score value, then averaged with weights based on the distribution of SAT scores at the institution.

Note that since private schools typically don’t differentiate between in-...

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest.

6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals.

7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives.

8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest.

9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group.

10. scChldHome:

This layer shows computer ownership and internet access by education. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of the population age 25+ who are high school graduates (includes equivalency) and have some college or associate's degree in households that have no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28006 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Cervical Cancer Risk Factors for Biopsy: This Dataset is Obtained from UCI Repository and kindly acknowledged! This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination! About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. In the United States, cervical cancer mortality rates plunged by 74% from 1955 - 1992 thanks to increased screening and early detection with the Pap test. AGE Fifty percent of cervical cancer diagnoses occur in women ages 35 - 54, and about 20% occur in women over 65 years of age. The median age of diagnosis is 48 years. About 15% of women develop cervical cancer between the ages of 20 - 30. Cervical cancer is extremely rare in women younger than age 20. However, many young women become infected with multiple types of human papilloma virus, which then can increase their risk of getting cervical cancer in the future. Young women with early abnormal changes who do not have regular examinations are at high risk for localized cancer by the time they are age 40, and for invasive cancer by age 50. SOCIOECONOMIC AND ETHNIC FACTORS Although the rate of cervical cancer has declined among both Caucasian and African-American women over the past decades, it remains much more prevalent in African-Americans -- whose death rates are twice as high as Caucasian women. Hispanic American women have more than twice the risk of invasive cervical cancer as Caucasian women, also due to a lower rate of screening. These differences, however, are almost certainly due to social and economic differences. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. HIGH SEXUAL ACTIVITY Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis).Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. FAMILY HISTORY Women have a higher risk of cervical cancer if they have a first-degree relative (mother, sister) who has had cervical cancer. USE OF ORAL CONTRACEPTIVES Studies have reported a strong association between cervical cancer and long-term use of oral contraception (OC). Women who take birth control pills for more than 5 - 10 years appear to have a much higher risk HPV infection (up to four times higher) than those who do not use OCs. (Women taking OCs for fewer than 5 years do not have a significantly higher risk.) The reasons for this risk from OC use are not entirely clear. Women who use OCs may be less likely to use a diaphragm, condoms, or other methods that offer some protection against sexual transmitted diseases, including HPV. Some research also suggests that the hormones in OCs might help the virus enter the genetic material of cervical cells. HAVING MANY CHILDREN Studies indicate that having many children increases the risk for developing cervical cancer, particularly in women infected with HPV. SMOKING Smoking is associated with a higher risk for precancerous changes (dysplasia) in the cervix and for progression to invasive cervical cancer, especially for women infected with HPV. IMMUNOSUPPRESSION Women with weak immune systems, (such as those with HIV / AIDS), are more susceptible to acquiring HPV. Immunocompromised patients are also at higher risk for having cervical precancer develop rapidly into invasive cancer. DIETHYLSTILBESTROL (DES) From 1938 - 1971, diethylstilbestrol (DES), an estrogen-related drug, was widely prescribed to pregnant women to help prevent miscarriages. The daughters of these women face a higher risk for cervical cancer. DES is no longer prsecribed.

We have studied the extent of the Red Giant Branch stellar population in the Fornax dwarf spheroidal galaxy using the spatially extended and homogeneous data set from Gaia EDR3. Our pre-selection of stars belonging to Fornax is based on their proper motions, parallaxes, and colour-magnitude diagram. The latter criteria provide a Fornax star sample, which we further restrict by colour and magnitude to eliminate contaminations due to either Milky Way stars or QSOs. The precision of the data has been sufficient to reach extremely small contaminations (0.02-0.3 per cent), allowing us to reach to a background level 12 magnitudes deeper than the central surface brightness of Fornax. We discover a break in the density profile, which reveals the presence of an additional component that extents 2.1 degree in radius, i.e. 5.4 kpc, and almost seven times the half-light radius of Fornax. The extended new component represents 10 per cent of the stellar mass of Fornax, and behaves like an extended halo. The absence of tidally elongated features at such an unprecedented depth (equivalent to 37.94 +/- 0.16 mag arcsec^-2^ in V-band) rules out a possible role of tidal stripping. We instead suggest that Fornax is likely at first infall, and has lost its gas very recently, which consequently leads to a lack of gravity implying that residual stars have spherically expanded to form the newly discovered stellar halo of Fornax.

Excel spreadsheet with the following columns of data: Date/Time (6/2016 to 10/2016) Total Gaseous Mercury Concentration (ng/m3) Sulfur dioxide concentration (ppb) Carbon monoxide concentration (ppm) Carbon dioxide concentration (ppm) Wind direction in degrees Wind speed in miles per hour Temperature in degrees celcius Percent relative humidity. This dataset is not publicly accessible because: The data used in this study were not generated or funded by the EPA. It can be accessed through the following means: The total-gaseous mercury (TGM) measurement data were collected by Dr. Lynne Gratz, Assistant Professor of Environmental Science, at Colorado College, 14 E. Cache La Poudre St. Colorado Springs, Colorado 80903 USA. This data is not available to EPA for public dissemination. Information on obtaining this data should be directed to Dr. Gratz at: lgratz@coloradocollege.edu or (719) 389-7465. The CO, SO2, temperature, wind speed, wind direction, and relative humidity were collected at the Colorado Department of Public Health & Environment (CDPHE) air monitoring location at Highway 24 in Colorado Springs. This data is already available in the public domain. Certified hourly data from this and other CDPHE air monitoring stations, including the Manitou Springs and Colorado College stations that were also mentioned in this manuscript, are available through the U.S. EPA Air Quality System (AQS) database (https://aqs.epa.gov/api) downloaded on September 1st 2017. Additional information can be obtained by emailing: cdphe.information@state.co.us303. Format: The data are in an Excel format. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

2013-2023 Virginia Median Household Income based on the past 12 months by Census County or County equivalent. Contains estimates and margins of error.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B19013 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

Annotation values are character representations of estimates and have values when non-integer information needs to be represented. Below are a few examples. Complete information is available on the ACS website under Notes on ACS Estimate and Annotation Values. (https://www.census.gov/data/developers/data-sets/acs-1year/notes-on-acs-estimate-and-annotation-values.html)

A value of -666,666,666 in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.

A value of -222,222,222 in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files.

This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture - Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). Previously, NLCD consisted of three major data releases based on a 10-year cycle. These include a circa 1992 conterminous U.S. land cover dataset with one thematic layer (NLCD 1992), a circa 2001 50-state/Puerto Rico updated U.S. land cover database (NLCD 2001 2011 Edition) with three layers including thematic land cover, percent imperviousness, and percent tree canopy, and a 1992/2001 Land Cover Change Retrofit Product. With these national data layers, there is often a 5-year time lag between the image capture date and product release. In some areas, the land cover can undergo significant change during production time, resulting in products that may be perpetually out of date. To address these issues, this circa 2006 NLCD land cover product (NLCD 2006 2011 Edition) was conceived to meet user community needs for more frequent land cover monitoring (moving to a 5-year cycle) and to reduce the production time between image capture and product release. NLCD 2006 (2011 edition) is designed to provide the user both updated land cover data and additional information that can be used to identify the pattern, nature, and magnitude of changes occurring between 2001 (2011 Edition) and 2006 (2011 Edition) for the conterminous United States at medium spatial resolution. For NLCD 2006 (2011 Edition), there are 4 primary data products: 1) NLCD 2006 Land Cover (2011 Edition); 2) NLCD 2001/2006 Land Cover Change Pixels (2011 Edition) labeled with the 2006 land cover class; 3) NLCD 2006 Percent Developed Imperviousness (2011 Edition), and 4) NLCD 2001/2006 Percent Developed Imperviousness Change (2011 Edition). In addition, ancillary metadata includes the NLCD 2006 Path/Row Index vector file showing the footprint of Landsat scene pairs used to derive 2001/2006 spectral change with change pair acquisition dates included in the attribute table. As part of the NLCD 2011 project, NLCD 2001 data products have been revised and reissued (2011 Edition) to provide full compatibility with all other NLCD 2011 Edition products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes. Land cover maps, derivatives and all associated documents are considered 'provisional 'until a formal accuracy assessment can be conducted. The NLCD 2006 is created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2006 land cover product can be directed to the NLCD 2006 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

The Gross Domestic Product per capita in the United States was last recorded at 66682.61 US dollars in 2024. The GDP per Capita in the United States is equivalent to 528 percent of the world's average. This dataset provides - United States GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The 2014 update of the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) for the conterminous United States (2014 NSHM; Petersen and others, 2014; https://pubs.usgs.gov/of/2008/1128/) included probabilistic ground motion maps for 2 percent and 10 percent probabilities of exceedance in 50 years, derived from seismic hazard curves for peak ground acceleration (PGA) and 0.2 and 1.0 second spectral accelerations (SAs) with 5 percent damping for the National Earthquake Hazards Reduction Program (NEHRP) site class boundary B/C (time-averaged shear wave velocity in the upper 30 meters [VS30]=760 meters per second [m/s]). This data release provides 0.1 degree by 0.1 degree gridded seismic hazard curves, 0.1 degree by 0.1 degree gridded probabilistic ground motions, and seismic hazard maps calculated for additional periods and additional uniform NEHRP site classes using the 2014 NSHM. For both the central and eastern U.S. (CEUS) and western U.S. (WUS), data and maps are provided for PGA, 0.1, 0.2, 0.3, 0.5, 1.0, and 2.0 second SAs with 5% damping for the NEHRP site class boundary B/C for 2, 5, and 10% probabilities of exceedance in 50 years. The WUS additionally includes data and maps for 0.75, 3.0, 4.0, and 5.0 SAs. The use of region-specific suites of weighted ground motion models (GMMs) in the 2014 NSHM precluded the calculation of ground motions for a uniform set of periods and site classes for the conterminous U.S. At the time of development of the 2014 NSHM, there was no consensus in the CEUS on an appropriate site-amplification model to use, therefore, we calculated hazard curves and maps for NEHRP Site Class A (VS30 = 2000 m/s), for which most stable continental GMMs were original developed, based on simulations for hard rock conditions. In the WUS, however, the GMMs allow amplification based on site class (defined by VS30), so we calculated hazard curves and maps for NEHRP site classes B (VS30 = 1080 m/s), C (VS30 = 530 m/s), D (VS30 = 260 m/s), and E (VS30 = 150 m/s) and site class boundaries A/B (VS30 = 1500 m/s), B/C (VS30 = 760 m/s), C/D (VS30 = 365 m/s), and D/E (VS30 = 185 m/s). Further explanation about how the data and maps were generated can be found in the accompanying U.S. Geological Survey Open-File Report 2018-1111 (https://doi.org/10.3133/ofr20181111). First Posted - July 18, 2018 Revised - February 20, 2019 (ver. 1.1)

The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture - Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). Previously, NLCD consisted of three major data releases based on a 10-year cycle. These include a circa 1992 conterminous U.S. land cover dataset with one thematic layer (NLCD 1992), a circa 2001 50-state/Puerto Rico updated U.S. land cover database (NLCD 2001 2011 Edition) with three layers including thematic land cover, percent imperviousness, and percent tree canopy, and a 1992/2001 Land Cover Change Retrofit Product. With these national data layers, there is often a 5-year time lag between the image capture date and product release. In some areas, the land cover can undergo significant change during production time, resulting in products that may be perpetually out of date. To address these issues, this circa 2006 NLCD land cover product (NLCD 2006 2011 Edition) was conceived to meet user community needs for more frequent land cover monitoring (moving to a 5-year cycle) and to reduce the production time between image capture and product release. NLCD 2006 (2011 edition) is designed to provide the user both updated land cover data and additional information that can be used to identify the pattern, nature, and magnitude of changes occurring between 2001 (2011 Edition) and 2006 (2011 Edition) for the conterminous United States at medium spatial resolution. For NLCD 2006 (2011 Edition), there are 4 primary data products: 1) NLCD 2006 Land Cover (2011 Edition); 2) NLCD 2001/2006 Land Cover Change Pixels (2011 Edition) labeled with the 2006 land cover class; 3) NLCD 2006 Percent Developed Imperviousness (2011 Edition), and 4) NLCD 2001/2006 Percent Developed Imperviousness Change (2011 Edition). In addition, ancillary metadata includes the NLCD 2006 Path/Row Index vector file showing the footprint of Landsat scene pairs used to derive 2001/2006 spectral change with change pair acquisition dates included in the attribute table. As part of the NLCD 2011 project, NLCD 2001 data products have been revised and reissued (2011 Edition) to provide full compatibility with all other NLCD 2011 Edition products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes. Land cover maps, derivatives and all associated documents are considered 'provisional 'until a formal accuracy assessment can be conducted. The NLCD 2006 is created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2006 land cover product can be directed to the NLCD 2006 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

This layer provides an estimate of flood frequency as one of seven classes:None: No reasonable possibility of flooding; one chance out of 500 of flooding in any year or less than 1 time in 500 years.Very Rare: Flooding is very unlikely but is possible under extremely unusual weather conditions; less than 1 percent chance of flooding in any year or less than 1 time in 100 years but more than 1 time in 500 years.Rare: Flooding is unlikely but is possible under unusual weather conditions; 1 to 5 percent chance of flooding in any year or nearly 1 to 5 times in 100 years.Occasional: Flooding is expected infrequently under usual weather conditions; 5 to 50 percent chance of flooding in any year or 5 to 50 times in 100 years.Common: (Obsolete Class) Combination of Occasional and FrequentFrequent: Flooding is likely to occur often under usual weather conditions; more than a 50 percent chance of flooding in any year (i.e., 50 times in 100 years), but less than a 50 percent chance of flooding in all months in any year.Very Frequent: Flooding is likely to occur very often under usual weather conditions; more than a 50 percent chance of flooding in all months of any year. Dataset SummaryPhenomenon Mapped: Flooding frequency Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date:December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for flooding frequency is derived from the gSSURGO map unit aggregated attribute table field Flooding Frequency - Dominant Condition (flodfreqdcd). What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selectingAddthenBrowse Living Atlas Layers. A window will open. Type "flooding frequency" in the search box and browse to the layer. Select the layer then clickAdd to Map. In ArcGIS Pro, open a map and selectAdd Datafrom the Map Tab. SelectDataat the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expandPortalif necessary, then selectLiving Atlas. Type "flooding frequency" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-inraster functionsor create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. The ArcGIS Living Atlas of the World provides an easy way to explore many otherbeautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This layer shows Households by Type. This is shown by county boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show percentage of households with no vehicles. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B11001, B25010, B25044, DP02, DP04Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 10, 2021National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS): About the SurveyGeography & ACSTechnical Documentation News & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes.
All of these are rendered in this dataset as null (blank) values.