- What is the Water Area?
- What is the Population Count?
- What is the Population Density?
- What is the Percent who did not finish the 9th grade?
- What is the Student Teacher Ratio?
- What is the Median Earnings?
- What is the Mean Job Proximity Index?
- What is the Number of Employees?
- What is the Percent Without Health Insurance?
- What is the Mean Environmental Health Hazard Index?
The land area of Bethel Census Area, AK was 40,570 in 2016.
Land area is a measurement providing the size, in square miles, of the land portions of geographic entities for which the Census Bureau tabulates and disseminates data. Area is calculated from the specific boundary recorded for each entity in the Census Bureau's geographic database. Land area is based on current information in the TIGER® data base, calculated for use with Census 2010.
Water Area figures include inland, coastal, Great Lakes, and territorial sea water. Inland water consists of any lake, reservoir, pond, or similar body of water that is recorded in the Census Bureau's geographic database. It also includes any river, creek, canal, stream, or similar feature that is recorded in that database as a two- dimensional feature (rather than as a single line). The portions of the oceans and related large embayments (such as Chesapeake Bay and Puget Sound), the Gulf of Mexico, and the Caribbean Sea that belong to the United States and its territories are classified as coastal and territorial waters; the Great Lakes are treated as a separate water entity. Rivers and bays that empty into these bodies of water are treated as inland water from the point beyond which they are narrower than 1 nautical mile across. Identification of land and inland, coastal, territorial, and Great Lakes waters is for data presentation purposes only and does not necessarily reflect their legal definitions.
Geographic and Area Datasets Involving Bethel Census Area, AK
- API bronx.lehman.cuny.edu | Last Updated 2013-06-10T03:17:53.000Z
Results from the 2010 Census regard ethnic makeup of Bronx census tracts. Source of this data came from the faculty of the Geography department at Lehman College
- API data.smcgov.org | Last Updated 2018-10-25T21:45:46.000Z
Demographics, including median income, total population, race, ethnicity, and age for unincorporated areas in San Mateo County. This data comes from the 2012 American Community Survey 5 year estimates DP03 and DP05 files. They Sky Londa area is located within two Census Tracts. The data for Sky Londa is the sum of both of those Census Tracts. Users of this data should take this into account when using data for Sky Londa.
- API opendata.ci.richmond.ca.us | Last Updated 2017-01-25T20:58:31.000Z
Census data from Bay Area Census and US Census.
- API opendata.utah.gov | Last Updated 2015-07-09T00:47:24.000Z
This data set contains median home values, demographic detail, educational levels, population data and other socioeconomic data for counties in Utah. Data is from the US Census Bureau.
- API data.pa.gov | Last Updated 2018-07-25T18:50:47.000Z
This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties. For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64 •3 sex categories: both sexes, male, and female •6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold •4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race). In addition, estimates for age category 0-18 by the income categories listed above are published. Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured. This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges. We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response. The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010 Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.
- API chhs.data.ca.gov | Last Updated 2017-02-17T22:33:14.000Z
The poverty rate (US Census-defined) table contains data on the percentage of the total population living below the poverty level, percentage of children living below the poverty level, and concentrated poverty data for California, its regions, counties, cities, towns, and Census tracts. Data for multiple time periods (2000, 2005-2007, 2008-2010, and 2006-2010) and with race/ethnicity stratification is included in the table. Concentrated poverty is the percentage of the poor living in Census tracts where 40% of the population or higher, are poor. The poverty rate table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity (http://www.cdph.ca.gov/programs/Pages/HealthyCommunityIndicators.aspx). Poverty is an important social determinant of health (see http://www.healthypeople.gov/2020/topicsobjectives2020/overview.aspx?topicid=39) that can impact people’s access to basic necessities (housing, food, education, jobs, and transportation), and is associated with higher incidence and prevalence of illness, and with reduced access to quality health care. More information on the data table and a data dictionary can be found in the About/Attachments section.
- API data.pa.gov | Last Updated 2017-07-19T15:40:20.000Z
This is a listing of Federal Information Processing Standard (FIPS) codes for each of the 67 counties in Pennsylvania. Information gathered from census data - https://www.census.gov/geo/reference/codes/cou.html For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.).
- API opendata.utah.gov | Last Updated 2016-01-19T21:35:42.000Z
This data set contains household survey data from Census. The American Community Survey was developed by the Census Bureau to replace the long form of the decennial census program. The ACS is a large demographic survey collected throughout the year using mailed questionnaires, telephone interviews, and visits from Census Bureau field representatives to about 3.5 million household addresses annually. Starting in 2005, the ACS produced social, housing, and economic characteristic data for demographic groups in areas with populations of 65,000 or more. (Prior to 2005, the estimates were produced for areas with 250,000 or more population.) The ACS also accumulates sample over 3-year and 5-year intervals to produce estimates for smaller geographic areas, including census tracts and block groups.
- API chhs.data.ca.gov | Last Updated 2017-02-17T22:10:28.000Z
This table contains data on the percent of population residing within ½ mile of a major transit stop for four California regions and the counties, cities/towns, and census tracts within the regions. The percent was calculated using data from four metropolitan planning organizations (San Diego Association of Governments, Southern California Association of Governments, Metropolitan Transportation Commission, and Sacramento Council of Governments) and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. A strong and sustainable transportation system supports safe, reliable, and affordable transportation opportunities for walking, bicycling, and public transit, and helps reduce health inequities by providing more opportunities for access to healthy food, jobs, health care, education, and other essential services. Active and public transportation promote health by enabling individuals to increase their level of physical activity, potentially reducing the risk of heart disease and obesity, improving mental health, and lowering blood pressure. More information about the data table and a data dictionary can be found in the About/Attachments section.
- API chhs.data.ca.gov | Last Updated 2017-02-17T22:39:18.000Z
This table contains data on the percent of residents within ½ mile of a park, beach, open space, or coastline, for California, its regions, counties, cities/towns, and census tracts. Data is from the California Protected Areas Database (CPAD version 1.8, 2012) and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity (http://www.cdph.ca.gov/programs/Pages/HealthyCommunityIndicators.aspx). As communities become increasingly more urban, parks and the protection of green and open spaces within cities increase in importance. Parks and natural areas buffer pollutants and contribute to the quality of life by providing communities with social and psychological benefits such as leisure, play, sports, and contact with nature. Parks are critical to human health by providing spaces for health and wellness activities. More information about the data table and a data dictionary can be found in the About/Attachments section.