The mean job proximity index of Bethel Census Area, AK was 54 in 2015.

Median Jobs Proximity Index

The jobs proximity index quantifies access to employment opportunities in a region. Values are percentile ranked and range from 0 to 100, with higher values corresponding to better access to jobs. Data is computed for U.S. counties by applying summary statistics across all census tracts present in a county and is current as of 2015.

The underlying index quantifies the accessibility of a given residential neighborhood as a function of its distance to all job locations within a census tract, with distance to larger employment centers weighted more heavily. Specifically, a gravity model is used, where the accessibility (Ai) of a given residential block-group is a summary description of the distance to all job locations, with the distance from any single job location positively weighted by the size of employment (job opportunities) at that location and inversely weighted by the labor supply (competition) to that location.

Above charts are based on data from the U.S. Department of Housing and Urban Development | Data Source | ODN Dataset | API - Notes:

1. ODN datasets and APIs are subject to change and may differ in format from the original source data in order to provide a user-friendly experience on this site.

2. To build your own apps using this data, see the ODN Dataset and API links.

3. If you use this derived data in an app, we ask that you provide a link somewhere in your applications to the Open Data Network with a citation that states: "Data for this application was provided by the Open Data Network" where "Open Data Network" links to http://opendatanetwork.com. Where an application has a region specific module, we ask that you add an additional line that states: "Data about REGIONX was provided by the Open Data Network." where REGIONX is an HREF with a name for a geographical region like "Seattle, WA" and the link points to this page URL, e.g. http://opendatanetwork.com/region/1600000US5363000/Seattle_WA

Jobs and Job Proximity Datasets Involving Bethel Census Area, AK

  • API

    Uninsured Population Census Data CY 2009-2014 Human Services

    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.

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    Poverty Rate (defined by U.S. Census) by California Regions, 2000-2010

    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.

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    Walkable Distance Public Transit, 2008-2012

    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.

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    Virginia Beach Demographics

    data.vbgov.com | Last Updated 2017-10-12T13:51:45.000Z

    This dataset provides demographic information from the American Community Survey about residents of Virginia Beach. This data was originally provided in the executive summary of the City of Virginia Beach’s Operating Budget.

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    Maryland Jurisdictions Socioeconomic Characteristics

    data.maryland.gov | Last Updated 2018-02-27T16:43:52.000Z

    Maryland county data for population, gender, race, labor force, educational attainment, income, poverty, households and housing units. Data Source: 2012 -2016 American Community Survey, 5 Year estimates. U.S. Census Bureau, released December 2017. Is updated annually for the 5 year period.

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    Educational Attainment, 2000-2010

    chhs.data.ca.gov | Last Updated 2017-06-14T18:11:25.000Z

    This table contains data on the percent of residents aged 25 years and older educational attainment (percent completing high school, associates degree, college or more advanced degree) for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity (https://www.cdph.ca.gov/Programs/OHE/Pages/Healthy-Communities-Data-and-Indicators-Project-(HCI).aspx). 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 about the data table and a data dictionary can be found in the About/Attachments section.

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    Living Wage

    chhs.data.ca.gov | Last Updated 2017-02-17T22:39:34.000Z

    This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator (http://livingwage.mit.edu/) and the U.S. Census Bureau, American Community Survey. 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). The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

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    Housing Cost Burden, 2006-2010

    chhs.data.ca.gov | Last Updated 2017-06-14T18:09:22.000Z

    This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity (https://www.cdph.ca.gov/Programs/OHE/Pages/Healthy-Communities-Data-and-Indicators-Project-(HCI).aspx). Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

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    Point In Time Homeless Survey Data

    data.sonomacounty.ca.gov | Last Updated 2018-07-30T16:40:53.000Z

    The County of Sonoma conducts an annual homeless count for the entire county. The survey data is derived from a sample of about 600 homeless persons countywide per year. The resulting information is statistically reliable only for the county as a whole, not for individual locations. The exception is the City of Santa Rosa, where the sample taken within the city is large enough to be predictive of the overall homeless population in that city.