- What is the Population Rate of Change?
- What is the Population Density?
- What is the Land Area?
- 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 population count of Juneau City and Borough, AK was 32,524 in 2016.
Demographics and Population Datasets Involving Juneau City and Borough, AK
- API 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.
- API data.smcgov.org | Last Updated 2016-09-08T17:56:35.000Z
Census data for cities and Census Designated Places in San Mateo County. Includes population, race, Hispanic ethnicity, gender, age groups, household, family, and housing information. This data is from the 2010 United States Census Summary File 1 (SF1).
- API data.smcgov.org | Last Updated 2016-09-30T03:35:19.000Z
Demographic data for Get Healthy San Mateo County's Healthy Cities SMC: http://www.gethealthysmc.org/healthy-cities-smc
- 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 data.smcgov.org | Last Updated 2015-06-22T17:29:36.000Z
Racial composition of people in cities and places in District 4. This data comes from the American Community Survey 2013 5 year estimates. Data for Menlo Park includes the entire city, not just the eastern portion of the city in District 4.
- API data.cityofnewyork.us | Last Updated 2018-10-05T20:23:46.000Z
The New York City Work and Family Leave Survey (WFLS), conducted in March 2016, was a telephone survey of New York City residents who gave birth in 2014. Its goal was to improve understanding about the availability and accessibility of paid family leave to working parents. The WFLS also sought to describe the role that paid family leave policies play in achieving health equity for parents and children. The WFLS was made possible through funding by the U.S. Department of Labor Women’s Bureau.
- 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 2017-02-17T22:34:56.000Z
This table contains data on the percentage of the total population living within 1/4 mile of alcohol outlets (off-sale, on-sale, total) for California, its regions, counties, county divisions, cities, towns, and Census tracts. Population data is from the 2010 Decennial Census, while the alcohol outlet location data is from 2014 (April). Race/ethnicity stratification is included in the table. 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). Some studies have found that proximity to alcohol outlets (living within walking distance) is positively associated with outcomes like excessive alcohol consumption and other alcohol related harms like injuries and violence. More information on the data table and a data dictionary can be found in the About/Attachments section.
- API churned-data.awcnet.org | Last Updated 2014-11-03T22:01:09.000Z
This dataset provides a number of community indicators related to income, age, diversity, and educational attainment. With the exception of the 2014 Pop. Estimate and Pop. Growth 2000 to Present, indicators are derived from the US Census' American Community Survey 2012 5-year estimates.
- API data.cdc.gov | Last Updated 2017-08-28T15:09:46.000Z
This dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).