- What is the Population Count?
- What is the Land Area?
- What is the Percent who did not finish the 9th grade?
- What is the Total Administration Salaries?
- What is the Student Teacher Ratio?
- What is the Median Earnings?
- What is the Number of Employees?
- What is the Percent Without Health Insurance?
- What is the Access to Exercise Opportunities Rate?
- What is the GDP per capita?
The population density of North Dakota was 11 in 2016.
Geographic and Population Datasets Involving North Dakota
- API brigades.opendatanetwork.com | Last Updated 2015-02-21T02:47:33.000Z
2012 - The population per square mile of the selected blockgroup. Data Dictionary Attached Data from American Community Survey (ACS) for 2010-2014.
- API data.sonomacounty.ca.gov | Last Updated 2018-04-26T08:20:26.000Z
Historical population data captured daily. Two figures are shown those in custody and those in outside custody but are still under the responsibility of Sonoma County Sheriff. Examples of outside custody include home confinement, state prison, hospital stays, weekend custody, and supervised by other agencies.
- API data.pa.gov | Last Updated 2017-07-31T18:19:23.000Z
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.orcities.org | Last Updated 2017-02-06T20:50:54.000Z
Population Data from Portland State University Center for Population Research
- API data.cityofnewyork.us | Last Updated 2017-12-08T16:43:06.000Z
Contains the main body of the “Development Data Book” as of January 1, 2016. The Development Data Book lists all of the Authority's Developments alphabetically and includes information on the development identification numbers, program and construction type, number of apartments and rental rooms, population, number of buildings and stories, street boundaries, and political districts.
- 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 opendata.utah.gov | Last Updated 2015-03-17T17:59:52.000Z
Incidence Of Brain And Central Nervous System Cancer Age 15 Under Per 1,000,000 All States
- API brigades.opendatanetwork.com | Last Updated 2015-02-21T02:49:07.000Z
Most recent Demographic, % Racial, Unmaintained Properties Data, etc. See Data Key for descriptions: LIST: Total Population Population Density White/Caucasian Black/African American Asian Hispanic/Latino Other Race Median Age Race/Ethnic Diversity Youth Population Retirement-Age Population Land Use Diversity Median Household Income Per Capita Income Residential Building Permit Values Commercial Building Permit Values Supplemental Security Income Crimes Involving Property Crimes with a Violent Component Drug-Related Crimes Renter-Occupied Housing Rent 30% or More of Income Monthly Owners Costs 30% or More of Income Minimum housing code violations Unmaintained Property Violations Average Age of Residential Property Percent Commuting 30 Minutes or More Percent of Commuters Driving Alone Tree Canopy Impervious Area Automotive Code Violations Sidewalk-to-Roadway Ratio Commuting to Work by Bicycle Commuting to Work by Foot Households Within Walking Distance to Bus Stops Number of Daily Bus Arrivals Number of Nightly Bus Arrivals Working from Home Households Within Walking Distance to Banks Households Within Walking Distance to Full Service Grocers Households Within Walking Distance to Pharmacies
- API data.code4sa.org | Last Updated 2015-01-25T20:33:53.000Z
- API data.edmonton.ca | Last Updated 2015-10-01T21:44:26.000Z
Capture results of mosquitoes from various locations in Edmonton. These collections are from standard New Jersey light traps that are commonly used to record changes in abundance of mosquitoes before and after control campaigns and to compare seasonal and annual fluctuations in population. Since not all mosquito species are attracted equally to light traps, the City uses a variety of other trapping and survey methods (with their own limitations) to monitor mosquitoes. Not all trap collection sites are factored into the historical averages. Some data can be incomplete due to trap failure. Some trap locations change over time. Trap collections reflect, not absolute population levels, but mosquito activity, which is influenced by changing environmental conditions (temperature, humidity, wind, etc.). The weekly averages do not include any male mosquitoes or any females of species that do not typically bite people. Each data set reflects the mosquito activity of the week previous to the collection date. To complement this dataset, there is the Rainfall Guage data which measures rainfall data in the Greater Edmonton area - https://data.edmonton.ca/Environmental-Services/Rainfall-Gauge-Results/7fus-qa4r/edit_metadata