The mean job proximity index of District of Columbia, DC was 91 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 District of Columbia, DC

  • API

    NCHS - Injury Mortality: United States

    data.cdc.gov | Last Updated 2018-06-15T13:22:36.000Z

    This dataset describes injury mortality in the United States beginning in 1999. Two concepts are included in the circumstances of an injury death: intent of injury and mechanism of injury. Intent of injury describes whether the injury was inflicted purposefully (intentional injury) and, if purposeful, whether the injury was self-inflicted (suicide or self-harm) or inflicted by another person (homicide). Injuries that were not purposefully inflicted are considered unintentional (accidental) injuries. Mechanism of injury describes the source of the energy transfer that resulted in physical or physiological harm to the body. Examples of mechanisms of injury include falls, motor vehicle traffic crashes, burns, poisonings, and drownings (1,2). Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia. Age-adjusted death rates (per 100,000 standard population) are based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of injury death are classified by the International Classification of Diseases, Tenth Revision (ICD–10). Categories of injury intent and injury mechanism generally follow the categories in the external-cause-of-injury mortality matrix (1,2). Cause-of-death statistics are based on the underlying cause of death. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES 1. National Center for Health Statistics. ICD–10: External cause of injury mortality matrix. 2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. 3. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf. 4. Miniño AM, Anderson RN, Fingerhut LA, Boudreault MA, Warner M. Deaths: Injuries, 2002. National vital statistics reports; vol 54 no 10. Hyattsville, MD: National Center for Health Statistics. 2006.

  • API

    Survey Of School System Finances Tables 2014 By District

    opendata.utah.gov | Last Updated 2017-03-22T17:34:31.000Z

    Public Elementary–Secondary Education Finance Data. Education finance data include revenues, expenditures, debt, and assets [cash and security holdings] of elementary and secondary public school systems. Statistics cover school systems in all states, and include the District of Columbia.

  • API

    155-1.1

    performance.princegeorgescountymd.gov | Last Updated 2016-11-28T15:25:11.000Z

  • API

    Percent of vehicle occupants observed using seat belts during daytime, New Jersey, by year: Beginning 2010

    healthdata.nj.gov | Last Updated 2017-02-17T19:39:05.000Z

    Ratio: Percent of Population Definition: Percentage of front-seat passenger car occupants observed using seat belts in automobiles Data Source: NJLPS Division of Highway Traffic Safety - National Occupant Protection Use Survey http://www-nrd.nhtsa.dot.gov/CATS/listpublications.aspx?Id=7&ShowBy=Category * In 2011 NHTSA established new uniform criteria (23 CFR Part 1340) for observational surveys. In the transitional period, NHTSA allows the States and Territories the option to use either the old or new criteria for 2012 surveys. In 2012, twenty-seven States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands used surveys that conformed to the new uniform criteria. From 2013 and beyond all State and Territory observational surveys will be based on the new criteria.

  • API

    155- FY19 Proposed, Performance Metrics, Obj 1.1

    performance.princegeorgescountymd.gov | Last Updated 2018-05-14T14:04:47.000Z

    Office of the Sheriff Objective 1.1- Identify and effectively mitigate the number of potential courthouse incidents per 1,000,000 visitors, FY 2019 Proposed Budget

  • API

    Opioid Seizures and Arrests Year 2013 - June 2018 County State Police

    data.pa.gov | Last Updated 2018-10-19T16:11:11.000Z

    This dataset contains summary information on opioid drug seizures and arrests made by Pennsylvania State Police (PSP) personnel, stationed statewide, on a quarterly basis. Every effort is made to collect and record all opioid drug seizures and arrests however, the information provided may not represent the totality of all seizures and opioid arrests made by PSP personnel. Data is currently available from January 1, 2013 through July 1, 2018. Seizure Opioids seized as a result of undercover buys, search warrants, traffic stops and other investigative encounters.

  • 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.

  • API

    Directory of Developmental Disabilities Service Provider Agencies

    data.ny.gov | Last Updated 2018-08-08T17:00:08.000Z

    This data set contains the address and phone number information for voluntary provider agencies of the following Office for People with Developmental Disabilities (OPWDD) supports and services: Intermediate Care Facilities (ICFs), Individual Residential Alternative (IRAs), Family Care, Self-Direction Services, Individual Support Services (ISSs), Day Training, Day Treatment, Senior/Geriatric Services, Day Habilitation, Work Shop, Prevocational, Supported Employment Enrollments, Community Habilitation, Family Support Services, Care At Home Waiver Services, and Developmental Centers And Special Population Services. The State sector district offices (DDSOs) have remained in the Developmental Disabilities Service Provider Agencies data because they too are identified by a provider agency code that identifies the voluntary providers.