The population count of White Oak, MD was 19,010 in 2018.

Population

Population Change

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

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Demographics and Population Datasets Involving White Oak, MD

  • API

    Maryland Counties Socioeconomic Characteristics (ACS 5-yr Estimates 2022)

    opendata.maryland.gov | Last Updated 2024-03-06T21:59:38.000Z

    Data for population, gender, race, labor force, educational attainment, income, poverty, households and housing units from the American Community Survey 5-yr Estimates, 2018-2022.

  • API

    Household Population Projections for Non-Hispanic White and All Other by Age, Sex and Race

    opendata.maryland.gov | Last Updated 2018-10-30T17:00:15.000Z

    Household Population Projections for Maryland and the jurisdictions - by age, sex and race projections out to 2045. Projections prepared by the Maryland Department of Planning, August 2018

  • API

    MD COVID-19 - Probable Deaths by Race and Ethnicity Distribution

    opendata.maryland.gov | Last Updated 2024-09-17T14:35:26.000Z

    <b>Note:</b> Starting April 27, 2023 updates change from daily to weekly. <b>Summary</b> The cumulative number of probable COVID-19 deaths among Maryland residents by race and ethnicity: African American; White; Hispanic; Asian; Other; Unknown. <b>Description</b> The MD COVID-19 - Probable Deaths by Race and Ethnicity Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by categories of race and ethnicity. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Confirmed Deaths by Race and Ethnicity Distribution data layer. <b>Terms of Use</b> The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

  • API

    Bronx Zip Population and Density

    bronx.lehman.cuny.edu | Last Updated 2012-10-21T14:06:17.000Z

    2010 Census Data on population, pop density, age and ethnicity per zip code

  • API

    Maryland Senate Districts Socioeconomic Characteristics - ACS 5-year Estimates (2018-2022)

    opendata.maryland.gov | Last Updated 2024-03-08T21:42:14.000Z

    Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates. The ACS 5-year period are period estimates that describe the average characteristics of the population and housing over the period of data collection (2018 through 2022). Data provides broad social, economics, housing, and demographics information by Maryland Senate Districts.

  • API

    Social Vulnerability Index 2018 - United States, county

    data.cdc.gov | Last Updated 2022-02-14T14:19:58.000Z

    ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event. SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking. In addition to tract-level rankings, SVI 2018 also has corresponding rankings at the county level. Notes below that describe “tract” methods also refer to county methods.

  • API

    NYSERDA Low- to Moderate-Income New York State Census Population Analysis Dataset: Average for 2013-2015

    data.ny.gov | Last Updated 2019-11-15T22:30:02.000Z

    How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

  • API

    Demographics For Unincorporated Areas In San Mateo County

    datahub.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

    2010 Census/ACS Detailed Block Group Data

    data.kcmo.org | Last Updated 2021-11-12T14:22:17.000Z

    detailed characteristics of people and housing for individual 2010 census block groups

  • API

    2018 Social Vulnerability Index

    data.brla.gov | Last Updated 2021-12-07T15:23:24.000Z

    The 2018 Social Vulnerability Index indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties in which the Census Bureau aggregates statistical data. The SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, every tract receives a ranking for each Census variable, each of the four themes, and an overall ranking.