The population count of Howard County, MD was 315,327 in 2018. The population count of Arlington County, VA was 231,803 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 Arlington County, VA or Howard County, MD

  • API

    Concentrations of Protected Classes from Analysis of Impediments

    data.austintexas.gov | Last Updated 2019-07-29T17:26:04.000Z

    A new component of fair housing studies is an analysis of the opportunities residents are afforded in “racially or ethnically concentrated areas of poverty,” also called RCAPs or ECAPs. An RCAP or ECAP is a neighborhood with significant concentrations of extreme poverty and minority populations. HUD’s definition of an RCAP/ECAP is: • A Census tract that has a non‐white population of 50 percent or more AND a poverty rate of 40 percent or more; OR • A Census tract that has a non‐white population of 50 percent or more AND the poverty rate is three times the average tract poverty rate for the metro/micro area, whichever is lower. Why the 40 percent threshold? The RCAP/ECAP definition is not meant to suggest that a slightly‐lower‐than‐40 percent poverty rate is ideal or acceptable. The threshold was borne out of research that concluded a 40 percent poverty rate was the point at which a neighborhood became significantly socially and economically challenged. Conversely, research has shown that areas with up to 14 percent of poverty have no noticeable effect on community opportunity. (See Section II in City of Austin’s 2015 Analysis of Impediments to Fair Housing Choice: http://www.austintexas.gov/sites/default/files/files/NHCD/Reports_Publications/1Analysis_Impediments_for_web.pdf) This dataset provides socioeconomic data on protected classes from the 2008-2012 American Community Survey on census tracts in Austin’s city limits and designates which of those tracts are considered RCAPs or ECAPs based on these socioeconomic characteristics. A map of the census tracts designated as RCAPs or ECAPs is attached to this dataset and downloadable as a pdf (see below).

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    MD COVID-19 - MASTER Case Tracker

    opendata.maryland.gov | Last Updated 2020-10-27T14:35:11.000Z

    <b>Summary</b> The cases, tests, positivity rates, hospitalizations, and confirmed and probable deaths for COVID-19 in Maryland. <b>Description</b> The MD COVID-19 - MASTER Case Tracker is a collection of Total Cases, Total Tests, Postivity Rates, Persons Tested Negative, Total Daily Hospital Beds, Total Ever Hospitalized, Total Persons Documented to Have Completed Home Isolation, Cases by County, Cases by Age Distribution, Cases by Gender Distribution, Cases by Race and Ethnicity Distribution, Confirmed Deaths Statewide, Confirmed Deaths by Date of Death, Confirmed Deaths by County, Confirmed Deaths by Age Distribution, Confirmed Deaths by Gender Distribution, Confirmed Deaths by Race And Ethnicity Distribution, Probable Deaths Statewide, Probable Deaths by Date of Death, Probable Deaths by County, Probable Deaths by Age Distribution, Probable Deaths by Gender Distribution, and Probable Deaths by Race And Ethnicity Distribution. <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.

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    2010 Census/ACS Basic Block Group Data

    data.kcmo.org | Last Updated 2013-02-08T20:03:40.000Z

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

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    2010 Census/ACS Basic Census Tract Data

    data.kcmo.org | Last Updated 2014-06-10T19:42:31.000Z

    basic characteristics of people and housing for individual 2010 census tract portions inside or outside KCMO

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    2010 Census/ACS Basic Block Group Data

    data.kcmo.org | Last Updated 2014-06-10T19:28:50.000Z

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

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    2010 Census/ACS Basic Census Tract Data

    data.kcmo.org | Last Updated 2019-04-19T19:05:00.000Z

    basic characteristics of people and housing for individual 2010 census tract portions inside or outside KCMO

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    2010 Census/ACS Basic Block Group Data

    data.kcmo.org | Last Updated 2019-04-19T18:51:48.000Z

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

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    Virginia Social Vulnerability Index (SVI) (2018) by Census Tract (Datathon)

    data.virginia.gov | Last Updated 2020-09-17T18:09:44.000Z

    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.

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    2010 Census/ACS Detailed Block Group Data

    data.kcmo.org | Last Updated 2013-02-08T20:10:26.000Z

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

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    Point In Time Homeless Survey Data

    data.sonomacounty.ca.gov | Last Updated 2019-07-12T18:26:35.000Z

    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.