The population density of Staunton city, VA was 1,201 in 2013.

Population Density

Population Density is computed by dividing the total population by Land Area Per Square Mile.

Above charts are based on data from the U.S. Census American Community Survey | 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.

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Geographic and Population Datasets Involving Staunton city, VA

  • API

    WAOFM - Census - Population and Housing, 2000 and 2010 | Last Updated 2021-09-01T17:20:31.000Z

    Population and housing information extracted from decennial census Public Law 94-171 redistricting summary files for Washington state for years 2000 and 2010.

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    Health Opportunity Index | Last Updated 2021-10-07T19:00:36.000Z

    The Virginia Health Opportunity Index (HOI) is a group of indicators that provide broad insight into the overall opportunity Virginians have to live long and healthy lives based on the Social Determinants of Health. It is a hierarchical index that allows users to examine social determinants of health at multiple levels of detail in Virginia. It is made up of over 30 variables, combined into 13 indicators, grouped into four profiles, which are aggregated into a single Health Opportunity Index. For more information visualizations visit:

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    Deer Tick Surveillance: Adults (Oct to Dec) excluding Powassan virus: Beginning 2008 | Last Updated 2022-03-23T14:44:39.000Z

    This dataset provides the results from collecting and testing adult deer ticks, also known as blacklegged ticks, or by their scientific name <i>Ixodes scapularis</i>. Collection and testing take place across New York State (excluding New York City) from October to December, when adult deer ticks are most commonly seen. Adult deer ticks are individually tested for different bacteria and parasites, which includes the bacteria responsible for Lyme disease. These data should simply be used to educate people that there is a risk of coming in contact with ticks and tick-borne diseases. These data only provide adult tick infections at a precise location and at one point in time. Both measures, tick population density and percentage, of ticks infected with the specified bacteria or parasite can vary greatly within a very small area and within a county. These data should not be used to broadly predict disease risk for a county. Further below on this page you can find links to tick prevention tips, a video on how to safely remove a tick, and more datasets with tick testing results. Interactive charts and maps provide an easier way to view the data.

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    VDH-PublicUseDataset-NCHS-Population | Last Updated 2021-10-07T19:01:09.000Z

    This dataset includes population estimates for each Virginia locality by year, age group, sex, race and ethnicity. This estimates are produced by the National Center for Health Statistics (NCHS) within the Centers for Disease Control and Prevention (CDC), more information can be found here:

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    Bronx Hexagon Population ACS2011 | Last Updated 2019-02-15T18:22:38.000Z

    Population per hexagon, using 5-year American Community Survey data from 2011. Since each hexagon is equivalent in area, this also serves as a population density map. The data was received as population per census tract. Then a ratio was created: Tract Population/Tract Area = Hexagon Population/Hexagon Area. This was rearranged so that: Hexagon population = HexArea(TractPop/TractArea).

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    Census 2006 - Total Population By Census Year | Last Updated 2021-06-10T22:22:08.000Z

    Total population, land area, and population density of neighbourhoods, neighbourhood clusters, wards, community areas, and custom areas including downtown and the entire city beginning with the 1971 census.

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    Library Active Users Statistics | Last Updated 2022-05-09T12:21:10.000Z

    This dataset contains information about Norfolk Public Library active users by month starting in July 2016. This information will be updated annually. Active users include users whose accounts have had any activity during the previous three years including card renewal, fines added, payments made, or computer usage. This dataset is updated monthly.

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    Urban and Rural Census Designations (2010) by Locality | Last Updated 2020-12-31T21:10:52.000Z

    This table uses U.S. Census data to create a dataset that identifies all Virginia localities as either Mostly Urban, Mostly Rural or Completely Rural. Total population and breakdown between urban and rural populations are included. For information on the U.S. Census Bureau's use of these designations see (Source data for this dataset is found at this link and is titled "County Classification Lookup Table [XLS]".)

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    NCHS - Drug Poisoning Mortality by County: United States | Last Updated 2022-03-30T13:15:49.000Z

    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).

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    Virginia County deaths by day (Datathon) | Last Updated 2020-10-01T12:03:43.000Z

    This dataset is an export from Opportunity Insights Economic Tracker ( The data in this dataset was last updated September 17, 2020. More current data is available at the project's GitHub repository: From the Web site: The Opportunity Insights Economic Tracker ( combines anonymized data from leading private companies – from credit card processors to payroll firms – to provide a real-time picture of indicators such as employment rates, consumer spending, and job postings across counties, industries, and income groups. All of the data displayed on the Economic Tracker can be downloaded here. In collaboration with our data partners, we are making this data freely available in order to assist in efforts to inform the public, policymakers, and researchers about the real-time state of the economy and the effects of COVID-19. Anyone is welcome to use this data; we simply we ask that you attribute our work by citing or linking to the accompanying paper and the Economic Tracker at