The population density of Greensville County, VA was 39 in 2018.

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:

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Geographic and Population Datasets Involving Greensville County, VA

  • API

    VDH-COVID-19-PublicUseDataset-PCR-Percent-Positivity

    data.virginia.gov | Last Updated 2021-11-29T16:33:00.000Z

    This dataset includes the number of COVID-19 PCR testing encounters, positive PCR tests, and percent positivity for PCR tests for each locality in Virginia by 14-day lab report date periods. Data for Covington, Emporia, Lexington, and Manassas Park are represented by the data for Alleghany, Greensville, Rockbridge, and Manassas, respectively. This data set was first published on October 05, 2020.The data set increases in size daily and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon weekly. When the data set is downloaded via the "Export" option, the dates will be sorted in ascending order, meaning that the earliest date will be at the top. To see data for the most recent date, please scroll down to the bottom of the data set.

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    WAOFM - Census - Population and Housing, 2000 and 2010

    data.wa.gov | 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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    WAOFM - Census - Population Density by County by Decade, 1900 to 2010

    data.wa.gov | Last Updated 2021-09-01T17:20:22.000Z

    Washington state population density by county by decade 1900 to 2010.

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    NCHS - Drug Poisoning Mortality by County: United States

    data.cdc.gov | Last Updated 2020-06-05T17:38:49.000Z

    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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    Choose Maryland: Compare Counties - Demographics

    opendata.maryland.gov | Last Updated 2019-12-13T12:53:02.000Z

    Population profile - total, rate of change, age, and density.

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    Deer Tick Surveillance: Nymphs (May to Sept) excluding Powassan virus: Beginning 2008

    health.data.ny.gov | Last Updated 2021-05-21T14:02:02.000Z

    This dataset provides the results from collecting and testing nymph 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 May to September, when nymph deer ticks are most commonly seen. Nymph 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 nymph 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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    Virginia County deaths by day (Datathon)

    data.virginia.gov | Last Updated 2020-10-01T12:03:43.000Z

    This dataset is an export from Opportunity Insights Economic Tracker ( https://www.tracktherecovery.org/) The data in this dataset was last updated September 17, 2020. More current data is available at the project's GitHub repository: https://github.com/OpportunityInsights/EconomicTracker From the Web site: The Opportunity Insights Economic Tracker (https://tracktherecovery.org) 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 https://tracktherecovery.org.

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    Deer Tick Surveillance: Adults (Oct to Dec) excluding Powassan virus: Beginning 2008

    health.data.ny.gov | Last Updated 2021-05-21T13:37:39.000Z

    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.

  • API

    NCHS - Drug Poisoning Mortality by County: United States

    data.cdc.gov | Last Updated 2020-06-05T17:18:14.000Z

    This dataset contains model-based county estimates for drug-poisoning mortality. 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 2011–2016 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. 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. Drug poisoning death rates may be underestimated in those instances. 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. 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 for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8). REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. 2. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html. 3. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013. 4. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014. 5. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf. 6. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e. 7. National Center for Health Statistics. County geog

  • API

    NCHS - Drug Poisoning Mortality by State: United States

    data.cdc.gov | Last Updated 2020-06-05T17:28:21.000Z

    This dataset describes drug poisoning deaths at the U.S. and state 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).