The population density of Providence County, RI was 1,550 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 Providence County, RI

  • API

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

    health.data.ny.gov | Last Updated 2020-03-27T14:49:32.000Z

    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.

  • API

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

    health.data.ny.gov | Last Updated 2020-03-27T14:45:51.000Z

    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

    Deer Tick Surveillance: Adults (Oct to Dec) Powassan Virus Only: Beginning 2009

    health.data.ny.gov | Last Updated 2020-03-27T14:54:51.000Z

    This dataset provides the results from collecting and testing adult deer ticks, also known as blacklegged ticks, or by their scientific name Ixodes scapularis. 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 tested in “pools”, or groups of up to ten adult ticks per pool, for the Powassan virus, also known as Deer tick virus. 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 minimum infection rates at a precise location and at a point in time. Both measures, tick population density and minimum infection percentages, 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

    Deer Tick Surveillance: Nymphs (May to Sept) Powassan Virus Only: Beginning 2009

    health.data.ny.gov | Last Updated 2020-03-27T14:52:18.000Z

    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 tested in “pools”, or groups of up to ten adult ticks per pool, for the Powassan virus, also known as Deer tick virus. 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 minimum infection rates at a precise location and at one point in time. Both measures, tick population density and minimum infection percentages, 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

    All Payer Inpatient Quality Indicators (IQI) Volume Measures by Hospital (SPARCS): Beginning 2009

    health.data.ny.gov | Last Updated 2019-12-06T19:50:59.000Z

    The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for IQIs generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ). The IQIs are a set of measures that provide a perspective on hospital quality of care using hospital administrative data. These indicators reflect quality of care inside hospitals and include inpatient mortality for certain procedures and medical conditions; utilization of procedures for which there are questions of overuse, underuse, and misuse; and volume of procedures for which there is some evidence that a higher volume of procedures is associated with lower mortality. All the IQI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) IQI measures. The mortality, volume and utilization measures IQIs are presented by hospital as rates or counts. Area-level utilization measures are presented by county as rates.

  • API

    Uninsured Population Census Data CY 2009-2014 Human Services

    data.pa.gov | Last Updated 2019-04-01T15:15:07.000Z

    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

    All Payer Patient Safety Indicators (PSI) Volume Measures by Hospital: Beginning 2009

    health.data.ny.gov | Last Updated 2019-12-13T16:24:16.000Z

    The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ). The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses. All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures. The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

  • API

    All Payer Inpatient Quality Indicators (IQI) Composite Measures by Hospital (SPARCS): Beginning 2009

    health.data.ny.gov | Last Updated 2018-02-16T16:00:35.000Z

    The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for IQIs generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ). The IQIs are a set of measures that provide a perspective on hospital quality of care using hospital administrative data. These indicators reflect quality of care inside hospitals and include inpatient mortality for certain procedures and medical conditions; utilization of procedures for which there are questions of overuse, underuse, and misuse; and volume of procedures for which there is some evidence that a higher volume of procedures is associated with lower mortality. All the IQI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) IQI measures. The mortality, volume and utilization measures IQIs are presented by hospital as rates or counts. Area-level utilization measures are presented by county as rates.

  • API

    Census Tracts

    data.princegeorgescountymd.gov | Last Updated 2015-06-12T13:57:20.000Z

    Prince George's County population figures by demographics for 2013. Figures are provided by the U.S. Census Bureau. This dataset gets updated as new figures are published by the U.S. Census Bureau (census.gov).

  • API

    All Payer Patient Safety Indicators (PSI) Composite Measures by Hospital: Beginning 2009

    health.data.ny.gov | Last Updated 2018-03-22T18:37:32.000Z

    The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ). The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses. All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures. The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.