- What is the Population Count?
- What is the Land Area?
- What is the Percent who did not finish the 9th grade?
- What is the Student Teacher Ratio?
- What is the Median Earnings?
- What is the Mean Job Proximity Index?
- What is the Number of Employees?
- What is the Percent Without Health Insurance?
- What is the Mean Environmental Health Hazard Index?
- What is the Access to Exercise Opportunities Rate?
The population density of Hamilton County, OH was 1,992 in 2017.
Geographic and Population Datasets Involving Hamilton County, OH
- API data.wa.gov | Last Updated 2016-08-09T16:23:33.000Z
Population and housing information extracted from decennial census Public Law 94-171 redistricting summary files for Washington state for years 2000 and 2010.
- API data.wa.gov | Last Updated 2016-08-09T16:28:24.000Z
Washington state population density by county by decade 1900 to 2010.
Hospital Inpatient Prevention Quality Indicators (PQI) for Adult Discharges by County (SPARCS): Beginning 2009health.data.ny.gov | Last Updated 2018-03-22T19:12:25.000Z
This is one of two datasets that contain observed and expected rates for Agency for Healthcare Research and Quality Prevention Quality Indicators – Adult (AHRQ PQI) beginning in 2009. This dataset is at the county level. The Agency for Healthcare Research and Quality (AHRQ) Prevention Quality Indicators (PQIs) are a set of population based measures that can be used with hospital inpatient discharge data to identify ambulatory care sensitive conditions. These are conditions where 1) the need for hospitalization is potentially preventable with appropriate outpatient care, or 2) conditions that could be less severe if treated early and appropriately. All PQIs apply only to adult populations (over the age of 18 years). The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data and Claritas population information. The observed rates and expected rates for each AHRQ PQI is presented by either resident county (including a statewide total) or resident zip code (including a statewide total).
- API opendata.maryland.gov | Last Updated 2019-07-16T15:59:38.000Z
Population profile - total, rate of change, age, and density.
- API health.data.ny.gov | Last Updated 2019-05-30T13:01:20.000Z
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 health.data.ny.gov | Last Updated 2019-05-17T16:48:55.000Z
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 data.novascotia.ca | Last Updated 2016-04-11T11:53:55.000Z
This table reports population density. This data is sourced from the Census of Population. Geographies available: provinces, counties, communities, municipalities, district health authorities, community health boards, economic regions, police districts, school boards, municipal electoral districts, provincial electoral districts, federal electoral districts, regional development authorities, watersheds
- API data.cdc.gov | Last Updated 2018-06-04T12:49:09.000Z
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 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 opendata.utah.gov | Last Updated 2019-02-11T21:26:09.000Z
This data set contains population density by land area and county of residence in Utah for 2010.