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The population density of Dana Point, CA was 5,158 in 2011.

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.

3. If you use this derived data in an app, we ask that you provide a link somewhere in your applications to the Open Data Network with a citation that states: "Data for this application was provided by the Open Data Network" where "Open Data Network" links to http://opendatanetwork.com. Where an application has a region specific module, we ask that you add an additional line that states: "Data about REGIONX was provided by the Open Data Network." where REGIONX is an HREF with a name for a geographical region like "Seattle, WA" and the link points to this page URL, e.g. http://opendatanetwork.com/region/1600000US5363000/Seattle_WA

Geographic and Population Datasets Involving Dana Point, CA

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    San Mateo County And California Crime Rates 2000-2014

    performance.smcgov.org | Last Updated 2016-08-31T20:40:07.000Z

    Violent and property crime rates per 100,000 population for San Mateo County and the State of California. The total crimes used to calculate the rates for San Mateo County include data from: Sheriff's Department Unincorporated, Atherton, Belmont, Brisbane, Broadmoor, Burlingame, Colma, Daly City, East Palo Alto, Foster City, Half Moon Bay, Hillsborough, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Bay Area DPR, BART, Union Pacific Railroad, and CA Highway Patrol.

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    Quarantine_SpottedLanternfly

    data.pa.gov | Last Updated 2024-08-13T22:58:13.000Z

    Quarantine_SpottedLanternfly

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

    health.data.ny.gov | Last Updated 2024-05-01T18:05:44.000Z

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

    health.data.ny.gov | Last Updated 2024-05-01T18:07:53.000Z

    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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    Bronx Hexagon Population ACS2011

    bronx.lehman.cuny.edu | 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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    Napa County and California Population Totals 2011-2020

    data.countyofnapa.org | Last Updated 2023-07-26T16:19:55.000Z

    Data Source: CA Department of Finance Data: Population estimates for January 1, 2011, through January 1, 2020. The population estimates benchmark for April 1, 2010 is also provided. Citation: State of California, Department of Finance, E-4 Population Estimates for Cities, Counties, and the State, 2011-2020, with 2010 Census Benchmark. Sacramento, California, May 2022. For detailed information on methodology and other data considerations, visit: https://dof.ca.gov/Forecasting/Demographics/Estimates/e-4-population-estimates-for-cities-counties-and-the-state-2011-2020-with-2010-census-benchmark-new/

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    Chicago COVID-19 Community Vulnerability Index (CCVI)

    data.cityofchicago.org | Last Updated 2022-02-03T23:22:50.000Z

    The Chicago CCVI identifies communities that have been disproportionately affected by COVID-19 and are vulnerable to barriers to COVID-19 vaccine uptake​. Vulnerability is defined as a combination of sociodemographic factors, epidemiological factors​, occupational factors​, and cumulative COVID-19 burden. The 10 components of the index include COVID-19 specific risk factors and outcomes and social factors known to be associated with social vulnerability in the context of emergency preparedness. The CCVI is derived from ranking values of the components by Chicago Community Area, then synthesizing them into a single composite weighted score. The higher the score, the more vulnerable the geographic area. ZIP Code CCVI is included to enable comparison with other COVID-19 data available on the Chicago Data Portal. Some elements of the CCVI are not available by ZIP Code. To create ZIP Code CCVI, the proportion of the ZIP Code population contributed by each Community Areas was determined. The apportioned populations were then weighted by the Community Area CCVI score and averaged to determine a ZIP Code CCVI score. The COVID-19 Community Vulnerability Index (CCVI) is adapted and modified from a Surgo Ventures collaboration (https://precisionforcovid.org/ccvi) and the CDC Social Vulnerability Index​. ZIP Codes are based on ZIP Code Tabulation Areas (ZCTAs) developed by the U.S. Census Bureau. For full documentation see: https://www.chicago.gov/content/dam/city/sites/covid/reports/012521/Community_Vulnerability_Index_012521.pdf

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    MCAH Birth File

    data.countyofnapa.org | Last Updated 2024-02-07T17:45:49.000Z

    Data Source: CA Department of Public Health, Maternal Child and Adolescent Health Division This data biography includes information about who created this data, and how, where, when, and why it was collected. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze, and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org. How was the data collected? This data product is the result of the merging of two data files spanning different time periods. The California Birth Statistical Master File from 2007 to 2017 and the California Comprehensive Master Birth File from 2018 to 2021 that replaced the Master File. Additional metrics were included from the calculations off the source datasets. Population Density data from the US Census Bureau American Community Survey 5-year estimates: Poverty States in the past 12 months & Population density data from the California Department of Health Care Access and Information: Healthcare Workforce were included as metrics or to calculate new metrics. Who was included and excluded from the data? Birth records from all live births of birthing parent resident of California collected by vital statistics offices throughout the state. Where was the data collected?  Data was collected for all California counties as well as for the state of California. When was the data collected? 2007-2021 Where can I learn more about this data? Data dictionary for the source files used to build the data product can be found here. Detailed definitions assumed for this data product as well as comments on some of the methodologies applied can be found here. For more information overall, please refer to https://www.cdph.ca.gov/Programs/CFH/DMCAH/surveillance/CDPH%20Document%20Library/Data-Dashboards/About-the-Data-Prenatal-Care.pdf.

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    Vital Signs: Life Expectancy – by ZIP Code

    data.bayareametro.gov | Last Updated 2018-07-06T18:05:06.000Z

    VITAL SIGNS INDICATOR Life Expectancy (EQ6) FULL MEASURE NAME Life Expectancy LAST UPDATED April 2017 DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time. DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/ U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population. Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly im

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    Bronx Population 2000 - 2010

    bronx.lehman.cuny.edu | Last Updated 2019-02-15T18:21:21.000Z

    American Fact Finder data compiled to illustrate population and population density changes in the Bronx from 2000 to 2010