The population rate of change of San Francisco County, CA was 0.67% in 2018.

Population

Population Change

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

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Demographics and Population Datasets Involving San Francisco County, 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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    Projections 2040 by Jurisdiction

    data.bayareametro.gov | Last Updated 2019-05-01T23:00:49.000Z

    Forecasts for Year 2010 through 2040 containing values for Households by Inc. Quartile; Households; Jobs; Population by Gender, Age; Units; Employed Residents; Population by Age; Population for jurisdictions in the nine county San Francisco Bay Area region.

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    COVID-19 Cases and Deaths Summarized by Geography

    data.sfgov.org | Last Updated 2020-12-05T15:32:26.000Z

    <strong>A. SUMMARY</strong> Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2018 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents. Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset. Dataset is cumulative and covers cases going back to March 2nd, 2020 when testing began. It is updated daily. Geographic areas summarized are: 1. <a href="https://data.sfgov.org/Geographic-Locations-and-Boundaries/Analysis-Neighborhoods/p5b7-5n3h">Analysis Neighborhoods</a> 2. <a href="https://data.sfgov.org/Geographic-Locations-and-Boundaries/Census-2010-Tracts-for-San-Francisco/rarb-5ahf">Census Tracts</a> 3. <a href="https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html">Census Zip Code Tabulation Areas</a> <strong>B. HOW THE DATASET IS CREATED</strong> Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2018 ACS estimates for population provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents. <strong>C. UPDATE PROCESS</strong> Geographic analysis is scripted by SFDPH staff and synced to this dataset each day. <strong>D. HOW TO USE THIS DATASET</strong> <em>Privacy rules in effect</em> To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000 <em>Rate suppression in effect where counts lower than 20</em> Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. <em>A note on Census ZIP Code Tabulation Areas (ZCTAs)</em> ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. <a href="https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html">Read how the Census develops ZCTAs on their website</a>. <em>Row included for Citywide case counts, incidence rate, and deaths</em> A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling bases.

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    Demographics For Unincorporated Areas In San Mateo County

    datahub.smcgov.org | Last Updated 2018-10-25T21:45:46.000Z

    Demographics, including median income, total population, race, ethnicity, and age for unincorporated areas in San Mateo County. This data comes from the 2012 American Community Survey 5 year estimates DP03 and DP05 files. They Sky Londa area is located within two Census Tracts. The data for Sky Londa is the sum of both of those Census Tracts. Users of this data should take this into account when using data for Sky Londa.

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    Final Projections 2017 By Jurisdiction

    data.bayareametro.gov | Last Updated 2018-09-07T19:52:32.000Z

    Forecasts for Year 2010 through 2040 containing values for Households by Inc. Quartile; Households; Jobs; Population by Gender, Age; Units; Employed Residents; Population by Age; Population for jurisdictions in the nine county San Francisco Bay Area region.

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    Economy and Education Indicators, San Bernardino County and California, 2005-2014

    data.communityvitalsigns.org | Last Updated 2016-03-01T19:43:41.000Z

    This dataset contains high school graduation rates from 2010-2014 for San Bernardino County and California (from California Department of Education, California Longitudinal Pupil Achievement Data System, Cohort Outcome Data by Gender Report), and percentage of the adult population age 25 years and older with a bachelor's degree or higher, median household income in the past 12 months (adjusted annually for inflation), and unemployment rate for the population age 16 years and older, for San Bernardino County and California from 2005-2014 (U.S. Census Bureau, American Community Survey 1-Year Estimates, Tables B19013, S1501 and S2301).

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    Draft Projections 2017 by Subregional Study Areas

    data.bayareametro.gov | Last Updated 2018-01-10T04:21:51.000Z

    Forecasts for Year 2010 through 2040 containing values for Households by Inc. Quartile; Households; Jobs; Population by Gender, Age; Units; Employed Residents; Population by Age; Population for Subregional Study Areas (Sphere's of Influence of Jurisdictions) in the nine county San Francisco Bay Area region.

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    2010 Census/ACS Basic Block Group Data

    data.kcmo.org | Last Updated 2013-02-08T20:03:40.000Z

    basic characteristics of people and housing for individual 2010 census block groups

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    Concentrations of Protected Classes from Analysis of Impediments

    data.austintexas.gov | Last Updated 2019-07-29T17:26:04.000Z

    A new component of fair housing studies is an analysis of the opportunities residents are afforded in “racially or ethnically concentrated areas of poverty,” also called RCAPs or ECAPs. An RCAP or ECAP is a neighborhood with significant concentrations of extreme poverty and minority populations. HUD’s definition of an RCAP/ECAP is: • A Census tract that has a non‐white population of 50 percent or more AND a poverty rate of 40 percent or more; OR • A Census tract that has a non‐white population of 50 percent or more AND the poverty rate is three times the average tract poverty rate for the metro/micro area, whichever is lower. Why the 40 percent threshold? The RCAP/ECAP definition is not meant to suggest that a slightly‐lower‐than‐40 percent poverty rate is ideal or acceptable. The threshold was borne out of research that concluded a 40 percent poverty rate was the point at which a neighborhood became significantly socially and economically challenged. Conversely, research has shown that areas with up to 14 percent of poverty have no noticeable effect on community opportunity. (See Section II in City of Austin’s 2015 Analysis of Impediments to Fair Housing Choice: http://www.austintexas.gov/sites/default/files/files/NHCD/Reports_Publications/1Analysis_Impediments_for_web.pdf) This dataset provides socioeconomic data on protected classes from the 2008-2012 American Community Survey on census tracts in Austin’s city limits and designates which of those tracts are considered RCAPs or ECAPs based on these socioeconomic characteristics. A map of the census tracts designated as RCAPs or ECAPs is attached to this dataset and downloadable as a pdf (see below).

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    Employment Data by City

    datahub.smcgov.org | Last Updated 2016-08-10T18:35:32.000Z

    Employment and unemployment data by city for places in San Mateo County. CDP is "Census Designated Place" - a recognized community that was unincorporated at the time of the 2000 Census. 1) Data may not add due to rounding. All unemployment rates shown are calculated on unrounded data. 2) These data are not seasonally adjusted. Methodology: Monthly city and CDP labor force data are derived by multiplying current estimates of county employment and unemployment by the employment and unemployment shares (ratios) of each city and CDP at the time of the 2000 Census. Ratios for cities of 25,000 or more persons were developed from special tabulations based on household population only from the Bureau of Labor Statistics. For smaller cities and CDP, ratios were calculated from published census data. City and CDP unrounded employment and unemployment are summed to get the labor force. The unemployment rate is calculated by dividing unemployment by the labor force. Then the labor force, employment, and unemployment are rounded. This method assumes that the rates of change in employment and unemployment, since 2000, are exactly the same in each city and CDP as at the county level (i.e., that the shares are still accurate). If this assumption is not true for a specific city or CDP, then the estimates for that area may not represent the current economic conditions. Since this assumption is untested, caution should be employed when using these data.