This raster dataset depicts the population denisty of the nine county San Francisco Bay Area Region, California produced with a Dasymetric Mapping Technique, which is used to depict quantitative areal data using boundaries that divide an area into zones of relative homogeneity with the purpose of better portraying the population distribution. The source data was then adjusted in order to get convert the units to persons per acre. This dataset is an accurate representation of population distribution within census boundaries and can be used in a number of ways, including as the Conservation Suitability layer for the Marxan inputs and the watershed integrity analysis.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

This data set represents 2000 population density by block group as a 100-m grid using data from the 2000 Census of Population and Housing. The demographic data is from CensusCD 2000 Short Form Blocks published by GeoLytics, E. Brunswick, NJ, which uses the 2000 Census Summary File 1 (SF 1). Grid cell values represent population density in people per square kilometer multiplied by 10 so that the data could be stored as integer.

This polygon shapefile depicts a watershed integrity cluster analysis at the CalWater 2.2.1 Planning Watershed (PWS) level performed by mapping factors representing some of the most significant watershed threats. Each of the individual watershed integrity factors was individually mapped and then combined in the watershed cluster analysis. This individual threat, cultivated, was created by taking CalWater watersheds at the planning unit level (most refined) and running zonal stats, part of spatial analyst. The Calwater PWS watershed was the zone dataset (pwsname as the zone field) and Population Density as the value raster. The result gives you the mean percent population density of the nine county San Francisco Bay Area Region, California at the watershed level in a table that you can join back to the CalWater GIS layer and then symbolize as a graduated color with the mean being the value field. This analysis was done by the Conservation Lands Network Fish and Riparian Focus Team.

The geographic distribution of human population is key to understanding the effects of humans on the natural world and how natural events such as storms, earthquakes, and other natural phenomenon affect humans. Dataset SummaryThis layer was created with a model that combines imagery, road intersection density, populated places, and urban foot prints to create a likelihood surface. The likelihood surface is then used to create a raster of population with a cell size of 0.00221 degrees (approximately 250 meters).The population raster is created usingDasymetriccartographic methods to allocate the population values in over 1.6 million census polygons covering the world.The population of each polygon was normalized to the 2013 United Nations population estimates by country.Each cell in this layer has an integer value depicting the number of people that are likely to reside in that cell. Tabulations based on these values should result in population totals that more accurately reflect the population of areas of several square kilometers.This layer has global coverage and was published by Esri in 2014.More information about this layer is available:Building the Most Detailed Population Map in the World

This Dataset shows some basic demographic data from the US census located around the San Francisco MSA at tract level. Attributes include Average age, female and male population, white population, hispanic population, population density, and total population.

2020 Census tracts for the San Francisco Bay Region. Features were extracted from California 2021 TIGER/Line shapefile by the Metropolitan Transportation Commission.Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses.Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline.Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy.In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.The Census Bureau uses suffixes to help identify census tract changes for comparison purposes. Local participants have an opportunity to review the existing census tracts before each census. If local participants split a census tract, the split parts usually retain the basic number, but receive different suffixes. In a few counties, local participants request major changes to, and renumbering of, the census tracts. Changes to individual census tract boundaries usually do not result in census tract numbering changes.Relationship to Other Geographic Entities—Within the standard census geographic hierarchy, census tracts never cross state or county boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian, Alaska Native, and Native Hawaiian areas.

California was the state with the highest resident population in the United States in 2024, with 39.43 million people. Wyoming had the lowest population with about 590,000 residents. Living the American Dream Ever since the opening of the West in the United States, California has represented the American Dream for both Americans and immigrants to the U.S. The warm weather, appeal of Hollywood and Silicon Valley, as well as cities that stick in the imagination such as San Francisco and Los Angeles, help to encourage people to move to California. Californian demographics California is an extremely diverse state, as no one ethnicity is in the majority. Additionally, it has the highest percentage of foreign-born residents in the United States. By 2040, the population of California is expected to increase by almost 10 million residents, which goes to show that its appeal, both in reality and the imagination, is going nowhere fast.

Evolutionary transitions from outcrossing to selfing can strongly affect the genetic diversity and structure of species at multiple spatial scales. We investigated the genetic consequences of mating system shifts in the North American, Pacific coast dune endemic plant Camissoniopsis cheiranthifolia (Onagraceae) by assaying variation at 13 nuclear (n) and six chloroplast (cp) microsatellite (SSR) loci for 38 populations across the species range. As predicted from the expected reduction of effective population size (Ne) caused by selfing, small-flowered, predominantly selfing (SF) populations had much lower nSSR diversity (but not cpSSR) than large flowered, predominantly outcrossing (LF) populations. The reduction of nSSR diversity was greater than expected from the effects of selfing on Ne alone, but could not be accounted for by indirect effects of selfing on population density. Although selfing should reduce gene flow, SF populations were not more genetically differentiated than LF po...

Difference of density estimates for the subsampled datasets compared to the full dataset, showing mean, absolute mean, and absolute max summaries.

This polygon shapefile depicts the suitability layer developed for use with Marxan estimates of ecological integrity to identify areas that are best suited to conserve target species in the nine county San Francisco Bay Area Region, California. Parcelization (Upland Habitat Goals), population density (USGS), and distance to paved roads (USGS) were chosen to estimate suitability because all three contribute to habitat degradation and fragmentation. Larger, intact regions are considered to be of higher ecological integrity. These three factors were summed to create the total suitability index for every hexagonal planning unit, displaying areas of low suitability (urban, close to roads, high population density) and more suitability (larger parcels, further distance to roads, lower population density). This layer was key input into the Marxan modeling process and helps encourage the model to capture large, intact landscapes and generally stay away from fragmented and converted lands.

San Francisco, CA, New York, NY, and Jersey City, NJ were the most pedestrian friendly cities in the United States in 2021. The source analyzed the walking routes of different locations in the ** largest cities in the country to different amenities, as well as additional metrics, such as population density, block length, and intersection density. San Francisco, CA received **** index points, while the **** city in the ranking, St. Louis, MO, received **** index points.

Human density (people/km2) estimates are from the 2000 US census, summarized in the California Department of Finance (www.dof.ca.gov). Expenditure estimates are from the US Fish and Wildlife endangered and threatened species expenditure reports for 2006 through 2008. Expenditures are reported by species and we used species distribution maps to identify species within counties and averaged species expenditures within census tracts across distributions. Regression coefficients and p-values are for human density versus the expenditures by census tract.