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

Population growth drives increasing demand for housing, jobs, food, education, transportation and many services. Population decline is the flip side of that dynamic, creating its own pressures on local business, government, housing and people.This map shows which areas are under significant pressure from population growth or decline. As the population of the U.S. continues to grow, the cities and the suburbs are experiencing changes in their population density. This map shows areas of declining density in brown, and high growth in dark green.Red areas will lose population by 2015, while green areas will grow. Darker green areas will grow more than 1.25% per year. Click on the map for details about an area. Use this map as a backdrop for your organization's locations, services areas, or other subjects. There is also a simple app showing this web map.You candownload the data from this map package.

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.

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.

At high densities, land uses get close enough to each other to support walk, bike, and transit modes above 60% of total trips. The San Francisco Bay Area census was used to define five density levels: rural, exurban, suburban, central city, and urban core. The urban core definition, over 50 persons per neighborhood acre, is much denser than in other research. The California Household Transportation Survey supplied new data on block group area, population, trip stages, trip distances, trip time, and travel mode by density. The National Household Transportation Survey supplied block group population, density, travel mode, and income data. Both sources show a strong nonlinear relationship going from rural to urban core: auto miles and trips decrease as walk and transit miles and trips increase. With density, people travel fewer miles and spend less time traveling. High-income households in dense areas travel far fewer miles than those living at higher densities. With sufficient density, complementary features play a role in furthering mode shift. For planning purposes, the need for parking greatly declines. The findings are a basis for similar research elsewhere on high densities and complementary features.

Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%.

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.

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

Pacific herring (Clupea pallasii) is an ecologically and commercially valuable forage fish in the North Pacific Ocean. However, knowledge gaps exist around the abiotic and biotic drivers behind its variable population dynamics–as well as on the ability of the species to show spatially structured trends that stabilize population portfolios in the face of environmental change. Here we examined how historical hydroclimatic variability in the San Francisco Estuary (California) has driven age-0 Pacific herring population dynamics over 35 years. First, we used wavelet analyses to examine spatio-temporal variation and synchrony in the environment, focusing on two key variables: salinity and temperature. Next, we fitted Multivariate Autoregressive State-Space models to environmental and abundance time series to test for spatial structure and to parse out abiotic (salinity and temperature) from biotic influences (spawning and density dependence). Finally, we examined the stabilizing effects of spatially asynchronous population fluctuations (i.e., portfolio effects) across the estuary. Our results showed that temperature, but not salinity, fluctuated synchronously across regions on seasonal and decadal timescales. The top-ranked model showed strong evidence of regional population structure and regional variation in population responses to the environment. As expected, age-0 herring were generally associated with cooler, saltier conditions in spring. Density dependence was strong in all regions, suggesting that local factors influencing rearing conditions limited juvenile population growth across the estuary. Notably, age-0 abundance fluctuations were on average 15% more stable across the estuary than in individual regions, demonstrating that portfolio effects arising from population asynchrony have been helping to stabilize recruitment across the estuary over the past four decades. We contend that ecosystem-based fishery management strategies to restore eelgrass and tidal-marsh-rearing habitats could increase the carrying capacity of the estuary, further stabilizing the herring population and reducing the risk of fishery closures.

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.

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.

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 populations. We detected five clusters of nSSR genotypes and three groups of cpSSR haplotypes across the species range consisting of parapatric groups of populations that usually (but not always) differed in mating system, suggesting that selfing may often initiate ecogeographic isolation. However, lineage-wide genetic variation was not lower for selfing clusters, failing to support the hypothesis that selection for reproductive assurance spurred the evolution of selfing in this species. Within three populations where LF and SF plants coexist we detected genetic differentiation among diverged floral phenotypes suggesting that reproductive isolation (probably postzygotic) may help maintain the striking mating system differentiation observed across the range of this species

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 50 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 88.7 index points, while the 20th city in the ranking, St. Louis, MO, received 65.7 index points.

description: The federal and state endangered California clapper rail, Rallus longirostris obsoletus. is a species that, until very recently, was on the verge of extinction. This secretive marsh bird's decline began over 100 years ago in the pristine marshes of San Francisco Bay (Bay) and the California coast (Fig. 1). In the earlier part of this century, the rail was found as far north as Humboldt Bay pd as far south as Morro Bay (Gill 1979) (Fig. 2). In the early 80s, the last known pair of rails outside of the Bay was seen at Elkhorn Slough in Monterey County. During the first half of this century, exploitation of the Bay's natural resources, including unrestricted filling and diking of the tidal marshes, began shrinking the rail's habitat in San Pablo Bay, Central and South San Francisco Bay from over 51,000 hectares to less than 9,000 hectares that now remain today (Dedrick 1993). The cumulative effects from this continued loss of critical habitat, combined with recent threats from increased predation, probable contamination, and other stresses associated with expanding urban growth, has created a crisis for our bay's indigenous rail. After the rail was listed as Endangered under the authority of the Endangered Species Act by the U.S. Fish and Wildlife Service (Service) in 1970, censuses of the population in the Bay were initiated. In the early 1970s, Gill estimated the total California clapper rail population at 4200 to 6000 individuals (1979). Surveys for the rail continued into the 80s (Moss 1980), with Harvey providing an estimate of 1200-1500 rails in 1981. The survey by Harvey was more accurate than the Gill estimate because an actual count was made, as compared to an average density which Gill applied to all suitable habitat. Subsequent censuses were sporadic and incomplete (Harvey 1987) until the Service, led by the San Francisco Bay National Wildlife Refuge (Refuge) began winter high tide surveys of South San Francisco Bay (South Bay) in 1988 (Foerster 1989). The Service began to suspect that the rail was in serious decline after the Refuge conducted a thorough survey of major South Bay marshes in the winter of 1988-89 and estimated a total population of only 700 rails. It was discovered that populations of rails in marshes on the east side of the bay were suffering the greatest declines and that predation by non-native predators was implicated as a primary factor (Foerster 1989). This hypothesis was confirmed by data collected by the Refuge and subsequently an Environmental Assessment and Predator Management Plan was implemented in March 1991 (Foerster and Takekawa 1991). Since 1988, the Refuge has continued to conduct annual winter high tide surveys of South Bay rail populations and some San Pablo Bay (North Bay) subpopulations (Figs. 2 and 3), with the assistance of the California Department of Fish and Game (CDFG) and other local organizations such as the San Francisco Bay Bird Observatory. This report summarizes data collected between November 1989 and January 1993, encompassing four annual winter surveys. During the last two years, the Refuge also initiated research into several factors which were implicated in rail population decline. The factors which were identified as significantly affecting rail survival included predation by non-native predators (Foerster and Takekawa 1991), and high levels of heavy metals in prey species (Lonzarich, et al. 1992). Continued analysis of these factors by the Service will culminate in a several reports to be released in late 1994.; abstract: The federal and state endangered California clapper rail, Rallus longirostris obsoletus. is a species that, until very recently, was on the verge of extinction. This secretive marsh bird's decline began over 100 years ago in the pristine marshes of San Francisco Bay (Bay) and the California coast (Fig. 1). In the earlier part of this century, the rail was found as far north as Humboldt Bay pd as far south as Morro Bay (Gill 1979) (Fig. 2). In the early 80s, the last known pair of rails outside of the Bay was seen at Elkhorn Slough in Monterey County. During the first half of this century, exploitation of the Bay's natural resources, including unrestricted filling and diking of the tidal marshes, began shrinking the rail's habitat in San Pablo Bay, Central and South San Francisco Bay from over 51,000 hectares to less than 9,000 hectares that now remain today (Dedrick 1993). The cumulative effects from this continued loss of critical habitat, combined with recent threats from increased predation, probable contamination, and other stresses associated with expanding urban growth, has created a crisis for our bay's indigenous rail. After the rail was listed as Endangered under the authority of the Endangered Species Act by the U.S. Fish and Wildlife Service (Service) in 1970, censuses of the population in the Bay were initiated. In the early 1970s, Gill estimated the total California clapper rail population at 4200 to 6000 individuals (1979). Surveys for the rail continued into the 80s (Moss 1980), with Harvey providing an estimate of 1200-1500 rails in 1981. The survey by Harvey was more accurate than the Gill estimate because an actual count was made, as compared to an average density which Gill applied to all suitable habitat. Subsequent censuses were sporadic and incomplete (Harvey 1987) until the Service, led by the San Francisco Bay National Wildlife Refuge (Refuge) began winter high tide surveys of South San Francisco Bay (South Bay) in 1988 (Foerster 1989). The Service began to suspect that the rail was in serious decline after the Refuge conducted a thorough survey of major South Bay marshes in the winter of 1988-89 and estimated a total population of only 700 rails. It was discovered that populations of rails in marshes on the east side of the bay were suffering the greatest declines and that predation by non-native predators was implicated as a primary factor (Foerster 1989). This hypothesis was confirmed by data collected by the Refuge and subsequently an Environmental Assessment and Predator Management Plan was implemented in March 1991 (Foerster and Takekawa 1991). Since 1988, the Refuge has continued to conduct annual winter high tide surveys of South Bay rail populations and some San Pablo Bay (North Bay) subpopulations (Figs. 2 and 3), with the assistance of the California Department of Fish and Game (CDFG) and other local organizations such as the San Francisco Bay Bird Observatory. This report summarizes data collected between November 1989 and January 1993, encompassing four annual winter surveys. During the last two years, the Refuge also initiated research into several factors which were implicated in rail population decline. The factors which were identified as significantly affecting rail survival included predation by non-native predators (Foerster and Takekawa 1991), and high levels of heavy metals in prey species (Lonzarich, et al. 1992). Continued analysis of these factors by the Service will culminate in a several reports to be released in late 1994.

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