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

Normalized raster output of Population Density (people per sq km of land area) by block group in the State of Iowa based on U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates. Used in the Transit Dependency Analysis as part of the 2020 Iowa DOT Public Transit Long Range Plan update. This factor was one of seven utilized in the analysis that was based on MTI Report 12-30 "Investigating the Determining Factors for Transit Travel Demand by Bus Mode in US Metropolitan Statistical Areas" by the Mineta Transportation Institute of San José State University (SJSU) in May 2015. https://transweb.sjsu.edu/research/investigating-determining-factors-transit-travel-demand-bus-mode-us-metropolitan

Population Density (people per sq km of land area) by block group in the State of Iowa based on U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates. Used in the Transit Dependency Analysis as part of the 2020 Iowa DOT Public Transit Long Range Plan update. This factor was one of seven utilized in the analysis that was based on MTI Report 12-30 "Investigating the Determining Factors for Transit Travel Demand by Bus Mode in US Metropolitan Statistical Areas" by the Mineta Transportation Institute of San José State University (SJSU) in May 2015.More information on the Transit Dependency Analysis can be found in Appendix 2 of the Iowa Public Transit Long Range Plan.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

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 SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. 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 December 2022.

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 (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

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 map shows four of these densely populated areas are in California. The San Francisco-Oakland and San Jose Urban Areas rank second and third, respectively. That the New York Metropolitan area ranks fifth on this list shows that this density ranking is greatly affected by the nature of the land area designated as urban. Census Urban Areas comprise an urban core and associated suburbs. California's urban and suburban areas are more uniform in density when compared to New York's urban core and suburban periphery which have vastly different densities. Delano ranks fourth because it has a very small land area and its population is augmented by two large California State Prisons housing 10,000 inmates.

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also needs su

Cities can have profound impacts on ecosystems, yet our understanding of these impacts is currently limited. First, the effects of socioeconomic dimensions of human society are often overlooked. Second, correlative analyses are common, limiting our causal understanding of mechanisms. Third, most research has focused on terrestrial systems, ignoring aquatic systems that also provide important ecosystem services. Here we compare the effects of human population density and low-income prevalence on the macroinvertebrate communities and ecosystem processes within water-filled artificial tree holes. We hypothesized that these human demographic variables would affect tree holes in different ways via changes in temperature, water nutrients, and the local tree hole environment. We recruited community scientists across Greater Vancouver (Canada) to provide host trees and tend 50 tree holes over 14 weeks of colonization. We quantified tree hole ecosystems in terms of aquatic invertebrates, litter decomposition, and chlorophyll-a. We compiled potential explanatory variables from field measurements, satellite images, or census databases. Using structural equation models, we showed that invertebrate abundance was affected by low-income prevalence but not human population density. This was driven by cosmopolitan species of Ceratopogonidae (Diptera) with known associations to anthropogenic containers. Invertebrate diversity and abundance were also affected by environmental factors, such as temperature, elevation, water nutrients, litter quantity, and exposure. By contrast, invertebrate biomass, chlorophyll-a, and litter decomposition were not affected by any measured variables. In summary, this study shows that some urban ecosystems can be largely unaffected by human population density. Our study also demonstrates the potential of using artificial tree holes as a standardized, replicated habitat for studying urbanization. Finally, by combining community science and urban ecology, we were able to involve our local community in this pandemic research pivot. This abstract is quoted from the original article "Insects in the city: Determinants of a contained aquatic microecosystem across an urbanized landscape" in Ecology (2023) by DS Srivastava et al. Methods These methods are quoted in abbreviated form from the original article [please also see README.md file for details on each script and data file, including description of every variable]: We installed 73 artificial tree holes (hereafter tree holes) throughout Greater Vancouver, specifically the cities of Vancouver, Abbottsford, Burnaby, Chilliwack, Delta, Maple Ridge, New Westminster, North Vancouver, Port Moody, Richmond, Surrey, and West Vancouver. We constructed artificial tree holes from black plastic buckets (950 ml, height: 12.2cm, diameter:11.5cm). Near the rim, we drilled 1-cm holes for water overflow and covered these with 1mm mesh to prevent loss of insects and litter (Figure 2a). We attached each tree hole to a deciduous tree with a cable tie, about 1.3 m above ground, before adding leaf litter and bottled spring water. The leaf litter consisted of dried (60°C for two days) and pre-weighed Acer macrophyllum (Sapindaceae) leaves collected in November 2020, both loose (2.50 g) and in a 0.5 mm mesh leaf bag (0.200 g). We filled each tree hole with ~750 ml spring water (Western FamilyTM). Community scientists were instructed to monitor water level in the tree holes during the experiment, topping up tree holes when they became half-empty with extra bottles of water (same brand) that we provided. We also added an iButtonTM temperature logger (Maxim Integrated, San Jose, CA, USA; models DS1921G, DS1921Z, and DS1922L) wrapped in ParafilmTM (Beemis Company, Neenah, WI, USA) and programmed it to collect data every hour for 85 days. We added a small stick to assist ovipositing insects to perch or pupating insects to emerge. We installed all tree holes 21–28 March 2021. We visited all tree holes 17–30 May 2021, to collect data on water chemistry (pH, chlorophyll-a concentration), light availability (canopy cover), potential oviposition cues (host tree diameter, nearby standing water), and potential source populations (distance to water bodies). We measured water pH directly using a calibrated OaktonⓇ pH 450 pH meter. To estimate chlorophyll-a concentration, we extracted 25 mL of water, filtered it through a glass microfiber (0.7 μm) filter, and froze the filters. In the lab, we extracted chlorophyll-a on filters with 90%-acetone. We used a Trilogy Laboratory Fluorometer (Turner Designs, San Jose, CA, USA) to determine chlorophyll-a concentration following Wasmund et al. (2006). To measure canopy cover, we took a photograph directly up by placing a smartphone flat on the tree hole and then used ImageJTM to differentiate open sky from any obstructing cover. We searched within 30m of tree holes for sources of persistent standing water, such as buckets, birdbaths, and tires holding >400 mL water. We retrieved tree holes 1–10 July 2021, in the same order as installation, standardizing the experimental duration to 14 weeks (± 4 days). Once in the lab, we measured water pH as before, turbidity with a portable Turner AquaflorⓇ fluorometer, and froze a 5mL volume of water for later nutrient analysis. To analyse nutrients NO2-, NO3-, NH4+, and PO4-3, we loaded water samples onto 96-well plates with standards corresponding to the nutrient of interest. We then added the relevant reagents to all wells and compared the absorbance of the samples to standards using a SpectraMax M2e spectrophotometer (Molecular Devices, San Jose, CA, USA). We averaged two measurements per sample. As NO2-, NO3-, and NH4+ represent three steps of the dissolved inorganic nitrogen (DIN) cycle, we summed their concentrations in a single measure of DIN. We retrieved the remaining leaf fragments in litter bags with tweezers, washing biofilm from them before drying (two days at 60°C) and determining their combined mass. Decomposition was quantified as the percent dry mass lost. We also collected all loose debris in tree holes manually and by filtering through a pre-weighted Fisherbrand™ Fluted Qualitative Circled Filter Paper before drying and determining dry mass. We recorded the total volume of water present in each tree hole. Finally, we searched tree hole contents for macroscopic (>1mm) invertebrates in small aliquots in white trays. We sorted invertebrates into morphospecies, preserved them in 70% ethanol, and later identified them to family or genus level with identification keys. We used allometric equations to estimate dry body mass of invertebrates from body length (mm), either at the individual (species > 10 mm) or species level (hellometry R package, P. Rogy). We preserved a few voucher specimens in 95% ethanol for DNA barcoding to unambiguously assign species identities. For DNA extraction, we used QIAGEN® DNeasy Blood & Tissue Kit. We amplified the barcoding region of the mitochondrial Cytochrome Oxidase I (COI) gene with the universal primers LCO 1490 and HCO 2198 (Folmer et al. 1994). PCR products were sequenced by Psomagen, Inc. The chromatograms were assembled with Geneious Prime® v. 2022.2.2, and the resulting sequences were compared with GenBank and BOLD databases.

Diversity patterns are determined by biogeographic, energetic, and anthropogenic factors, yet few studies have combined them into a large-scale framework in order to decouple and compare their relative effects on fish faunas. Using an empirical dataset derived from 1527 underwater visual censuses (UVC) at 18 oceanic islands (five different marine provinces), we determined the relative influence of such factors on reef fish species richness, functional dispersion, density and biomass estimated from each UVC unit. Species richness presented low variation but was high at large island sites. High functional dispersion, density, and biomass were found at islands with large local species pool and distance from nearest reef. Primary productivity positively affected fish richness, density and biomass confirming that more productive areas support larger populations, and higher biomass and richness on oceanic islands. Islands densely populated by humans had lower fish species richness and biomass reflecting anthropogenic effects. Species richness, functional dispersion, and biomass were positively related to distance from the mainland. Overall, species richness and fish density were mainly influenced by biogeographical and energetic factors, whereas functional dispersion and biomass were strongly influenced by anthropogenic factors. Our results extend previous hypotheses for different assemblage metrics estimated from empirical data and confirm the negative impact of humans on fish assemblages, highlighting the need for conservation of oceanic islands.