Chart and table of population level and growth rate for the San Francisco metro area from 1950 to 2025.

In 2022, the total population of the Guangdong - Hong Kong - Macao Greater Bay Area reached around 86.6 million. In terms of population, China's Greater Bay Area was larger than other major Bay Areas in the world. However, per capita GDP was only about half of that in the Tokyo Bay Area and only one seventh of that in the San Francisco Bay Area.

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

INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

A. SUMMARY 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 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

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 3/2/2020 when testing began.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED 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 2016-2020 American Community Survey (ACS) population estimates 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.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Privacy rules in effect 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

Rate suppression in effect where counts lower than 20 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.

A note on Census ZIP Code Tabulation Areas (ZCTAs) 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. Read how the Census develops ZCTAs on their website.

Row included for Citywide case counts, incidence rate, and deaths 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 basis.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases and deaths summarized by geography are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case and death data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 2/23/2022 - the New Cases Map dashboard began pulling from this dataset. To access Cases by Geography Over Time, please refer to this dataset.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update.

A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases.

1. All cases: Includes cases among all San Francisco residents regardless of vaccination status.

2. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated.

3. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.”

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available.

B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address.

We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included.

C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day.

D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”.

Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time.

The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category.

New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days.

New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a particular day with the prior six days. Percent of total new cases shows the percent of all cases on each day that were among a particular vaccination status.

Here is more information on how each case rate is calculated:

1. The case rate for all cases is equal to the number of new cases among all residents divided by the estimated total resident population.

2. Unvaccinated case rates are equal to the number of new cases among unvaccinated residents divided by the estimated number of unvaccinated residents. The estimated number of unvaccinated residents is calculated by subtracting the number of residents that have received at least one dose of a vaccine from the total estimated resident population.

3. Completed primary series case rates are equal to the number of new cases among completed primary series residents divided by the estimated number of completed primary series residents. The estimated number of completed primary series residents is calculated by taking the number of residents who have completed their primary series over time and adding a 14-day delay to the “date_administered” column, to align with the definition of “Completed primary series cases” above.

E. CHANGE LOG

• 6/28/2023 - data on cases by vaccination status are no longer being updated. This data is currently through 6/20/2023 (as of 6/28/2023) and will not include any new data after this date.
  
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “sf_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 7/15/2022 - references to “fully vaccinated” replaced with “completed primary series” in column “vaccination_status".
• 7/15/2022 - rows with “partially vaccinated” in column “vaccination_status” removed from dataset.

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

About this dataset

Context

The dataset presents the median household income across different racial categories in San Francisco township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of San Francisco township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 95.31% of the total residents in San Francisco township. Notably, the median household income for White households is $133,763. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $133,763.

https://i.neilsberg.com/ch/san-francisco-township-mn-median-household-income-by-race.jpeg" alt="San Francisco township median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in San Francisco township.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for San Francisco township median household income by race. You can refer the same here

Daily Miles Traveled (T14)

FULL MEASURE NAME
Total vehicle miles traveled

LAST UPDATED
August 2022

DESCRIPTION
Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE
California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php
2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm
2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2001-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2020

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

Daily Miles Traveled (T14)

FULL MEASURE NAME
Total vehicle miles traveled

LAST UPDATED
August 2022

DESCRIPTION
Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE
California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php
2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm
2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2001-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2020

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

Daily Miles Traveled (T14)

FULL MEASURE NAME
Total vehicle miles traveled

LAST UPDATED
August 2022

DESCRIPTION
Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE
California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php
2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm
2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2001-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2020

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

VITAL SIGNS INDICATOR
Transit Ridership (T11)

FULL MEASURE NAME
Daily transit boardings

LAST UPDATED
February 2023

DESCRIPTION
Transit ridership refers to the number of passenger boardings on public transportation, which includes buses, rail systems and ferries. The dataset includes metropolitan area, regional, mode and operator tables for total typical weekday boardings.

DATA SOURCE
Federal Transit Administration: National Transit Database - http://www.ntdprogram.gov/ntdprogram/data.htm
1991-2022

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1991-2022

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1991-2022

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The National Transit Database (NTD) dataset was lightly cleaned to correct for erroneous zero values - in which null values (unsubmitted data) were incorrectly marked as zeroes. Paratransit data is sparse in early years of the NTD dataset, meaning that transit ridership estimates in the early 1990s are likely underestimated. Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD.

2022 data should be considered preliminary, as it comes from the monthly data tables rather than the longer-term time series dataset. Weekday ridership is calculated by taking the total annual ridership and dividing by 300, an assumption which is consistent with MTC travel modeling procedures; it was also compared to observed weekday boarding data (which is more limited in availability) to ensure consistency on the regional level. Per-capita transit ridership is calculated for the operator's general service area or taxation district; for example, BART includes the three core counties (San Francisco, Alameda, and Contra Costa), as well as northern San Mateo County post-SFO extension, and AC Transit includes the cities located within its service area. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data.

VITAL SIGNS INDICATOR
Transit Ridership (T11)

FULL MEASURE NAME
Daily transit boardings

LAST UPDATED
February 2023

DESCRIPTION
Transit ridership refers to the number of passenger boardings on public transportation, which includes buses, rail systems and ferries. The dataset includes metropolitan area, regional, mode and operator tables for total typical weekday boardings.

DATA SOURCE
Federal Transit Administration: National Transit Database - http://www.ntdprogram.gov/ntdprogram/data.htm
1991-2022

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1991-2022

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1991-2022

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The National Transit Database (NTD) dataset was lightly cleaned to correct for erroneous zero values - in which null values (unsubmitted data) were incorrectly marked as zeroes. Paratransit data is sparse in early years of the NTD dataset, meaning that transit ridership estimates in the early 1990s are likely underestimated. Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD.

2022 data should be considered preliminary, as it comes from the monthly data tables rather than the longer-term time series dataset. Weekday ridership is calculated by taking the total annual ridership and dividing by 300, an assumption which is consistent with MTC travel modeling procedures; it was also compared to observed weekday boarding data (which is more limited in availability) to ensure consistency on the regional level. Per-capita transit ridership is calculated for the operator's general service area or taxation district; for example, BART includes the three core counties (San Francisco, Alameda, and Contra Costa), as well as northern San Mateo County post-SFO extension, and AC Transit includes the cities located within its service area. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data.

In 2017, the California Tax Credit Allocation Committee (CTCAC) and the Department of Housing and Community Development (HCD) created the California Fair Housing Task Force (Task Force). The Task Force was asked to assist CTCAC and HCD in creating evidence-based approaches to increasing access to opportunity for families with children living in housing subsidized by the Low-Income Housing Tax Credit (LIHTC) program.

This feature set contains Resource Opportunity Areas (ROAs) that are the results of the Task Force's analysis for the two regions used for the San Francisco Bay Region; one is for the cities and towns (urban) and the other is for the rural areas. The reason for treating urban and rural areas as separate reasons is that using absolute thresholds for place-based opportunity could introduce comparisons between very different areas of the total region that make little sense from a policy perspective — in effect, holding a farming community to the same standard as a dense, urbanized neighborhood.

ROA analysis for urban areas is based on census tract data. Since tracts in rural areas of are approximately 37 times larger in land area than tracts in non-rural areas, tract-level data in rural areas may mask over variation in opportunity and resources within these tracts. Assessing opportunity at the census block group level in rural areas reduces this difference by 90 percent (each rural tract contains three block groups), and thus allows for finer-grained analysis.

In addition, more consistent standards can be useful for identifying areas of concern from a fair housing perspective — such as high-poverty and racially segregated areas. Assessing these factors based on intraregional comparison could mischaracterize areas in more affluent areas with relatively even and equitable development opportunity patterns as high-poverty, and could generate misleading results in areas with higher shares of objectively poor neighborhoods by holding them to a lower, intraregional standard.

To avoid either outcome, the Task Force used a hybrid approach for the CTCAC/HCD ROA analysis — accounting for regional differences in assessing opportunity for most places, while applying more rigid standards for high-poverty, racially segregated areas in all regions. In particular:

Filtering for High-Poverty, Racially Segregated Areas The CTCAC/HCD ROA filters areas that meet consistent standards for both poverty (30% of the population below the federal poverty line) and racial segregation (over-representation of people of color relative to the county) into a “High Segregation & Poverty” category. The share of each region that falls into the High Segregation & Poverty category varies from region to region.

Calculating Index Scores for Non-Filtered Areas The CTCAC/HCD ROAs process calculates regionally derived opportunity index scores for non-filtered tracts and rural block groups using twenty-one indicators (see Data Quality section of metadata for more information). These index scores make it possible to sort each non-filtered tract or rural block group into opportunity categories according to their rank within the urban or rural areas.

To allow CTCAC and HCD to incentivize equitable development patterns in each region to the same degree, the CTCAC/HCD analysis 20 percent of tracts or rural block groups in each urban or rural area, respectively, with the highest relative index scores to the "Highest Resource” designation and the next 20 percent to the “High Resource” designation.

The region's urban area thus ends up with 40 percent of its total tracts with reliable data as Highest or High Resource (or 40 percent of block groups in the rural area). The remaining non-filtered tracts or rural block groups are then evenly divided into “Low Resource” and “Moderate Resource” categories.

Excluding Tracts or Block Groups The analysis also excludes certain census areas from being categorized. To improve the accuracy of the mapping, tracts and rural block groups with the following characteristics are excluded from the application of the filter and from categorization based on index scores: ● Areas with unreliable data, as defined later in this document; ● Areas where prisoners make up at least 75 percent of the population; ● Areas with population density below 15 people per square mile and total population below 500; and ● Areas where at least half of the age 16+ population is employed by the armed forces, in order to exclude military base areas where it is not possible to develop non-military affordable housing.

Excluded tracts and rural block groups are identified as “nan” in the attribute table.

The full methodology used by the Task Force can be found in the California Fair Housing Task Force Opportunity Mapping Methodology report (https://www.treasurer.ca.gov/ctcac/opportunity/2022/2022-hcd-methodology.pdf) on the California Office of State Treasurer website.

Source data and maps can be found on the CTCAC/HCD Opportunity Area Maps page (https://www.treasurer.ca.gov/ctcac/opportunity.asp).