A. SUMMARY This dataset maps 2020 census tracts to Analysis Neighborhoods.

The Department of Public Health and the Mayor’s Office of Housing and Community Development, with support from the Planning Department originally created the 41 Analysis Neighborhoods by grouping 2010 Census tracts, using common real estate and residents’ definitions for the purpose of providing consistency in the analysis and reporting of socio-economic, demographic, and environmental data, and data on City-funded programs and services. They are not codified in Planning Code nor Administrative Code.

B. HOW THE DATASET IS CREATED This dataset is produced by mapping the 2020 Census tracts to Analysis neighborhoods.

C. UPDATE PROCESS This dataset is static. Changes to the census tract boundaries are tracked in multiple datasets. See here for the 2010 census tracts assigned to neighborhoods

D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID, which is the primary key for census tracts in the dataset

E. RELATED DATASET 2020 census tract boundaries for San Francisco can be found here

This dataset tracks annual distribution of students across grade levels in San Francisco Unified School District and average distribution per school district in California

Annually, the San Francisco Health Service System (SFHSS) publishes a demographic report of its membership. SFHSS administers comprehensive health benefits for employees and retirees from the City and County of San Francisco (CCSF), San Francisco Unified School District (SFUSD), City College of San Francisco (SFCCD), and the Superior Court of San Francisco (CRT), as well as their eligible dependents.

Population: SF: Rostov Region: Volgodonsk: Non Working Age: Age 0 to 15 data was reported at 30.000 Person th in 2019. This records a decrease from the previous number of 30.400 Person th for 2018. Population: SF: Rostov Region: Volgodonsk: Non Working Age: Age 0 to 15 data is updated yearly, averaging 26.000 Person th from Dec 2003 (Median) to 2019, with 17 observations. The data reached an all-time high of 30.400 Person th in 2018 and a record low of 23.200 Person th in 2007. Population: SF: Rostov Region: Volgodonsk: Non Working Age: Age 0 to 15 data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA018: Population: by City: Southern Federal District.

Historical Dataset of San Francisco Unified School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,American Indian Student Percentage Comparison Over Years (1989-2009),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2009-2015),Two or More Races Student Percentage Comparison Over Years (2009-2023),Comparison of Students By Grade Trends

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

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).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of pe

Resident Population in Federal Reserve District 12: San Francisco was 70171.31800 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in Federal Reserve District 12: San Francisco reached a record high of 70171.31800 in January of 2024 and a record low of 30584.76900 in January of 1970. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in Federal Reserve District 12: San Francisco - last updated from the United States Federal Reserve on June of 2025.

Population: SF: Republic of Kalmykia: Elista data was reported at 103.100 Person th in 2019. This records an increase from the previous number of 102.600 Person th for 2018. Population: SF: Republic of Kalmykia: Elista data is updated yearly, averaging 103.400 Person th from Dec 1992 (Median) to 2019, with 28 observations. The data reached an all-time high of 111.900 Person th in 2000 and a record low of 94.400 Person th in 1992. Population: SF: Republic of Kalmykia: Elista data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA018: Population: by City: Southern Federal District.

A. SUMMARY The Department of Public Health and the Mayor’s Office of Housing and Community Development, with support from the Planning Department, created these 41 neighborhoods by grouping 2010 Census tracts, using common real estate and residents’ definitions for the purpose of providing consistency in the analysis and reporting of socio-economic, demographic, and environmental data, and data on City-funded programs and services. These neighborhoods are not codified in Planning Code nor Administrative Code, although this map is referenced in Planning Code Section 415 as the “American Community Survey Neighborhood Profile Boundaries Map. Note: These are NOT statistical boundaries as they are not controlled for population size. This is also NOT an official map of neighborhood boundaries in SF but an aggregation of Census tracts and should be used in conjunction with other spatial boundaries for decision making. B. HOW THE DATASET IS CREATED This dataset is produced by assigning Census tracts to neighborhoods based on existing neighborhood definitions used by Planning and MOHCD. A qualitative assessment is made to identify the appropriate neighborhood for a given tract based on understanding of population distribution and significant landmarks. Once all tracts have been assigned a neighborhood, the tracts are dissolved to produce this dataset, Analysis Neighborhoods. C. UPDATE PROCESS This dataset is static. Changes to the analysis neighborhood boundaries will be evaluated as needed by the Analysis Neighborhood working group led by DataSF and the Planning department and includes staff from various other city departments. Contact us for any questions. D. HOW TO USE THIS DATASET Downloading this dataset and opening it in Excel may cause some of the data values to be lost or not display properly (particularly the Analysis Neighborhood column). For a simple list of Analysis Neighborhoods without geographic coordinates, click here: https://data.sfgov.org/resource/xfcw-9evu.csv?$select=nhood E. RELATED DATASETS 2020 Census tracts assigned a neighborhood 2010 Census tracts assigned a neighborhood

This dataset tracks annual black student percentage from 1991 to 2023 for San Francisco Unified School District vs. California

A. SUMMARY Feature class includes geographic boundaries of neighborhoods in San Francisco identified as Muni Service Equity Strategy Neighborhoods. These are defined for the Muni Service Equity Policy adopted by the SFMTA Board of Directors in May 2014. For more information on this strategy refer to the latest FY Year 2016-2017 and 2017-2018 report: https://www.sfmta.com/sites/default/files/agendaitems/2016/4-5-16%20Item%2013%20Muni%20Equity%20Strategy%20Report_1.pdf

B. METHODOLOGY Boundaries were created through geospatial analysis of demographic data using census tracts and defined boundaries of city planning neighborhoods. The following criteria was used in selecting neighborhood concentrations of households with low income, concentrations of residents who identify with a race other than white, zero vehicle ownership households, and concentrations of affordable and public housing developments.

C. UPDATE FREQUENCY Geospatial analysis conducted in concurrence with bi-annual Muni Service Equity Strategy report

D. OTHER CRITICAL INFO Not all equity strategy neighborhood boundaries use their defined city planning neighborhood boundaries. Some city neighborhoods were merged or adjusted to include bordering census tracts

Population: SF: Volgograd City: Above Working Age data was reported at 266.800 Person th in 2019. This records a decrease from the previous number of 277.100 Person th for 2018. Population: SF: Volgograd City: Above Working Age data is updated yearly, averaging 255.100 Person th from Dec 2003 (Median) to 2019, with 17 observations. The data reached an all-time high of 277.100 Person th in 2018 and a record low of 227.100 Person th in 2003. Population: SF: Volgograd City: Above Working Age data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA018: Population: by City: Southern Federal District.

Population: SF: Volgograd Region: Volzhsky: Working Age data was reported at 187.700 Person th in 2019. This records an increase from the previous number of 184.400 Person th for 2018. Population: SF: Volgograd Region: Volzhsky: Working Age data is updated yearly, averaging 196.400 Person th from Dec 2003 (Median) to 2019, with 17 observations. The data reached an all-time high of 202.900 Person th in 2011 and a record low of 184.400 Person th in 2018. Population: SF: Volgograd Region: Volzhsky: Working Age data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA018: Population: by City: Southern Federal District. Labour Force population includes men aged 16-59 years old and women aged 16-54 years old Население трудоспособного возраста - мужчины 16-59 лет, женщины 16-54 года

Summary File 1 Data Profile 1 (SF1 Table DP-1) for Census Tracts in the Minneapolis-St. Paul 7 County metropolitan area is a subset of the profile of general demographic characteristics for 2000 prepared by the U.S. Census Bureau.

This table (DP-1) includes: Sex and Age, Race, Race alone or in combination with one or more otehr races, Hispanic or Latino and Race, Relationship, Household by Type, Housing Occupancy, Housing Tenure

US Census 2000 Demographic Profiles: 100-percent and Sample Data

The profile includes four tables (DP-1 thru DP-4) that provide various demographic, social, economic, and housing characteristics for the United States, states, counties, minor civil divisions in selected states, places, metropolitan areas, American Indian and Alaska Native areas, Hawaiian home lands and congressional districts (106th Congress). It includes 100-percent and sample data from Census 2000. The DP-1 table is available as part of the Summary File 1 (SF 1) dataset, and the other three tables are available as part of the Summary File 3 (SF 3) dataset.

The US Census provides DP-1 thru DP-4 data at the Census tract level through their DataFinder search engine. However, since the Metropolitan Council and MetroGIS participants are interested in all Census tracts within the seven county metropolitan area, it was quicker to take the raw Census SF-1 and SF-3 data at tract levels and recreate the DP1-4 variables using the appropriate formula for each DP variable. This file lists the formulas used to create the DP variables.

Population: SF: Volgograd City: Working Age data was reported at 578.300 Person th in 2019. This records an increase from the previous number of 571.900 Person th for 2018. Population: SF: Volgograd City: Working Age data is updated yearly, averaging 611.300 Person th from Dec 2003 (Median) to 2019, with 17 observations. The data reached an all-time high of 629.700 Person th in 2003 and a record low of 571.900 Person th in 2018. Population: SF: Volgograd City: Working Age data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA018: Population: by City: Southern Federal District. Labour Force population includes men aged 16-59 years old and women aged 16-54 years old Население трудоспособного возраста - мужчины 16-59 лет, женщины 16-54 года

Population: SF: Republic of Kalmykia: Elista: Non Working Age: Age 0 to 15 data was reported at 24.200 Person th in 2019. This records an increase from the previous number of 24.000 Person th for 2018. Population: SF: Republic of Kalmykia: Elista: Non Working Age: Age 0 to 15 data is updated yearly, averaging 22.100 Person th from Dec 2003 (Median) to 2019, with 17 observations. The data reached an all-time high of 24.200 Person th in 2019 and a record low of 20.300 Person th in 2008. Population: SF: Republic of Kalmykia: Elista: Non Working Age: Age 0 to 15 data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA018: Population: by City: Southern Federal District.

This dataset tracks annual hispanic student percentage from 1991 to 2023 for San Francisco Unified School District vs. California

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents. Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case 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 for a given date. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date). COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 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). This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date. Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative 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 spec

Summary File 3 Data Profile 3 (SF3 Table DP-3) for Minneapolis-St. Paul 7 County metropolitan area is a subset of the profile of selected economic characteristics for 2000 prepared by the U. S. Census Bureau.

This table (DP-3) includes: Employment Status, Commuting to Work, Occupation, Industry, Class of Worker, Income in 1999, Median earnings, Number Below Poverty Level, Poverty Status in 1999, For Whom Poverty Status is Determined

US Census 2000 Demographic Profiles: 100-percent and Sample Data

The profile includes four tables (DP-1 thru DP-4) that provide various demographic, social, economic, and housing characteristics for the United States, states, counties, minor civil divisions in selected states, places, metropolitan areas, American Indian and Alaska Native areas, Hawaiian home lands and congressional districts (106th Congress). It includes 100-percent and sample data from Census 2000. The DP-1 table is available as part of the Summary File 1 (SF 1) dataset, and the other three tables are available as part of the Summary File 3 (SF 3) dataset.

The US Census provides DP-1 thru DP-4 data at the Census tract level through their DataFinder search engine. However, since the Metropolitan Council and MetroGIS participants are interested in all Census tracts within the seven county metropolitan area, it was quicker to take the raw Census SF-1 and SF-3 data at tract levels and recreate the DP1-4 variables using the appropriate formula for each DP variable. This file lists the formulas used to create the DP variables.

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.