ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
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.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
As of July 19, 2015, the PD District boundaries have been updated through a redistricting process. These new boundaries are not reflected in the dataset yet so you cannot compare data from July 19, 2015 onward to official reports from PD with the Police District column. We are working on an update to the dataset to reflect the updated boundaries starting with data entered July 19 onward.
Incidents derived from SFPD Crime Incident Reporting system Updated daily, showing data from 1/1/2003 up until two weeks ago from current date. Please note: San Francisco police have implemented a new system for tracking crime. The dataset included here is still coming from the old system, which is in the process of being retired (a multi-year process). Data included here is no longer the official SFPD data. We will migrate to the new system for DataSF in the upcoming months.
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Graph and download economic data for Unemployed Persons in San Francisco-Oakland-Hayward, CA (MSA) (LAUMT064186000000004) from Jan 1990 to Dec 2024 about San Francisco, CA, household survey, unemployment, persons, and USA.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
A. SUMMARY This dataset contains population and demographic estimates and associated margins of error obtained and derived from the US Census. The data is presented over multiple years and geographies. The data is sourced primarily from the American Community Survey.
B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived.
C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org
D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of San Francisco by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for San Francisco. The dataset can be utilized to understand the population distribution of San Francisco by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in San Francisco. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for San Francisco.
Key observations
Largest age group (population): Male # 30-34 years (50,273) | Female # 30-34 years (44,861). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for San Francisco Population by Gender. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of San Francisco County by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of San Francisco County across both sexes and to determine which sex constitutes the majority.
Key observations
There is a slight majority of male population, with 51.4% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for San Francisco County Population by Race & Ethnicity. You can refer the same here
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Graph and download economic data for Employed Persons in San Francisco County/City, CA (LAUCN060750000000005) from Jan 1990 to Jan 2025 about San Francisco County/City, CA; household survey; employment; persons; and USA.
The South Market (SoMa) had an office vacancy rate of about 47 percent in the fourth quarter of 2024. This made it the district with the highest vacancy rate of office space in San Francisco. The lowest vacancy rate of about 4.8 percent was recorded in the Presidio district.
This statistic shows the takeout food delivery market share in San Francisco, United States, as of April 2021. In that year, DoorDash accounted for 74 percent of the food delivery market in San Francisco.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of San Francisco township by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of San Francisco township across both sexes and to determine which sex constitutes the majority.
Key observations
There is a majority of male population, with 54.26% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for San Francisco township Population by Race & Ethnicity. You can refer the same here
Statistics on speeding rates and exceedance of speed limit along selected street segments throughout San Francisco. The dataset was prepared as part of the SF Indicators project and covers from 2004 through 2009. http://www.sfindicatorproject.org/indicators/view/49
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
Search and browse hundreds of datasets from the City and County of San Francisco
Financial overview and grant giving statistics of San Francisco Bay Area Interactive Group
Financial overview and grant giving statistics of San Francisco Parents Coalition
In 2023, the GDP of the San Francisco Bay Area amounted to 681.89 billion U.S. dollars, an increase from the previous year. The overall quarterly GDP growth in the United States can be found here. The GDP of the San Francisco Bay Area The San Francisco Bay Area, commonly known as the Bay Area, is a metropolitan region that surrounds the San Francisco and San Pablo estuaries in Northern California. The region encompasses metropolitan areas such as San Francisco-Oakland (12th largest in the country), San Jose (31st largest in the country), along with smaller urban and rural areas. Overall, the Bay Area consists of nine counties, 101 cities, and 7,000 square miles. The nine counties are Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma. There are approximately 4.62 million people living in the metro area as of 2022. Silicon Valley In the ten year period between 2001 and 2011, the Bay Area saw steady GDP growth. Starting in 2012, it began to skyrocket. This is thanks to an economic boom in the tech sector, and high value companies headquartered in Silicon Valley - also part of the Bay Area. Silicon Valley is known as the center of the global technology industry. Companies like Google, Facebook, eBay and Apple are headquartered there. Additionally, California ranked first on a list of U.S. states by GDP, with more than 3.59 trillion U.S. dollars in GDP in 2022.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
5 to 17 years Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in San Francisco, California by age, education, race, gender, work experience and more.
A. SUMMARY This dataset is used to report on public dataset access and usage within the open data portal. Each row sums the amount of users who access a dataset each day, grouped by access type (API Read, Download, Page View, etc).
B. HOW THE DATASET IS CREATED This dataset is created by joining two internal analytics datasets generated by the SF Open Data Portal. We remove non-public information during the process.
C. UPDATE PROCESS This dataset is scheduled to update every 7 days via ETL.
D. HOW TO USE THIS DATASET This dataset can help you identify stale datasets, highlight the most popular datasets and calculate other metrics around the performance and usage in the open data portal.
Please note a special call-out for two fields: - "derived": This field shows if an asset is an original source (derived = "False") or if it is made from another asset though filtering (derived = "True"). Essentially, if it is derived from another source or not. - "provenance": This field shows if an asset is "official" (created by someone in the city of San Francisco) or "community" (created by a member of the community, not official). All community assets are derived as members of the community cannot add data to the open data portal.
Financial overview and grant giving statistics of San Francisco Chamber Of Commerce
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Graph and download economic data for All Employees: Government: Local Government in San Francisco-San Mateo-Redwood City, CA (MD) (SMU06418849093000001) from Jan 1990 to Jan 2025 about local govt, San Francisco, CA, government, employment, and USA.
Large multifamily buildings (20+ units) comprised the largest share of new housing stock in San Francisco in 2023. During the same period, only 19 single-family homes were added, compared to 2,231 units in buildings with 20 units or more.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
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.