For up to date data starting in 2018, please go to the new dataset at: https://data.sfgov.org/d/wg3w-h783

As of May 2018, the feed from the legacy mainframe CABLE was discontinued. It was extremely prone to issues and caused many delays in data accessibility. The new dataset linked above comes from the Crime Data Warehouse, a more reliable data system maintained by the Police Department.

This data will undergo a minor update to conform more closely to the schema of the new dataset. We will post a change notice when that work is planned. This change will not include adding new fields or backfilling data. It is provided as is. We are keeping data from the two systems separate to make it transparent to data users that there were fundamental changes.

A. SUMMARY These data represent hate crimes reported by the SFPD to the California Department of Justice. Read the detailed overview of this dataset here. What is a Hate Crime? A hate crime is a crime against a person, group, or property motivated by the victim's real or perceived protected social group. An individual may be the victim of a hate crime if they have been targeted because of their actual or perceived: (1) disability, (2) gender, (3) nationality, (4) race or ethnicity, (5) religion, (6) sexual orientation, and/or (7) association with a person or group with one or more of these actual or perceived characteristics. Hate crimes are serious crimes that may result in imprisonment or jail time. B. HOW THE DATASET IS CREATED How is a Hate Crime Processed? Not all prejudice incidents including the utterance of hate speech rise to the level of a hate crime. The U.S. Constitution allows hate speech if it does not interfere with the civil rights of others. While these acts are certainly hurtful, they do not rise to the level of criminal violations and thus may not be prosecuted. When a prejudice incident is reported, the reporting officer conducts a preliminary investigation and writes a crime or incident report. Bigotry must be the central motivation for an incident to be determined to be a hate crime. In that report, all facts such as verbatims or statements that occurred before or after the incident and characteristics such as the race, ethnicity, sex, religion, or sexual orientations of the victim and suspect (if known) are included. To classify a prejudice incident, the San Francisco Police Department’s Hate Crimes Unit of the Special Investigations Division conducts an analysis of the incident report to determine if the incident falls under the definition of a “hate crime” as defined by state law. California Penal Code 422.55 - Hate Crime Definition C. UPDATE PROCESS These data are updated monthly. D. HOW TO USE THIS DATASET This dataset includes the following information about each incident: the hate crime offense, bias type, location/time, and the number of hate crime victims and suspects. The data presented mirrors data published by the California Department of Justice, albeit at a higher frequency. The publishing of these data meet requirements set forth in PC 13023. E. RELATED DATASETS California Department of Justice - Hate Crimes Info California Department of Justice - Hate Crimes Data

San Francisco Police Department Crime Reporting Plots. These have historically been used for reporting various stats. Derived from shapefile sent by SFPD in May 2003.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in San Mateo County, CA (DISCONTINUED) (FBITC006081) from 2004 to 2020 about San Mateo County, CA; crime; violent crime; property crime; San Francisco; CA; and USA.

Violent and property crime rates per 100,000 population for San Mateo County and the State of California. The total crimes used to calculate the rates for San Mateo County include data from: Sheriff's Department Unincorporated, Atherton, Belmont, Brisbane, Broadmoor, Burlingame, Colma, Daly City, East Palo Alto, Foster City, Half Moon Bay, Hillsborough, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Bay Area DPR, BART, Union Pacific Railroad, and CA Highway Patrol.

Context

DataSF seeks to transform the way that the City of San Francisco works -- through the use of data.

https://datasf.org/about/

Content

This dataset contains the following tables: ['311_service_requests', 'bikeshare_stations', 'bikeshare_status', 'bikeshare_trips', 'film_locations', 'sffd_service_calls', 'sfpd_incidents', 'street_trees']

• This data includes all San Francisco 311 service requests from July 2008 to the present, and is updated daily. 311 is a non-emergency number that provides access to non-emergency municipal services.
• This data includes fire unit responses to calls from April 2000 to present and is updated daily. Data contains the call number, incident number, address, unit identifier, call type, and disposition. Relevant time intervals are also included. Because this dataset is based on responses, and most calls involved multiple fire units, there are multiple records for each call number. Addresses are associated with a block number, intersection or call box.
• This data includes incidents from the San Francisco Police Department (SFPD) Crime Incident Reporting system, from January 2003 until the present (2 weeks ago from current date). The dataset is updated daily. Please note: the SFPD has implemented a new system for tracking crime. This dataset is still sourced from the old system, which is in the process of being retired (a multi-year process).
• This data includes a list of San Francisco Department of Public Works maintained street trees including: planting date, species, and location. Data includes 1955 to present.

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

http://datasf.org/

• https://cloud.google.com/bigquery/public-data/sfo-311
• https://cloud.google.com/bigquery/public-data/sffd-service-calls
• https://cloud.google.com/bigquery/public-data/sfpd-reports
• https://cloud.google.com/bigquery/public-data/sfo-trees

Dataset Source: SF OpenData. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://sfgov.org/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @meric from Unplash.

Inspiration

Which neighborhoods have the highest proportion of offensive graffiti?

Which complaint is most likely to be made using Twitter and in which neighborhood?

What are the most complained about Muni stops in San Francisco?

What are the top 10 incident types that the San Francisco Fire Department responds to?

How many medical incidents and structure fires are there in each neighborhood?

What’s the average response time for each type of dispatched vehicle?

Which category of police incidents have historically been the most common in San Francisco?

What were the most common police incidents in the category of LARCENY/THEFT in 2016?

Which non-criminal incidents saw the biggest reporting change from 2015 to 2016?

What is the average tree diameter?

What is the highest number of a particular species of tree planted in a single year?

Which San Francisco locations feature the largest number of trees?

There has been little research on United States homicide rates from a long-term perspective, primarily because there has been no consistent data series on a particular place preceding the Uniform Crime Reports (UCR), which began its first full year in 1931. To fill this research gap, this project created a data series on homicides per capita for New York City that spans two centuries. The goal was to create a site-specific, individual-based data series that could be used to examine major social shifts related to homicide, such as mass immigration, urban growth, war, demographic changes, and changes in laws. Data were also gathered on various other sites, particularly in England, to allow for comparisons on important issues, such as the post-World War II wave of violence. The basic approach to the data collection was to obtain the best possible estimate of annual counts and the most complete information on individual homicides. The annual count data (Parts 1 and 3) were derived from multiple sources, including the Federal Bureau of Investigation's Uniform Crime Reports and Supplementary Homicide Reports, as well as other official counts from the New York City Police Department and the City Inspector in the early 19th century. The data include a combined count of murder and manslaughter because charge bargaining often blurs this legal distinction. The individual-level data (Part 2) were drawn from coroners' indictments held by the New York City Municipal Archives, and from daily newspapers. Duplication was avoided by keeping a record for each victim. The estimation technique known as "capture-recapture" was used to estimate homicides not listed in either source. Part 1 variables include counts of New York City homicides, arrests, and convictions, as well as the homicide rate, race or ethnicity and gender of victims, type of weapon used, and source of data. Part 2 includes the date of the murder, the age, sex, and race of the offender and victim, and whether the case led to an arrest, trial, conviction, execution, or pardon. Part 3 contains annual homicide counts and rates for various comparison sites including Liverpool, London, Kent, Canada, Baltimore, Los Angeles, Seattle, and San Francisco.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Contra Costa County, CA (DISCONTINUED) (FBITC006013) from 2004 to 2020 about Contra Costa County, CA; crime; violent crime; property crime; San Francisco; CA; and USA.

This survey was conducted by the Center for Urban Affairs and Policy Research at Northwestern University to gather information for two projects that analyzed the impact of crime on the lives of city dwellers. These projects were the Reactions to Crime (RTC) Project, which was supported by the United States Department of Justice's National Institute of Justice as part of its Research Agreements Program, and the Rape Project, supported by the National Center for the Prevention and Control of Rape, a subdivision of the National Institute of Mental Health. Both investigations were concerned with individual behavior and collective reactions to crime. The Rape Project was specifically concerned with sexual assault and its consequences for the lives of women. The three cities selected for study were Chicago, Philadelphia, and San Francisco. A total of ten neighborhoods were chosen from these cities along a number of dimensions -- ethnicity, class, crime, and levels of organizational activity. In addition, a small city-wide sample was drawn from each city. Reactions to crime topics covered how individuals band together to deal with crime problems, individual responses to crime such as property marking or the installation of locks and bars, and the impact of fear of crime on day-to-day behavior -- for example, shopping and recreational patterns. Respondents were asked several questions that called for self-reports of behavior, including events and conditions in their home areas, their relationship to their neighbors, who they knew and visited around their homes, and what they watched on TV and read in the newspapers. Also included were a number of questions measuring respondents' perceptions of the extent of crime in their communities, whether they knew someone who had been a victim, and what they had done to reduce their own chances of being victimized. Questions on sexual assault/rape included whether the respondent thought this was a neighborhood problem, if the number of rapes in the neighborhood were increasing or decreasing, how many women they thought had been sexually assaulted or raped in the neighborhood in the previous year, and how they felt about various rape prevention measures, such as increasing home security, women not going out alone at night, women dressing more modestly, learning self-defense techniques, carrying weapons, increasing men's respect of women, and newspapers publishing the names of known rapists. Female respondents were asked whether they thought it likely that they would be sexually assaulted in the next year, how much they feared sexual assault when going out alone after dark in the neighborhood, whether they knew a sexual assault victim, whether they had reported any sexual assaults to police, and where and when sexual assaults took place that they were aware of. Demographic information collected on respondents includes age, race, ethnicity, education, occupation, income, and whether the respondent owned or rented their home.

Reference data provided by the Crime Analysis Unit at the Police Department referenced in the Police Department Incident Reports: 2018 to present (https://data.sfgov.org/d/wg3w-h783)

Transmitted by the department on May 2, 2018.

This data includes incidents from the San Francisco Police Department (SFPD) Crime Incident Reporting system, from January 2003 until the present (2 weeks ago from current date). The dataset is updated daily. Please note: the SFPD has implemented a new system for tracking crime. This dataset is still sourced from the old system, which is in the process of being retired (a multi-year process). This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

description: [Update 04/16/2018]: We are still developing the automation for the new dataset. We do not have an updated publishing date at the moment. We have a target schema and have provided a crosswalk document attached to the existing dataset in advance of the changes. We will update this document if there's new information to share. [Change Notice 03/13/2018]: By the end of this month, this dataset will become historical and a new one will be created starting with incident data in 2018. This one will remain here, but no longer be updated. The new one will have data coming from a new system, will not have a 2 week lag, and have updated districts among other quality improvements. We will attach a guide here with more detailed change updates as soon as we have them. +++++++++ 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.; abstract: [Update 04/16/2018]: We are still developing the automation for the new dataset. We do not have an updated publishing date at the moment. We have a target schema and have provided a crosswalk document attached to the existing dataset in advance of the changes. We will update this document if there's new information to share. [Change Notice 03/13/2018]: By the end of this month, this dataset will become historical and a new one will be created starting with incident data in 2018. This one will remain here, but no longer be updated. The new one will have data coming from a new system, will not have a 2 week lag, and have updated districts among other quality improvements. We will attach a guide here with more detailed change updates as soon as we have them. +++++++++ 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.

A. SUMMARY Please note that the "Data Last Updated" date on this page denotes the most recent DataSF update and does not reflect the most recent update to this dataset. To confirm the completeness of this dataset please contact the District Attorney's office at districtattorney@sfgov.org. This dataset includes information on every arrest that has been presented to the SFDA since 2011 and the “action” decision made by the office on each, based on data from the SFDA’s internal case management system. After law enforcement has made an arrest for suspected criminal activity, the arresting agency presents its evidence to the District Attorney’s Office to determine what, if any, charges can be proven beyond a reasonable doubt. This prosecutorial decision to file or to reject arrest charges represents the first, and one of the most important, decisions of the District Attorney’s Office. In addition to either filing new charges or declining to file charges (“discharging” the arrest), the prosecutor may also take other actions, such as initiating a motion to revoke probation or parole, referring the case back to the arresting agency for further investigation, or referring the case to another criminal justice agency. More information about this dataset can be found under the “District Attorney Actions on Arrests Presented” section on the Data Dashboards page Disclaimer: The San Francisco District Attorney's Office does not guarantee the accuracy, completeness, or timeliness of the information as the data is subject to change as modifications and updates are completed. B. HOW THE DATASET IS CREATED When an arrest is presented to the District Attorney’s office, relevant data is manually entered into the District Attorney Office's case management system. Data reports are pulled from this system on a semi-regular basis, cleaned, anonymized, and added to Open Data. C. UPDATE PROCESS We strive to update this dataset at the beginning of every week. However, the creation of this dataset requires a manual pull from the Office's case management system and is dependent on staff availability. D. HOW TO USE THIS DATASET Please review the “District Attorney Actions on Arrests Presented” section on the Data Dashboards page for more information about this dataset. E. RELATED DATASETS District Attorney Cases Prosecuted District Attorney Case Resolutions

Revision Note (February 10, 2025): The data pipeline for "Law Enforcement Dispatched Calls for Service: Real-Time" has been updated. A spelling error with the column "priority_original" has been corrected. Column data types and formatting have been updated to reflect DataSF standards. Law Enforcement Dispatched Calls for Service: Closed Calls has also been updated as part of a larger effort to upgrade our Calls for Service pipelines. Email support@datasf.org with any questions or concerns.

A. SUMMARY Law Enforcement Dispatched Calls for Service: Real-Time includes all calls for service that generate a record in the Department of Emergency Management's Computer Aided Dispatch (CAD) System and result in a law enforcement unit being dispatched to a location. Calls originate either from the public (via calls to the 911 call center) or from law enforcement officers in the field upon viewing an incident (‘On-View’).

This dataset represents a rolling 48 hour window of calls for service. It contains both open and closed calls. It is both updated every 10 minutes and delayed by an additional 10 minutes. Open calls are defined as active calls that are unverified, but being worked by law enforcement. Closed calls are calls that law enforcement have indicated are resolved. Not all calls for service generate a police incident report, so data does not directly correlate to the Police Incident Reports dataset. The Real-time Calls dataset contains calls handled by law enforcement which can include Police, MTA parking enforcement, the Sheriff’s Office, and others. Some fields in the calls for service data are redacted due to the sensitive nature of the call and/or privacy concerns related to the incident.

Please refer Law Enforcement Dispatched Calls for Service Explainer for full documentation.

B. HOW THE DATASET IS CREATED
Once a received 911 call is dispatched, a record (CAD#) is generated in the Department of Emergency Management's Computer Aided Dispatch (CAD) System.

C. UPDATE PROCESS Updated every 10 minutes with the past 48hrs of open and closed calls that have been dispatched.

D. HOW TO USE THIS DATASET Please refer Law Enforcement Dispatched Calls for Service Explainer for full documentation.

Note: To find data for calls originating from the 311 Connected Worker app dispatched from the Healthy Streets Operations Center (HSOC), search for the value “HSOC” in the onview_flag column.

E. KEY RELATED DATASETS Datasets: Law Enforcement Dispatched Calls for Service: Closed Calls Police Department Incident Reports: 2018 to Present Fire Department Calls for Service

Geographic Boundaries: Current Police Districts
Analysis Neighborhoods Supervisor Districts

Abstract (en): These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study evaluates the impacts of re-entry programs developed by seven grantees awarded funds under the Second Chance Act (SCA) Adult Demonstration Program to reduce recidivism by addressing the challenges faced by adults returning to their communities after incarceration. The collection contains 3 SAS data files: admin30.sas(n=966; 111 variables), MIS.sas(n=606; 48 variables), and survey.sas(n=789; 273 variables) and 1 SAS syntax file. This evaluation estimates the impacts of programs developed by seven agencies that were awarded grants through the first round of funding under the SCA Adult Demonstration Program; these grants were awarded in fiscal year(FY) 2009. The Adult Demonstration Program represents one of a number of separate grant programs authorized through SCA. The seven grantees were purposively selected and drawn from only one grant program. In estimating impacts, the evaluation used a randomized controlled trial, whereby 966 individuals eligible for SCA were randomly assigned to either a program group, whose members could participate in individualized SCA services, or a control group, whose members could receive all re-entry services otherwise available but not individualized SCA services. Each study participant was measured on a range of outcomes at 18 months after random assignment and again approximately one year later. The grantees selected by BJA for the study include: State Agencies 1. Kentucky Department of Corrections 2. Oklahoma Department of Correction 3. South Dakota Department of Corrections Local Agencies 4. Allegheny County (PA) Department of Human Services 5. Marion County (OR) Sheriff's Office 6. San Francisco (CA) Department of Public Health 7. San Mateo County (CA) Division of Health and Recovery Services The outcomes at 18 months, measured through a survey of study participants and from administrative data, included services received, recidivism (re-arrest, reconviction, and re-incarceration), employment and earnings, housing stability, and self-reported health, among others. The outcomes measured one year later were drawn solely from administrative data and included recidivism and employment and earnings. Crime related variables include the number and nature of convictions and time spent incarcerated. Other demographic variables include gender, age, race, ethnicity, education, income, marital status, and number of children. Presence of Common Scales: Several likert-type scales were used. Response Rates: 82 percent (18 Month Follow-up Survey) Datasets:DS1: Dataset Adults who have been imprisoned in a state, local, or tribal prison who were convicted as an adult and are classified as being at medium or high risk of recidivism. Smallest Geographic Unit: none Those determined eligible for SCA were randomly assigned to either a program group or a control group. The study allowed each grantee to establish its own criteria for determining who was eligible for SCA. All those eligible were at medium or high risk or recidivism. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (2010-RY-BX-0003). record abstracts computer-assisted personal interview (CAPI) computer-assisted telephone interview (CATI)

This dataset shows the type of transportation people use to go to work. The information is mapped according to place of residence. The data is part of the Census Transportation Planning Package (CTPP), and is the result of a cooperative effort between various groups including the State Departments of Transportation, U.S. Census Bureau, and the Federal Highway Administration. The data is a special tabulation of responses from households completing the decennial census long form. The data was collected in 2000 and is shown at tract level.