SANDAG provides an annual report on crime in the San Diego region. This dataset contains data from the 2009 through 2022 editions of the report. Data for 2023 is converted from California Incident Based Reporting System (CIBRS) data provided by SANDAG. Additional data comes from Arjis and DOJ OpenJustice. Some data for previous years reports is updated with new editions. "San Diego County" includes all cities and unincorporated areas in San Diego County. "Sheriff - Total" includes the contract cities and the unincorporated area served by the San Diego County Sheriff's Department. California and United States data come from the FBI's Annual Crime Reports.

In 2021, the San Diego Police Department began reporting to the FBI’s National Incident-Based Reporting System (NIBRS) for statistics. NIBRS collects data on all crimes reported to law enforcement agencies, not just the highest charges within those crimes. Crime categories in NIBRS include crimes against Persons (i.e., assault, homicide, etc.), Property (i.e., burglary, theft, robbery, etc.), and Society (i.e., alcohol and drug offenses, weapon offenses, gambling, etc.). These new categories are a shift from the older FBI reporting style (UCR Summary) of simply reporting violent and property crime counts. For the most part, SDPD’s crime reporting will follow the NIBRS categories now. Historical data will still be posted to the Annual Crime Reports website. This dataset comprises of Crime Offense Data extracted from the San Diego Police Department’s Records Management System and includes information such as the date/time of the reported offense, offense category, underlying code sections, and the hundred block address where the incident occurred. This data is dynamic as records are updated daily and may change or be deleted as police investigations proceed. Records range from January 2020 – Present. The SDPD Neighborhood Crime Summary Dashboard visualizes this dataset. For more information on NIBRS reporting guidelines please refer to the FBI’s 2021.1 National Incident-Based Reporting System User Manual. For national NIBRS data, you can visit the FBI’s Crime Data Explorer.

Financial overview and grant giving statistics of San Diego Crime Commission

The Criminal Justice Research Division of the San Diego Association of Governments (SANDAG) received funds from the National Institute of Justice to assist the Regional Auto Theft Task (RATT) force and evaluate the effectiveness of the program. The project involved the development of a computer system to enhance the crime analysis and mapping capabilities of RATT. Following the implementation of the new technology, the effectiveness of task force efforts was evaluated. The primary goal of the research project was to examine the effectiveness of RATT in reducing auto thefts relative to the traditional law enforcement response. In addition, the use of enhanced crime analysis information for targeting RATT investigations was assessed. This project addressed the following research questions: (1) What were the characteristics of vehicle theft rings in San Diego and how were the stolen vehicles and/or parts used, transported, and distributed? (2) What types of vehicles were targeted by vehicle theft rings and what was the modus operandi of suspects? (3) What was the extent of violence involved in motor vehicle theft incidents? (4) What was the relationship between the locations of vehicle thefts and recoveries? (5) How did investigators identify motor vehicle thefts that warranted investigation by the task force? (6) Were the characteristics of motor vehicle theft cases investigated through RATT different than other cases reported throughout the county? (7) What investigative techniques were effective in apprehending and prosecuting suspects involved in major vehicle theft operations? (8) What was the impact of enhanced crime analysis information on targeting decisions? and (9) How could public education be used to reduce the risk of motor vehicle theft? For Part 1 (Auto Theft Tracking Data), data were collected from administrative records to track auto theft cases in San Diego County. The data were used to identify targets of enforcement efforts (e.g., auto theft rings, career auto thieves), techniques or strategies used, the length of investigations, involvement of outside agencies, property recovered, condition of recoveries, and consequences to offenders that resulted from the activities of the investigations. Data were compiled for all 194 cases investigated by RATT in fiscal year 1993 to 1994 (the experimental group) and compared to a random sample of 823 cases investigated through the traditional law enforcement response during the same time period (the comparison group). The research staff also conducted interviews with task force management (Parts 2 and 3, Investigative Operations Committee Initial Interview Data and Investigative Operations Committee Follow-Up Interview Data) and other task force members (Parts 4 and 5, Staff Initial Interview Data and Staff Follow-Up Interview Data) at two time periods to address the following issues: (1) task force goals, (2) targets, (3) methods of identifying targets, (4) differences between RATT strategies and the traditional law enforcement response to auto theft, (5) strategies employed, (6) geographic concentrations of auto theft, (7) factors that enhance or impede investigations, (8) opinions regarding effective approaches, (9) coordination among agencies, (10) suggestions for improving task force operations, (11) characteristics of auto theft rings, (12) training received, (13) resources and information needed, (14) measures of success, and (15) suggestions for public education efforts. Variables in Part 1 include the total number of vehicles and suspects involved in an incident, whether informants were used to solve the case, whether the stolen vehicle was used to buy parts, drugs, or weapons, whether there was a search warrant or an arrest warrant, whether officers used surveillance equipment, addresses of theft and recovery locations, date of theft and recovery, make and model of the stolen car, condition of vehicle when recovered, property recovered, whether an arrest was made, the arresting agency, date of arrest, arrest charges, number and type of charges filed, disposition, conviction charges, number of convictions, and sentence. Demographic variables include the age, sex, and race of the suspect, if known. Variables in Parts 2 and 3 include the goals of RATT, how the program evolved, the role of the IOC, how often the IOC met, the relationship of the IOC and the executive committee, how RATT was unique, why RATT was successful, how RATT could be improved, how RATT was funded, and ways in which auto theft could be reduced. Variables in Parts 4 and 5 include the goals of RATT, sources of information about vehicle thefts, strategies used to solve auto theft cases, location of most vehicle thefts, how motor vehicle thefts were impacted by RATT, impediments to the RATT program, suggestions for improving the program, ways in which auto theft could be reduced, and methods to educate citizens about auto theft. In addition, Part 5 also has variables about the type of officers' training, usefulness of maps and other data, descriptions of auto theft rings in terms of the age, race, and gender of its members, and types of cars stolen by rings.

Financial overview and grant giving statistics of San Diego County Crime Stoppers Inc.

This study focused on the effect of economic resources and racial/ethnic composition on the change in crime rates from 1970-2004 in United States cities in metropolitan areas that experienced a large growth in population after World War II. A total of 352 cities in the following United States metropolitan areas were selected for this study: Atlanta, Dallas, Denver, Houston, Las Vegas, Miami, Orange County, Orlando, Phoenix, Riverside, San Bernardino, San Diego, Silicon Valley (Santa Clara), and Tampa/St. Petersburg. Selection was based on the fact that these areas developed during a similar time period and followed comparable development trajectories. In particular, these 14 areas, known as the "boomburbs" for their dramatic, post-World War II population growth, all faced issues relating to the rapid growth of tract-style housing and the subsequent development of low density, urban sprawls. The study combined place-level data obtained from the United States Census with crime data from the Uniform Crime Reports for five categories of Type I crimes: aggravated assaults, robberies, murders, burglaries, and motor vehicle thefts. The dataset contains a total of 247 variables pertaining to crime, economic resources, and race/ethnic composition.

This dataset provides the number of domestic violence-related calls for assistance in 2018. Domestic violence is defined according to California Penal Code 13700. Information like this may be useful for studying safety and abuse.Spatial Extent: Southern California (Imperial, Kern, Los Angeles, Orange, Riverside, San Bernardino, San Diego, and Ventura County)Spatial Unit: CityCreated: 2018Updated: n/aSource: California Department of Justice (Criminal Justice Statistics Center)Contact Person: Open Justice InitiativeContact Email: openjustice@doj.ca.govSource Link: https://openjustice.doj.ca.gov/exploration/crime-statistics/domestic-violence-related-calls-assistance

The SanGIS data set includes an extensive collection of GIS maps that are available to the public.

 Application Data Included:

 1. Public Safety: Crime Mapping & Analysis, Computer Aided Dispatch,
 Emergency Response Planning

 2. Planning & Development: Specific Plans, Vegetation Mapping, Zoning,
 Geologic Hazards, Codes Enforcement

 3. Facilities Management: Water and Waste Water Utilities, Street
 Lighting, Storm Drains, Pavement Management

 4. Subdivision Mapping: Basemap Maintenance, Parcel Mapping, Survey
 Control, Orthophotography

 5. Route Management: Water Meter Readers, Trash & Recycling Routes

 6. Decision Support & Analysis: Facility Siting, Airport Noise, Slope
 Analysis, Demographics, Economic Development

 SanGIS was created in July, 1997, as a Joint Powers Agreement (JPA)
 between the City and County of San Diego. After 13 years of working
 together on data and application development, the City and County
 decided to formalize their partnership in GIS by creating the SanGIS
 JPA. Finding that access to correct and current geographic data was
 considered more important than application development to County and
 City departments, SanGIS focuses on ensuring geographic data is
 maintained and accessible.

 SanGIS Mission:

 To maintain and promote the use of a regional geographic data
 warehouse for the San Diego area and to facilitate the development of
 shared geographic data and automated systems which use that data.

 SanGIS Goals:

 1. To ensure geographic data currency and integrity.

 2. To provide cost effective access to geographic data to member
 agencies, subscribers and the public.

 3. To generate revenue from the sale of geographic data products to
 reduce the cost of map maintenance to member agencies.

 Data Collection:

 SanGIS data was created or obtained from several sources. Some of our
 data is licensed; some data was created from tabular digital files;
 some data was digitized from paper maps; and other data was entered
 using coordinate geometry tools.

 Updating the Data:

 Responsibility for the maintenance of the over 200 geographic data
 layers is distributed to City and County departments based on several
 factors such as who has the source documents, who has the greatest
 need for the data, and who is held accountable for this data as part
 of their city-wide or county-wide duties. Most basemap maintenance is
 completed by SanGIS staff. SanGIS is also responsible for coordinating
 with other data maintainers to ensure currency and accuracy for all
 participants.

 Data Coverage:

 All of the SanGIS geographic data is within San Diego County
 only. Much of our data covers the entire County of San Diego but some
 is only for the City of San Diego.

 [Summary provided by SanGIS]