Scroll to Data and select Download to create an csv/xslx download of the data.

Technical Specifications and Instructions pertaining to the data collection are available at the following link: https://vsp.virginia.gov/sections-units-bureaus/bass/criminal-justice-information-services/uniform-crime-reporting/#LE-agency-and-vendor-information

A data collection consisting of all traffic and investigatory stops made in Virginia as aggregated by Virginia Department of State Police. https://law.lis.virginia.gov/vacode/title52/chapter6.1/

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes over 1 gb of stop data from Ohio. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

This table contains demographics information for employees of the Chapel Hill Police Department.

Metropolitan Police Department (MPD) Police Service Areas (PSA). The dataset contains polygons representing of MPD PSA, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Police jurisdictions were initially created selecting street arcs from the planimetric street centerlines and street polygons, water polygons, real property boundaries and District of Columbia boundaries.2019 Boundary Changes:Periodically, MPD conducts a comprehensive assessment of our patrol boundaries to ensure optimal operations. This effort considers current workload, anticipated population growth, development, and community needs. The overarching goals for the 2019 realignment effort included: optimal availability of police resources, officer safety and wellness, and efficient delivery of police services. These changes took effect on 01/10/2019. On 03/27/2019, this boundary was modified to adjust dispatching of North Capitol Street’s northwest access roads to be more operationally efficient.

Assets:data tables each for city of Indianapolis (IN) and Baltimore (MD) in MS Excel and MS Word, ditto shapefiles in ESRI format; all files ZIPPEDContext – abstract of report published in a blog at the serial Geospatial World:This case study called into question police conduct and policy injustice discovered in two American big cities. My path to learn about Indianapolis (IN) and Baltimore (MD) policing patterns and crime events was due to availability of open data focused on use-of-force (UOF). My specific goal was to conduct geospatial data analytics aimed at these two cities using location and other key variables. Two spreadsheets captured small data that laid acceptable statistical groundwork for iterations of exploratory spatial data analysis (ESDA). Bivariate scatterplots revealed possible police misconduct. Parallel coordinate plotting – an innovative multivariate tool – was then used to display co-occurrences of plotted UOF and racial variables associated with key police districts in Indianapolis and Baltimore. A final summary visualization sought to cartographically and dramatically compare force and race variables by way of comparative plots, graphs, and maps. I closed with three action items pertaining to a “social-justice” framework for future data-visualization, to the heightening of standards for law enforcement reform, and for a need to make a hypothetical “citizen’s arrest” of police misconduct.

This data set contains instances of a field interview conducted by an MCP officer with an individual subject. Update Frequency: Daily

Austin City Council Resolution 20230914-132

This complete version of the dataset contains traffic citations issued in Somerville by Somerville police officers since 2017. Citations include both written warnings and those with a monetary fine. Every citation is composed of one or more violations. Each row in the dataset represents a violation. This data set should be refreshed daily with data appearing with a one-month delay (e.g. citations issued on 1/1 will appear on 2/1). If a daily update does not refresh, please email data@somervillema.gov.

Data Description: This dataset captures all subjects of traffic stops involving motor vehicles. Time of incident, officer assignment, race/sex of stop subject, and outcome of the stop ("Action taken") are also included in this data. Individual traffic stops may populate multiple data rows to account for multiple subjects and multiple outcomes: "interview number" is the unique identifier for every one (1) traffic stop.

Data Creation: Cincinnati Police Department (CPD) officers record all traffic stops involving motor vehicles via Contact Cards. Contact Cards are completed every time a CPD officer stops vehicles or pedestrians. The use of Contact Cards came out of the Collaborative Agreement.

Data Created By: The source of this data is the Cincinnati Police Department.

Refresh Frequency: This data is updated daily.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/h48j-wkz6

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

Crime and Policing Statistics

This data set contains data from individuals cited by a police officer in Montgomery County. Update Frequency : Daily

Chicago Police district station locations and contact information.

The Tempe Police Department prides itself in its continued efforts to reduce harm within the community and is providing this dataset on hate crime incidents that occur in Tempe.The Tempe Police Department documents the type of bias that motivated a hate crime according to those categories established by the FBI. These include crimes motivated by biases based on race and ethnicity, religion, sexual orientation, disability, gender and gender identity.The Bias Type categories provided in the data come from the Bias Motivation Categories as defined in the Federal Bureau of Investigation (FBI) National Incident-Based Reporting System (NIBRS) manual, version 2020.1 dated 4/15/2021. The FBI NIBRS manual can be found at https://www.fbi.gov/file-repository/ucr/ucr-2019-1-nibrs-user-manua-093020.pdf with the Bias Motivation Categories found on pages 78-79.Although data is updated monthly, there is a delay by one month to allow for data validation and submission.Information about Tempe Police Department's collection and reporting process for possible hate crimes is included in https://storymaps.arcgis.com/stories/a963e97ca3494bfc8cd66d593eebabaf.Additional InformationSource: Data are from the Law Enforcement Records Management System (RMS)Contact: Angelique BeltranContact E-Mail: angelique_beltran@tempe.govData Source Type: TabularPreparation Method: Data from the Law Enforcement Records Management System (RMS) are entered by the Tempe Police Department into a GIS mapping system, which automatically publishes to open data.Publish Frequency: MonthlyPublish Method: New data entries are automatically published to open data. Data Dictionary

This data contains information related to officer-initated stops by the City of Boulder Police Department. Information on the demographics of the person stopped (sex, race, ethnicity, year of birth, whether they are a Boulder resident) is included in this file. See the "Outcomes of Police Stops" dataset for more details on the outcome of the stop (stop location, duration, search, and result). This demographic data is collected at the stop level, and no individual-level identifiers are recorded in the system during a stop.The data published are limited to stops where the officer initiated, or had discretion, in making a stop. Instances where an officer is responding to a community or police call are considered non-discretionary, and demographics information is not collected for those stops and not included here. There are some instances of non-discretion within a stop interaction as well. For example, there may be instances where there is an outstanding felony warrant for the person stopped, and by law the officer must arrest that person.Please read the methodology and data dictionary documents for more information. The fields for this demographics dataset are referred to as the "Main" file in the data dictionary.

This dataset consists of the police districts within Bellevue. These districts are designated areas managed by specific police stations, responsible for providing law enforcement and public safety services. The dataset is crucial for coordinating policing efforts, resource allocation, and public safety planning among various law enforcement agencies in the city.

This data ceased updating with the transition to a new records management system on 11/14/2023. Access to the updated data set has been added as of April 11, 2025 here: https://data.wprdc.org/dataset/monthly-criminal-activity-dashboard.

The Police Blotter Archive contains crime incident data after it has been validated and processed to meet Uniform Crime Reporting (UCR) standards, published on a nightly basis. This data validation process creates a data publishing delay of approximately thirty days. Users who require the most recent incident data should use the 30 Day Police Blotter. The 30 Day Police Blotter dataset contains more recent data, but has not yet been run through quality control and standardization procedures by the Police Bureau. All data is reported at the block/intersection level, with the exception of sex crimes, which are reported at the police zone level.

This dataset only contains information reported by City of Pittsburgh Police, and does not contain incidents that solely involve other police departments operating within the city (campus police, Port Authority, etc.)

More documentation is available in our Crime Data Guide.

Data Description: This dataset captures all Cincinnati Police Department Calls for Service. The City of Cincinnati's Computer Aided Dispatch (CAD) system records police incident response activity, which includes all calls for service to emergency operators, 911, alarms, police radio and non-emergency calls. CAD records all dispatch information, which is used by dispatchers, field supervisors, and on-scene officers to determine the priority, severity, and response needs surrounding the incident. Once an officer responds to a call, he/she updates the disposition to reflect findings on-scene.

This dataset includes both proactive and reactive police incident data.

Data Creation: This data is created through the City’s computer-aided dispatch (CAD) system.

Data Created By: The source of this data is the Cincinnati Police Department.

Refresh Frequency: This data is updated daily.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/xw7t-5phj

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.