This dataset contains county-level totals for the years 2002-2014 for eight types of crime: murder, rape, robbery, aggravated assault, burglary, larceny, motor vehicle theft, and arson. These crimes are classed as Part I criminal offenses by the United States Federal Bureau of Investigations (FBI) in their Uniform Crime Reporting (UCR) program. Each record in the dataset represents the total of each type of criminal offense reported in (or, in the case of missing data, attributed to) the county in a given year.

Gain critical insights into crime trends, risk assessment, and public safety with our comprehensive Crime Dataset. Designed for law enforcement agencies, researchers, and analysts, this dataset provides structured and reliable crime data to support investigations, policy-making, and crime prevention strategies.

Dataset Features

Crime Reports: Access detailed records of reported crimes, including incident type, date, time, and location. Law Enforcement Data: Extract information on arrests, case statuses, and law enforcement responses. Geospatial Crime Mapping: Analyze crime distribution across different regions, cities, and neighborhoods. Trends & Patterns: Identify crime trends over time, including seasonal fluctuations and high-risk areas. Demographic Insights: Understand crime demographics, including offender and victim profiles.

Customizable Subsets for Specific Needs Our Crime Dataset is fully customizable, allowing you to filter data based on crime type, location, time period, or law enforcement jurisdiction. Whether you need broad coverage for national crime analysis or focused data for local risk assessment, we tailor the dataset to your needs.

Popular Use Cases

Crime Risk Assessment & Prevention: Identify high-crime areas, assess risk factors, and develop crime prevention strategies. Law Enforcement & Investigations: Support law enforcement agencies with structured crime data for case analysis and intelligence gathering. Urban Planning & Public Safety: Use crime data to inform city planning, improve public safety measures, and allocate resources effectively. AI & Predictive Analytics: Train AI models for crime forecasting, anomaly detection, and predictive policing. Policy & Legal Research: Analyze crime trends to support policy-making, legal studies, and criminal justice reforms.

Whether you're analyzing crime trends, supporting law enforcement, or developing predictive models, our Crime Dataset provides the structured data you need. Get started today and customize your dataset to fit your research and security objectives.

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) for all complete quarters so far this year (2017). For additional details, please see the attached data dictionary in the ‘About’ section.

Serious violent crimes consist of Part 1 offenses as defined by the U.S. Department of Justice’s Uniform Reporting Statistics. These include murders, nonnegligent homicides, rapes (legacy and revised), robberies, and aggravated assaults. LAPD data were used for City of Los Angeles, LASD data were used for unincorporated areas and cities that contract with LASD for law enforcement services, and CA Attorney General data were used for all other cities with local police departments. This indicator is based on location of residence. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Neighborhood violence and crime can have a harmful impact on all members of a community. Living in communities with high rates of violence and crime not only exposes residents to a greater personal risk of injury or death, but it can also render individuals more susceptible to many adverse health outcomes. People who are regularly exposed to violence and crime are more likely to suffer from chronic stress, depression, anxiety, and other mental health conditions. They are also less likely to be able to use their parks and neighborhoods for recreation and physical activity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Note: Due to the RMS change for CPS, this data set stops on 6/2/2024. For records beginning on 6/3/2024, please see the dataset at this link: https://data.cincinnati-oh.gov/safety/Reported-Crime-STARS-Category-Offenses-/7aqy-xrv9/about_data

The combined data will be available by 3/10/2025 at the linke above.

Data Description: This data represents reported Crime Incidents in the City of Cincinnati. Incidents are the records, of reported crimes, collated by an agency for management. Incidents are typically housed in a Records Management System (RMS) that stores agency-wide data about law enforcement operations. This does not include police calls for service, arrest information, final case determination, or any other incident outcome data.

Data Creation: The Cincinnati Police Department's (CPD) records crime incidents in the City through Records Management System (RMS) that stores agency-wide data about law enforcement operations.

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/8eaa-xrvz

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.

This data collection contains responses to victimization surveys that were administered as part of both the planning and evaluation stages of the Hartford Project, a crime opportunity reduction program implemented in a residential neighborhood in Hartford, Connecticut, in 1976. The Hartford Project was an experiment in how to reduce residential burglary and street robbery/purse snatching and the fear of those crimes. Funded through the Hartford Institute of Criminal and Social Justice, the project began in 1973. It was based on a new "environmental" approach to crime prevention: a comprehensive and integrative view addressing not only the relationship among citizens, police, and offenders, but also the effect of the physical environment on their attitudes and behavior. The surveys were administered by the Center for Survey Research at the University of Massachusetts at Boston. The Center collected Hartford resident survey data in five different years: 1973, 1975, 1976, 1977, and 1979. The 1973 survey provided basic data for problem analysis and planning. These data were updated twice: in 1975 to gather baseline data for the time of program implementation, and in the spring of 1976 with a survey of households in one targeted neighborhood of Hartford to provide data for the time of implementation of physical changes there. Program evaluation surveys were carried out in the spring of 1977 and two years later in 1979. The procedures for each survey were essentially identical each year in order to ensure comparability across time. The one exception was the 1976 sample, which was not independent of the one taken in 1975. In each survey except 1979, respondents reported on experiences during the preceding 12-month period. In 1979 the time reference was the past two years. The survey questions were very similar from year to year, with 1973 being the most unique. All surveys focused on victimization, fear, and perceived risk of being victims of the target crimes. Other questions explored perceptions of and attitudes toward police, neighborhood problems, and neighbors. The surveys also included questions on household and respondent characteristics.

The primary purpose of the second wave of the National Neighborhood Crime Study (NNCS2) was to develop a panel dataset of serious reported crimes in urban neighborhoods circa two time points - 2000 and 2010. These data offer the opportunity to assess the sources and consequences of neighborhood crime change for "communities" of different ethno-racial and economic compositions across the United States. The study also sought to examine the role of a neighborhood's broader ecology on crime levels and crime change by integrating indicators of city and/or metropolitan conditions. The NNCS2 includes two datasets. The first dataset, the NNCS2-Panel file (NNCS2-P), contains information on the Federal Bureau of Investigation's (FBI) Part 1 Index crimes (except arson), socioeconomic and demographic characteristics, and a variety of other neighborhood and city level controls for circa 2000 and 2010 for tracts in 81 of the 91 cities in the NNCS, wave 1. The second dataset, the NNCS2-Cross-Sectional file (NNCS2-CS), allows for examination of the local and contextual sources of neighborhood crime inequality circa 2010. The NNCS2-CS incorporates parallel data for census tracts and cities as in the Panel file, but includes a few additional cities for which panel data could not be compiled, as well information on the metropolitan areas within which cities are located.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

Recorded crime figures for CSP areas. Number of offences for the last two years, percentage change, and rates per 1,000 population for the latest year.

This study assessed the implementation and impact of the One Vision One Life (OVOL) violence-prevention strategy in Pittsburgh, Pennsylvania. In 2003, the rise in violence in Pittsburgh prompted community leaders to form the Allegheny County Violence Prevention Imitative, which became the OVOL program. The OVOL program sought to prevent violence using a problem-solving, data-driven model to inform how community organizations and outreach teams respond to homicide incidents. The research team examined the impact of the OVOL program on violence using a quasi-experimental design to compare violence trends in the program's target areas before and after implementation to (1) trends in Pittsburgh neighborhoods where One Vision was not implemented, and (2) trends in specific nontarget neighborhoods whose violence and neighborhood dynamics One Vision staff contended were most similar to those of target neighborhoods. The Pittsburgh Bureau of Police provided the violent-crime data, which the research team aggregated into monthly counts. The Pittsburgh Department of City Planning provided neighborhood characteristics data, which were extracted from the 2000 Census. Monthly data were collected on 90 neighborhoods in Pittsburgh, Pennsylvania from 1996 to 2007, resulting in 12,960 neighborhood-by-month observations.

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit https://crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

Tempe’s trust data for this measure is collected every month and comes from the “Safety” result from the monthly administered Police Sentiment Survey. There is one question which feeds into these results: "When it comes to the threat of crime, how safe do you feel in your neighborhood?" Benchmark data is from cohorts of communities with similar characteristics, such as size, population density, and region. This data is collected every month and quarter via a recurring report. This page provides data for the Feeling of Safety in Your Neighborhood performance measure. The performance measure dashboard is available at 1.05 Feeling of Safety in Your Neighborhood. Additional Information Source: Zencity Contact: Adam SamuelsContact email: Adam_Samuels@tempe.govData Source Type: Excel, CSVPreparation Method: Take the "Safety" score from the Police Sentiment Survey. This score includes the average of the top two results from the question underneath this area on the report. These months are then averaged to get the quarterly score.Publish Frequency: MonthlyPublish Method: Manual Data Dictionary

Context

From 1934 to 1963, San Francisco was infamous for housing some of the world's most notorious criminals on the inescapable island of Alcatraz. Today, the city is known more for its tech scene than its criminal past. But, with rising wealth inequality, housing shortages, and a proliferation of expensive digital toys riding BART to work, there is no scarcity of crime in the city by the bay. From Sunset to SOMA, and Marina to Excelsior, this dataset provides nearly 12 years of crime reports from across all of San Francisco's neighborhoods.

This dataset was featured in our completed playground competition entitled San Francisco Crime Classification. The goals of the competition were to:

• predict the category of crime that occurred, given the time and location

• visualize the city and crimes (see Mapping and Visualizing Violent Crime for inspiration)

Content

This dataset contains incidents derived from SFPD Crime Incident Reporting system. The data ranges from 1/1/2003 to 5/13/2015. The training set and test set rotate every week, meaning week 1,3,5,7... belong to test set, week 2,4,6,8 belong to training set. There are 9 variables:

• Dates - timestamp of the crime incident

• Category - category of the crime incident (only in train.csv).

• Descript - detailed description of the crime incident (only in train.csv)

• DayOfWeek - the day of the week

• PdDistrict - name of the Police Department District

• Resolution - how the crime incident was resolved (only in train.csv)

• Address - the approximate street address of the crime incident

• X - Longitude

• Y - Latitude

Acknowledgements

This dataset is part of our completed playground competition entitled San Francisco Crime Classification. Visit the competition page if you are interested in checking out past discussions, competition leaderboard, or more details regarding the competition. If you are curious to see how your results rank compared to others', you can still make a submission at the competition submission page!

The original dataset is from SF OpenData, the central clearinghouse for data published by the City and County of San Francisco.

Suburb-based crime statistics for crimes against the person and crimes against property. The Crime statistics datasets contain all offences against the person and property that were reported to police in that respective financial year. The Family and Domestic Abuse-related offences datasets are a subset of this, in that a separate file is presented for these offences that were flagged as being of a family and domestic abuse nature for that financial year. Consequently the two files for the same financial year must not be added together.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

Toronto Neighbourhoods Boundary File includes Crime Data by Neighbourhood. Counts are available at the offence and/or victim level for Assault, Auto Theft, Bike Theft, Break and Enter, Robbery, Theft Over, Homicide, Shootings and Theft from Motor Vehicle. Data also includes crime rates per 100,000 people by neighbourhood based on each year's Projected Population by Environics Analytics.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario..In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

This dataset includes all Major Crime Indicators (MCI) occurrences by reported date and related offences since 2014.Major Crime Indicators DashboardDownload DocumentationThe Major Crime Indicators categories include Assault, Break and Enter, Auto Theft, Robbery and Theft Over (Excludes Sexual Violations). This data is provided at the offence and/or victim level, therefore one occurrence number may have several rows of data associated to the various MCIs used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

Context

This dataset is used to find the best neighborhood to open a new gym in Toronto city. The data was collected the neighborhood profile data https://open.toronto.ca/dataset/neighbourhood-profiles/ and the crime data https://open.toronto.ca/dataset/neighbourhood-crime-rates/ and the venues information was obtained from Foursquare API.

Content

Total population: The population for each neighborhood of Toronto city during 2016. number of educated people: The number of educated people per neighborhood. number of 15-45: The number of population aged from 15 to 45. number of employers: The number of employers per neighborhood. long_latt: The longitudes and latitudes for each neighborhood. number of gyms: The number of gyms in each neighborhood. number of venues: The number of venues in each neighborhood.

For official crime statistics, please visit CMPD's Crime Statistics page at:https://charlottenc.gov/CMPD/Safety/Pages/CrimeStats.aspx . Includes all CMPD incident report types, both criminal and non-criminal. Many reports are taken only to fully document a non-criminal circumstance like a missing person, lost/missing property, etc. Other reports are only taken to document the recovery of vehicles stolen in other jurisdictions. Each incident is classified based on FBI NIBRS standards by applying a national crime hierarchy to choose the highest offense assigned to each report. More information about NIBRS standards can be found on the FBI website. Cases where Highest NIBRS Code / Highest NIBRS Description is non-criminal offense (codes in the 800 series) should not be included in analysis of total “criminal” incident reports. In addition, data includes incidents with any clearance status, including unfounded cases. A clearance status of “Unfounded” means the report has been investigated and determined either to be a false report or to involve circumstances that do not actually constitute a crime.