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.

Description Crime incidents starting with those reported in 2016. The data provided is the latest available information and is updated regularly as statistics change. For access to comprehensive reports, kindly submit a public record request here.Note: Crimes that occurred before 2016 are included if the date reported was in 2016 or later.

Disclaimer: The City strives to provide the highest-quality information on this platform. The content on this website is provided as a public service, on an ‘as is’ basis. The City makes no warranty, representation, or guarantee of any type as to the content, accuracy, timeliness, completeness, or fitness for any particular purpose or use of any public data provided on this portal; nor shall any such warranty be implied, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose. The City assumes no liability by making data available to the public or other departments.This dataset is featured in the following app(s): Cleveland Division of Police Crime DashboardCrime Incidents MapData GlossarySee the Attributes section below for details about each column in this dataset.Update Frequency Daily around 8 AM EST

Contacts

 City of Cleveland, Division of Police

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Crawford County, OH (DISCONTINUED) (FBITC039033) from 2005 to 2021 about Crawford County, OH; crime; violent crime; property crime; OH; and USA.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Franklin County, OH (DISCONTINUED) (FBITC039049) from 2004 to 2021 about Franklin County, OH; crime; violent crime; property crime; Columbus; OH; and USA.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Hamilton County, OH (DISCONTINUED) (FBITC039061) from 2004 to 2021 about Hamilton County, OH; crime; violent crime; property crime; Cincinnati; OH; and USA.

In 2023, a total of ******* violent crimes were committed in Texas, the most out of any U.S. state. New York followed, with ******* violent crimes committed. California, Illinois, and Michigan rounded out the top five states for violent crimes in that year.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Ohio County, KY was 119.00000 Known Incidents in January of 2021, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Ohio County, KY reached a record high of 209.00000 in January of 2012 and a record low of 89.00000 in January of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Ohio County, KY - last updated from the United States Federal Reserve on July of 2025.

The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a "very high crime" tract was based on its crime rate compared to crime rates for other tracts in the same city. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988.

The impact of criminal victimization on the health status of women is the focus of this data collection. The researchers examined the extent to which victimized women differed from nonvictimized women in terms of their physical and psychological well-being and differences in their use of medical services. The sample was drawn from female members of a health maintenance plan at a worksite in Cleveland, Ohio. Questions used to measure criminal victimization were taken from the National Crime Survey and focused on purse snatching, home burglary, attempted robbery, robbery with force, threatened assault, and assault. In addition, specific questions concerning rape and attempted rape were developed for the study. Health status was assessed by using a number of instruments, including the Cornell Medical Index, the Mental Health Index, and the RAND Corporation test battery for their Health Insurance Experiment. Medical service usage was assessed by reference to medical records. Demographic information includes age, race, income, and education.

The study examined the situational and contextual influences on violence in bars and apartment complexes in Cincinnati, Ohio. Interviews of managers and observations of sites were made for 199 bars (Part 1). Data were collected on 1,451 apartment complexes (Part 2). For apartment complexes owners were interviewed for 307 and observations were made at 994. Crime data were obtained from the Cincinnati Police Department records of calls for service and reported crimes.

Much of the analysis of juvenile justice reform to date has focused on assessing particular programs and their impacts on subgroups of cases at a particular point in time. While this is instructive as to the effects of those initiatives, it is essential to evaluate the impact of policy across multiple levels and with multiple stakeholders in mind. Ohio has implemented a series of initiatives in its juvenile justice system designed to reduce reliance on state custody of youth in favor of local alternatives. In doing so, they have focused on multiple segments of the population of justice involved-youths throughout the state. The main vehicle for these shifts has been the state's Reasoned and Equitable Community and Local Alternatives to the Incarceration of Minors (RECLAIM) legislation and a series of initiatives that have followed from its inception. Other steps were followed and programming modifications were made during the study period as well. This research project focused on these initiatives as a case study of juvenile justice reform initiatives in order to provide insights about the impact of those recent reforms across multiple dimensions that were viewed as relevant to the discussion of juvenile justice reform. The data set analyzed at the individual level included the records of more than 5,000 youths sampled from cases processed from 2008 to 2015. First, presumed reductions in the number of youth committed to state residential correctional facilities in favor of community-based alternatives were analyzed. The relative effectiveness of residential facilities and community-based alternatives in terms of youth recidivism were then assessed with a subsample of 2,855 case records from randomly-selected counties. A third research objective focused on county-level trends and variation. Specifically, the longitudinal trends in key juvenile justice inputs and official juvenile crime rates across Ohio's 88 counties were formally modeled using data from public reports, data collection with counties, and official juvenile arrest data archived by the Federal Bureau of Investigation. Elements of the previous analyses (especially comparative recidivism rates) and cost data collected from existing sources and public reports were used in a preliminary fashion to quantify the potential return on investment that accrued from Ohio's investment in these juvenile justice initiatives. This deposit contains two datasets: Individual Level Data and County Level Data. The Individual Level Data contains the following demographic data: age at admission, sex, and race (White, Black, Asian, Native American, and other).

Financial overview and grant giving statistics of Central Ohio Crime Stoppers Inc.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Jefferson County, OH (DISCONTINUED) (FBITC039081) from 2004 to 2020 about Jefferson County, OH; crime; violent crime; property crime; OH; and USA.

The purpose of the study was to evaluate the implementation of the Safe City crime prevention model that was implemented in designated retail areas in jurisdictions across the United States. The model involved frequent meetings and information-sharing among the police, Target, and neighboring retailers, along with the implementation of enhanced technology. The first step in the Safe City evaluation involved selecting evaluation sites. The final sites selected were Chula Vista, California, and Cincinnati, Ohio. Next, for each of the two sites, researchers selected a site that had a potential for crime displacement caused by the intervention area, and a matched comparison area in another jurisdiction that would likely have been selected as a Safe City site. For Chula Vista, the displacement area was 2 miles east of the intervention area and the comparison area was in Houston, Texas. For Cincinnati, the displacement area was 1.5 miles north of the intervention area and the comparison area was in Buffalo, New York. In Chula Vista, the Safe City intervention activities were focused on gaining a better understanding of the nature and underlying causes of the crime and disorder problems occurring in the designated Safe City site, and strengthening pre-existing partnerships between law enforcement and businesses affected by these problems. In Cincinnati, the Safe City intervention activities centered on increasing business and citizen awareness, communication, and involvement in crime control and prevention activities. The research team collected pre- and post-intervention crime data from local police departments (Part 1) to measure the impact of the Safe City initiatives in Chula Vista and Cincinnati. The 981 records in Part 1 contain monthly crime counts from January 2004 to November 2008 for various types of crime in the retail areas that received the intervention in Chula Vista and Cincinnati, and their corresponding displacement zones and matched comparison areas. Using the monthly crime counts contained in the Safe City Monthly Crime Data (Part 1) and estimations of the total cost of crime to society for various offenses from prior research, the research team calculated the total cost of crimes reported during the month/year for each crime type that was readily available (Part 2). The 400 records in the Safe City Monthly Cost Benefit Analysis Data (Part 2) contain monthly crime cost estimates from January 2004 to November 2008 for assaults, burglaries, larcenies, and robberies in the retail areas that received the intervention in Chula Vista and Cincinnati, and their corresponding displacement zones and matched comparison areas. The research team also received a total of 192 completed baseline and follow-up surveys with businesses in Chula Vista and Cincinnati in 2007 and 2008 (Part 3). The surveys collected data on merchants' perceptions of crime and safety in and around businesses located in the Safe City areas. The Safe City Monthly Crime Data (Part 1) contain seven variables including the number of crimes in the target area, the month and year the crime was committed, the number of crimes in the displacement area, the number of crimes in a comparable area in a comparable city, the city, and the crime type. The Safe City Monthly Cost Benefit Analysis Data (Part 2) contain seven variables including the cost of the specified type of crime occurring in the target area, the month and year the cost was incurred, the cost of the specified type of crime in the displacement area, the cost of the specified type of crime in a matched comparison area, the city, and the crime type. The Safe City Business Survey Data (Part 3) contain 132 variables relating to perceptions of safety, contact with local police, experience and reporting of crime, impact of crime, crime prevention, community connections, and business/employee information.

This study investigated changes in the geographic concentration of drug crimes in Cleveland from 1990 to 2001. The study looked at both the locations of drug incidents and where drug offenders lived in order to explore factors that bring residents from one neighborhood into other neighborhoods to engage in drug-related activities. This study was based on data collected for the 224 census tracts in Cleveland, Ohio, in the 1990 decennial Census for the years 1990 to 1997 and 1999 to 2001. Data on drug crimes for 1990 to 1997 and 1999 to 2001 were obtained from Cleveland Police Department (CPD) arrest records and used to produce counts of the number of drug offenses that occurred in each tract in each year and the number of arrestees for drug offenses who lived in each tract. Other variables include counts and rates of other crimes committed in each census tract in each year, the social characteristics and housing conditions of each census tract, and net migration for each census tract.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Perry County, OH (DISCONTINUED) (FBITC039127) from 2004 to 2021 about Perry County, OH; crime; violent crime; property crime; OH; and USA.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Noble County, OH (DISCONTINUED) (FBITC039121) from 2015 to 2019 about Noble County, OH; crime; violent crime; property crime; OH; and USA.

The purpose of this research was to improve understanding of the conditions under which criminal sanctions do and do not reduce repeat violence between intimate partners. This study involved repeated reading and close inspection of four documents in order to compare and contrast the multivariate analyses reported by John Wooldredge and Amy Thistlethwaite (RECONSIDERING DOMESTIC VIOLENCE RECIDIVISM: INDIVIDUAL AND CONTEXTUAL EFFECTS OF COURT DISPOSITIONS AND STAKE IN CONFORMITY IN HAMILTON COUNTY, OHIO, 1993-1998 [ICPSR 3013]). The first part of this study's design involved the detailed literature review of four Wooldredge and Thistlethwaite publications between the years 1999 and 2005. The second element of the study's design required researchers to gain a detailed understanding of the archived data using the documentation provided by Wooldredge. The third element of the study's secondary analysis research design involved using the identified variables to reproduce the multivariate empirical findings about the effects of sanctions, stakes, and social context on repeat offending. These findings were presented in a series of tables in the four Wooldredge and Thistlethwaite publications. After numerous iterations of reading reports and documentation and exploring alternative measures and methods, researchers produced a report detailing their ability to reproduce Wooldredge and Thistlethwaite's descriptive measures. This study's design called for using explicit criteria for determining the extent to which Wooldredge and Thistlethwaite's findings could be reproduced. Researchers developed and applied three criteria for making that determination. The first was a simple comparison of the regression coefficients and standard errors. The second criterion was a determination of whether the reproduced results conformed to the direction and statistical significance levels of the original analyses. The third criterion was to apply a statistical test to assess the significance of any differences in the sizes of the original and the reproduced coefficients. The data archived by Wooldredge provided seven dichotomous measures of criminal sanctions (no charges filed, dismissed, acquitted, a treatment program, probation only, jail only, and a combination of probation and jail). Part of the design of this study was to go beyond reproducing Wooldredge and Thistlethwaite's approaches and to reformulate the available measures of criminal sanctions to more directly test the prosecution, conviction, and sentence severity hypotheses. The researchers produced these tests by constructing three new measures of criminal sanctions (prosecution, conviction, and sanction severity) and testing each of them in separate multivariate models. The Part 1 (Hamilton County, Ohio, Census Tract Data) data file contains 206 cases and 35 variables. The Part 2 (Neighborhood Data) data file contains 47 cases and 12 variables. The variables in Part 1 (Hamilton County, Ohio, Census Tract Data) include a census tract indicator, median household income of tract, several proportions such as number of college graduates in the tract and corresponding Z-scores, a regression factor score for analysis 1, a socio-economic factor, a census tract number for the city of Cincinnati, Ohio, and a Cincinnati neighborhoods indicator. Variables in Part 2 (Neighborhood Data) include a neighborhood indicator, average age in the neighborhood, demographic proportions such as proportion male in the neighborhood and proportion of college graduates in the neighborhood, and a social class factor.

Alaska saw the highest rape rate in the United States in 2023, with 118.4 rapes per 100,000 inhabitants. The lowest rate was found in New Jersey, with 17.9 rapes per 100,000 inhabitants. Sexual assault in Alaska Fighting sexual assault in Alaska is particularly difficult due to small, isolated, close-knit communities who can be wary of airing their dirty laundry to outsiders, as well as a low number of law enforcement employees in the state. In addition, Alaska’s low population is spread out over a large land area, meaning that in the event of an assault being reported to police, it can take law enforcement hours, or even days, to reach the most isolated communities. The victims of sexual assault There tends to be more reported female victims of sexual assault than male victims. However, since sexual assault is typically an underreported crime, especially among males, these figures could be, and probably are, much higher. In addition, many victims of sexual offenses tend to be young, although sexual assault can occur at any age.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Montgomery County, OH (DISCONTINUED) (FBITC039113) from 2009 to 2021 about Montgomery County, OH; Dayton; crime; violent crime; property crime; OH; and USA.