In 2023, the District of Columbia had the highest reported violent crime rate in the United States, with 1,150.9 violent crimes per 100,000 of the population. Maine had the lowest reported violent crime rate, with 102.5 offenses per 100,000 of the population. Life in the District The District of Columbia has seen a fluctuating population over the past few decades. Its population decreased throughout the 1990s, when its crime rate was at its peak, but has been steadily recovering since then. While unemployment in the District has also been falling, it still has had a high poverty rate in recent years. The gentrification of certain areas within Washington, D.C. over the past few years has made the contrast between rich and poor even greater and is also pushing crime out into the Maryland and Virginia suburbs around the District. Law enforcement in the U.S. Crime in the U.S. is trending downwards compared to years past, despite Americans feeling that crime is a problem in their country. In addition, the number of full-time law enforcement officers in the U.S. has increased recently, who, in keeping with the lower rate of crime, have also made fewer arrests than in years past.

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed. This study examines municipal crime levels and changes over a nine year time frame, from 2000-2008, in the fifth largest primary Metropolitan Statistical Area (MSA) in the United States, the Philadelphia metropolitan region. Crime levels and crime changes are linked to demographic features of jurisdictions, policing arrangements and coverage levels, and street and public transit network features.

In the fall of 2013, Temple University's Department of Criminal Justice was awarded a research grant by the National Institute of Justice to evaluate Philadelphia's Focused Deterrence (FD) strategy. The study was designed to provide a comprehensive, objective review of FD and to determine if the law enforcement partnership accomplished what it set out to accomplish. The findings from this study highlight the key results from the impact evaluation that assessed whether gun violence saw a statistically significant decline that could be attributed to FD. The impact evaluation focused on area-level reductions in shootings. The evaluation uses victim and incident data from 2003 through March 2015 received from Philadelphia Police Department. The post-FD period of examination consists of the first 24 months after the implementation of FD (April 2013 through March 2015).

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.

Crime incidents from the Philadelphia Police Department. Part I crimes include violent offenses such as aggravated assault, rape, arson, among others. Part II crimes include simple assault, prostitution, gambling, fraud, and other non-violent offenses.

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 was designed to investigate whether the presence of CCTV cameras can reduce crime by studying the cameras and crime statistics of a controlled area. The viewsheds of over 100 CCTV cameras within the city of Philadelphia, Pennsylvania were defined and grouped into 13 clusters, and camera locations were digitally mapped. Crime data from 2003-2013 was collected from areas that were visible to the selected cameras, as well as data from control and displacement areas using an incident reporting database that records the location of crime events. Demographic information was also collected from the mapped areas, such as population density, household information, and data on the specific camera(s) in the area. This study also investigated the perception of CCTV cameras, and interviewed members of the public regarding topics such as what they thought the camera could see, who was watching the camera feed, and if they were concerned about being filmed.

The purpose of this study was to examine interrelated issues surrounding the use of the criminal justice system by immigrant victims and to identify ways to improve the criminal justice response to immigrants' needs and problems. Two cities, New York City and Philadelphia, were selected for intensive investigation of victimization of immigrants. In each of these cities, three immigrant communities in a neighborhood were chosen for participation. In New York's Jackson Heights area, Colombians, Dominicans, and Indians were the ethnic groups studied. In Philadelphia's Logan section, Vietnamese, Cambodians, and Koreans were surveyed. In all, 87 Jackson Heights victims were interviewed and 26 Philadelphia victims were interviewed. The victim survey questions addressed can be broadly divided into two categories: issues pertaining to crime reporting and involvement with the court system by immigrant victims. Variables include type of crime, respondent's role in the incident, relationship to the perpetrator, whether the incident was reported to police, and who reported the incident. Respondents were also asked whether they were asked to go to court, whether they understood what the people in court said to them, whether they understood what was happening in their case, and, if victimized again, whether they would report the incident to the police.

This study measured the difference that defense counsel made to the outcome of homicide and death penalty cases. One in five indigent murder defendants in Philadelphia were randomly assigned representation by the Defender Association of Philadelphia while the remainder received court-appointed private attorneys. This study's research design utilized this random assignment to measure how defense counsel affected murder case outcomes. The research team collected data on 3,157 defendants charged with murder in Philadelphia Municipal Court between 1995-2004, using records provided by the Philadelphia Courts (First Judicial District of Pennsylvania). Data were also obtained from the Philadelphia Court of Common Pleas, the Pennsylvania Unified Judicial System web portal, the National Corrections Reporting Program, and the 2000 Census. This study contains a total of 47 variables including public defender representation, defendant demographics, ZIP code characteristics, prior criminal history, case characteristics, case outcomes, and case handling procedures.

!!!WARNING~~~This dataset has a large number of flaws and is unable to properly answer many questions that people generally use it to answer, such as whether national hate crimes are changing (or at least they use the data so improperly that they get the wrong answer). A large number of people using this data (academics, advocates, reporting, US Congress) do so inappropriately and get the wrong answer to their questions as a result. Indeed, many published papers using this data should be retracted. Before using this data I highly recommend that you thoroughly read my book on UCR data, particularly the chapter on hate crimes (https://ucrbook.com/hate-crimes.html) as well as the FBI's own manual on this data. The questions you could potentially answer well are relatively narrow and generally exclude any causal relationships. ~~~WARNING!!!Version 8 release notes:Adds 2019 dataVersion 7 release notes:Changes release notes description, does not change data.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), made all character values lower case, reordered columns. I also generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.

Version 5 release notes:
Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. Version 4 release notes:
Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent.
Made the 'population' column which is the total population in that agency.

Version 3 release notes:
Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes:
Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.

Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency.
Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.).

The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

This study was an evaluation of multiple imputation strategies to address missing data using the New Approach to Evaluating Supplementary Homicide Report (SHR) Data Imputation, 1990-1995 (ICPSR 20060) dataset.

To explore the relationship between alcohol, drugs, and crime in the electronic dance music and hip hop nightclub scenes of Philadelphia, Pennsylvania, researchers utilized a multi-faceted ethnographic approach featuring in-depth interviews with 51 respondents (Dataset 1, Initial Interview Qualitative Data) and two Web-based follow-up surveys with respondents (Dataset 2, Follow-Up Surveys Quantitative Data). Recruitment of respondents began in April of 2005 and was conducted in two ways. Slightly more than half of the respondents (n = 30) were recruited with the help of staff from two small, independent record stores. The remaining 21 respondents were recruited at electronic dance music or hip hop nightclub events. Dataset 1 includes structured and open-ended questions about the respondent's background, living situation and lifestyle, involvement and commitment to the electronic dance music and hip hop scenes, nightclub culture and interaction therein, and experiences with drugs, criminal activity, and victimization. Dataset 2 includes descriptive information on how many club events were attended, which ones, and the activities (including drug use and crime/victimization experiences) taking place therein. Dataset 3 (Demographic Quantitative Data) includes coded demographic information from the Dataset 1 interviews.

Beginning in the mid-1980s, the Office of Juvenile Justice and Delinquency Prevention (OJJDP) funded the creation of Habitual Offender Units (HOUs) in 13 cities. HOUs were created to prosecute habitual juvenile offenders by deploying the most experienced attorneys to handle these cases from start to finish. By targeting the earliest points in the career sequence of the juvenile offenders, the greatest number of serious offenses can potentially be averted. Selection criteria to qualify for priority prosecution by an HOU usually encompassed one or more generic components relating to aspects of a juvenile's present and prior offense record. In Philadelphia, to be designated a serious habitual offender and to qualify for priority prosecution by the HOU, a youth had to have two or more prior adjudications or open cases for specific felonies, as well as a current arrest for a specified felony. The first three police contacts in a Philadelphia juvenile offender's record were of special interest because they included the earliest point (i.e., the third contact) at which a youth could be prosecuted in the Philadelphia HOU, under their selection criteria. The main objectives of this study were to determine how well the selection criteria identified serious habitual offenders and which variables, reflecting HOU selection criteria, criminal histories, and personal characteristics, were most strongly and consistently related to the frequency and seriousness of future juvenile and young adult offending. To accomplish this, an assessment was conducted using a group of juveniles born in 1958 whose criminal career outcomes were already known. Applying the HOU selection criteria to this group made it possible to determine the extent to which the criteria identified future habitual offending. Data for the analyses were obtained from a birth cohort of Black and white males born in 1958 who resided in Philadelphia from their 10th through their 18th birthdays. Criminal careers represent police contacts for the juvenile years and arrests for the young adult years, for which police contacts and arrests are synonymous. The 40 dependent variables were computed using 5 different criminal career aspects for 4 crime type groups for 2 age intervals. The data also contain various dummy variables related to prior offenses, including type of offense, number of prior offenses, disposition of the offenses, age at first prior offense, seriousness of first prior offense, weapon used, and whether it was a gang-related offense. Dummy variables pertaining to the current offenses include type of offense, number of crime categories, number of charges, number of offenders, gender, race, and age of offenders, type of intimidation used, weapons used, number of crime victims, gender, race, and age of victims, type of injury to victim, type of victimization, characteristics of offense site, type of complainant, and police response. Percentile of the offender's socioeconomic status is also provided. Continuous variables include age at first prior offense, age at most recent prior offense, age at current offense, and average age of victims.