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.

The study was designed to explore the relationship between employment and involvement with the criminal justice system. Males arrested primarily for felony offenses were interviewed at the central booking agency in Brooklyn, New York, at the time of their arrests in 1979. A subsample of 152 arrestees was reinterviewed in 1980. The data include information on labor market participation, arrests, periods of incarceration, and the respondents' demographic characteristics. The labor market information spans a two-year period prior to those arrests. Arrest history and other criminal justice data cover the two years prior to arrest and one year following the arrest. Additional variables supply information on employment and occupation, social and neighborhood characteristics, and perceptions of the risk of committing selected crimes.

This map shows the incidence of seven major felonies -- burglary, felony assault, grand larceny, grand larceny of a motor vehicle, murder, rape, and robbery -- in New York City over the past year. Data can be mapped in aggregate at the precinct level, as a heat map showing concentration of crimes, or as individual incident points.

All BPD data on Open Baltimore is preliminary data and subject to change. The information presented through Open Baltimore represents Part I victim based crime data. The data do not represent statistics submitted to the FBI's Uniform Crime Report (UCR); therefore any comparisons are strictly prohibited. For further clarification of UCR data, please visit http://www.fbi.gov/about-us/cjis/ucr/ucr. Please note that this data is preliminary and subject to change. Prior month data is likely to show changes when it is refreshed on a monthly basis. All data is geocoded to the approximate latitude/longitude location of the incident and excludes those records for which an address could not be geocoded. Any attempt to match the approximate location of the incident to an exact address is strictly prohibited.

The Division of Criminal Justice Services (DCJS) collects crime reports from more than 500 New York State police and sheriffs' departments. DCJS compiles these reports as New York's official crime statistics and submits them to the FBI under the National Uniform Crime Reporting (UCR) Program. UCR uses standard offense definitions to count crime in localities across America regardless of variations in crime laws from state to state. In New York State, law enforcement agencies use the UCR system to report their monthly crime totals to DCJS. The UCR reporting system collects information on seven crimes classified as Index offenses which are most commonly used to gauge overall crime volume. These include the violent crimes of murder/non-negligent manslaughter, forcible rape, robbery, and aggravated assault; and the property crimes of burglary, larceny, and motor vehicle theft. Police agencies may experience reporting problems that preclude accurate or complete reporting. The counts represent only crimes reported to the police but not total crimes that occurred. DCJS posts preliminary data in the spring and final data in the fall.

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 (2016). Offenses occurring at intersections are represented at the X Coordinate and Y Coordinate of the intersection. Crimes occurring anywhere other than an intersection are geo-located to the middle of the block. For additional details, please see the attached data dictionary in the ‘About’ section.

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 (2019). For additional details, please see the attached data dictionary in the ‘About’ section.

All BPD data on Open Baltimore is preliminary data and subject to change. The information presented through Open Baltimore represents Part I victim based crime data. The data do not represent statistics submitted to the FBI's Uniform Crime Report (UCR); therefore any comparisons are strictly prohibited. For further clarification of UCR data, please visit http://www.fbi.gov/about-us/cjis/ucr/ucr. Please note that this data is preliminary and subject to change. Prior month data is likely to show changes when it is refreshed on a monthly basis. All data is geocoded to the approximate latitude/longitude location of the incident and excludes those records for which an address could not be geocoded. Any attempt to match the approximate location of the incident to an exact address is strictly prohibited.

List of every criminal summons issued in NYC during the current calendar year.

This is a breakdown of every criminal summons issued in NYC by the NYPD during the current calendar year. This data is manually extracted every quarter and reviewed by the Office of Management Analysis and Planning before being posted on the NYPD website. Each record represents a criminal summons issued in NYC by the NYPD and includes information about the type of crime, the location and time of enforcement. In addition, information related to suspect demographics is also included. This data can be used by the public to explore the nature of police enforcement activity. Please refer to the attached data footnotes for additional information about this dataset.

This study examined the ways in which the model of the Kings County Felony Domestic Violence Court (FDVC) changed the way cases were processed and adjudicated, the impact of this approach on outcomes, and its effects on recidivism. In order to evaluate the implementation and effectiveness of the FDVC, the researchers selected three samples of cases for collection of detailed data and comparisons on case characteristics, processing, and outcomes. First, felony domestic violence cases indicted from 1995 to early 1996 before the FDVC was established, and adjudicated by various parts of the state Supreme Court were studied. These pre-FDVC cases provided a comparison group for assessing differences associated with the FDVC model. Very few of these cases had felony protection order violations as the sole or top indictment charge, since they predated the implementation of the expanded criminal contempt law that went into effect in September 1996. Second, a sample of cases adjudicated by FDVC in its early period (the first half of 1997, after the model was fully implemented) and similar in indictment charges to the pre-FDVC cases was selected. These were cases that had indictment charges other than, or in addition to, felony criminal contempt charges for protection order violations. In other words, these were cases that would have been indicted and adjudicated in the state Supreme Court even without application of the September 1996 law. Third, because the September 1996 law felonizing many protection order violations (under criminal contempt statutes) broadened the types of cases handled by the Supreme Court, compared with those handled in the Supreme Court prior to this law, an additional sample of cases adjudicated by the FDVC (beginning in the first half of 1997) was selected. This was a small sample in which felony protection order violations were the only indicted felony charges. These cases would not have been indicted on felonies during the pre-FDVC period, and so would have remained in the criminal courts as misdemeanors. The inclusion of this sample allowed the researchers to assess how the protection order violation cases were different from the general population of FDVC cases, and how they might be handled differently by the Court and partner agencies. These cases were designated "CC-only" because their only felony indictment was for criminal contempt, the law under which felony protection order violations were charged. Variables in Part 1, Recidivism Data, contain information on number of appearance warrants issued, days incarcerated for predisposition, number of appearances for predisposition and post-disposition, bail conditions (i.e., batterer treatment or drug treatment), top charge at arrest, indictment, and disposition, indications of defendant's substance abuse of alcohol, marijuana, or other drugs, and psychological problems, types of disposition and probation, months of incarceration, sentence conditions, history of abuse by defendant against the victim, length of abuse in months, history of physical assault and sexual abuse, past weapon use, and medical attention needed for past domestic violence. Additional variables focus on whether an order of protection was issued before the current incident, whether the defendant was arrested for past domestic violence with this victim, total number of known victims, weapon used during incident, injury during the incident, medical attention sought, number of final orders of protection, whether the defendant was jailed throughout the pending case, number of releases during the case, number of reincarcerations after release, whether the victim lived with the defendant, whether the victim lived with children in common with the defendant, relationship between the victim and the defendant, number of months the victim had known the defendant, number of children in common with the defendant, whether the victim attempted to drop charges, whether the victim testified at trial, whether a victim advocate was assigned, total violations during pending case, predisposition violations, and number of probation violations. Demographic variables in Part 1 include defendant and victims' gender, race, victim age at defendant's arrest, defendant's income, employment status, and education. Variables in Part 2, Top Charge Data, relating to the defendant include number and types of prior arrests and convictions, top charge at arrest, severity of top charge at arrest, top charge at grand jury indictment, severity of top charge indictment, disposition details, Uniform Crime Reporting (UCR) arrest indicators, child victim conviction indicator, drug conviction indicator, weapon conviction indicator, types of probation, sentence, disposition, and offenses. Demographic variables in Part 2 include sex and race of the defendant.

The researchers sought to add to the incipient literature on randomized studies of batterer treatment, by conducting an experimental study that compared batterers assigned to treatment to batterers assigned to a community service program irrelevant to the problem of violence. The study was conducted using a true experimental design and consisted of 376 spousal assault cases drawn from the Kings County (New York) Criminal Court which were adjudicated between February 19, 1995, and March 1, 1996. Batterers were mandated to attend a 40-hour batterer treatment program or to complete 40 hours of community service. The random assignment was made at sentencing, after all parties (judge, prosecutor, and defense) had agreed that batterer treatment was appropriate, the defendant agreed to treatment and was accepted by the Alternatives to Violence (ATV) program, and the program was available based on the random assignment process. Interviews were also conducted with both the batterer and the victim at sentencing as well as 6 months post-sentence and 12 months post-sentence. These interviews collected data in areas regarding demographics (first interview only), recidivism, beliefs about domestic violence, conflict management strategies, locus of control, and for victims, self esteem. Administrative records were also used to obtain data regarding any new crimes committed.

In 2023, the City of New York experienced a total of 1,455 rapes. This was a significant decrease from 2001 when 1,981 rapes were reported. These figures include all crimes as defined in the FBI Uniform Crime Reporting definition of rape.

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.The study examined four research questions: (1) Was the Red Hook Community Justice Center (RHCJC) implemented according to plan?; (2) Did RHCJC make a difference in sanctioning, recidivism, and arrests?; (3) How did RHCJC produce any observed reductions to recidivism and arrests?; and (4) Is RHCJC cost-efficient from the viewpoint of taxpayers? The community survey (Red Hook Resident Data, n = 95) was administered by research teams in the spring and summer of 2010. Teams generally went house-to-house ringing apartment buzzers at varying times of day, usually on the weekend when working people are more likely to be home or approached people on the sitting on park benches to conduct interviews.In autumn 2010, the research team administered a survey to 200 misdemeanor offenders (Red Hook Offender Data, n = 205) who were recruited from within the catchment area of the Red Hook Community Justice Center (RHCJC) using Respondent Driven Sampling (RDS).To examine how the RHCJC was implemented (Red Hook Process Evaluation Data, n= 35,465 and Red Hook Work File Data, n= 3,127), the research team relied on a diverse range of data sources, including 52 structured group and individual interviews with court staff and stakeholders carried out over five site visits; observation of courtroom activities and staff meetings; extensive document review; and analysis of case-level data including all adult criminal cases and some juvenile delinquency cases processed at the Justice Center from 2000 through 2009. To aid in understanding the RHCJC's impact on the overall level of crime in the catchment area, researchers obtained monthly counts (Arrest Data, n = 144) of felony and misdemeanor arrests in each of the three catchment area police precincts (the 72nd, 76th, and 78th precincts).

This study examined the relationship between legal pressure and drug treatment retention by assessing perceptions of legal pressure held by two groups of legally-mandated treatment clients: (1) participants of the Drug Treatment Alternative to Prison (DTAP) program operated by the Kings County (Brooklyn) District Attorney in New York City, and (2) a matched group of probationers, parolees, Treatment Alternatives to Street Crime (TASC) participants, and other court-mandated offenders attending the same community-based treatment programs used by DTAP. The Brooklyn DTAP was selected for study because of the program's uniquely coercive program components, including the threat of a mandatory prison term for noncompliance. The goals of this project were (1) to test whether DTAP participants would show significantly higher retention rates when compared to a matched sample of other legally-mandated treatment clients, and (2) to assess the role of perceived legal pressure in predicting retention for both of these groups. Data were collected from program participants through interviews conducted at admission to treatment and follow-up interviews conducted about eight weeks later. Intake interviews were conducted, on average, one week after the client's admission to treatment. The one-to-one interviews, which lasted up to two hours, were administered by trained researchers in a private location at the treatment site. The intake interview battery included a mixture of standardized measures and those developed by the Vera Institute of Justice. Data in Part 1 were collected with the Addiction Severity Index and include age, sex, race, religion, and education. Additional variables cover medical problems, employment history, detailed substance abuse and treatment history, number of times arrested for various crimes, history of incarceration, family's substance abuse and criminal histories, relationships with family and friends, psychological problems such as depression, anxiety, and suicide, current living arrangements, and sources of income. Part 2, Supplemental Background and Retention Data, contains treatment entry date, number of days in treatment, age at treatment entry, termination date, treatment condition, arrest date, detention at arrest, date released on probation/parole, violation of probation/parole arrest date and location, problem drug, prior drug treatment, as well as age, gender, race, education, and marital status. Part 3, Division of Criminal Justice Services Data, includes data on the number of arrests before and after program entry, and number of total misdemeanor and felony arrests, convictions, and sentences. Part 4, Chemical Use, Abuse, and Dependence Data, contains information on type of substance abuse, intoxication or withdrawal at work, school, or home, effects of abuse on social, occupational, or recreational activities, and effects of abuse on relationships, health, emotions, and employment. Parts 5 and 6 contain psychiatric data gathered from the Symptom Checklist-90-Revised and Beck's Depression Inventory, respectively. Part 7 variables from the Circumstances, Motivation, Readiness, and Suitability scale include family's attitude toward treatment, subject's need for treatment, subject's desire to change life, and legal consequences if subject did not participate in treatment. Part 8, Stages of Change Readiness and Treatment Eagerness scale, contains data on how the subject viewed the drug problem, desire to change, and history of dealing with substance abuse. Part 9, Motivational/Program Supplement Data, includes variables on the subject's need for treatment, attitudes toward treatment sessions, the family's reaction to treatment, and a likelihood of completion rating. Part 10, Perceived Legal Coercion Data, gathered information on who referred the subject to the treatment program, who was keeping track of attendance, whether someone explained the rules of participation in the program and the consequences if the subject failed the program, whether the rules and consequences were put in writing, who monitored program participants, the likelihood of using drugs while in treatment, the likelihood of leaving the program before completion, whether the subject understood the legal consequences of failing the program, the type and frequency of reports and contacts with the criminal justice system, and the subject's reaction to various penalties for not completing the program. Part 11 contains data from the Community Oriented Programs Environment Scale (COPES). Part 12, Treatment Services Review Data, includes data on the number of times the subject received medical attention, days in school, days employed, days intoxicated, days in substance abuse treatment, days tested for drugs, number of contacts with the criminal justice system, days treated for psychological problems, and time spent at recreational activities. Additional variables include the number of individual and group treatment sessions spent discussing medical problems, education and employment, substance abuse, legal problems, and psychological and emotional problems.

Since 1998, the New York City Police Department (NYPD) has been tasked with the collection and maintenance of crime data for incidents that occur in New York City public schools. The NYPD has provided this data for the 2010-2011 school year to the New York City Department of Education (DOE), covering the period from July 1, 2010-June 30, 2011. The DOE has compiled this data by schools and locations for the information of our parents and students, our teachers and staff, and the general public. In some instances, several Department of Education learning communities co-exist within a single building. In other instances, a single school has locations in several different buildings. In either of these instances, the data presented here is aggregated by building location rather than by school, since safety is always a building-wide issue. We use “consolidated locations” throughout the presentation of the data to indicate the numbers of incidents in buildings that include more than one learning community.

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 contains data from a project by the New York City Police Department (NYPD) involving GIS data on environmental risk factors that correlate with criminal behavior. The general goal of this project was to test whether risk terrain modeling (RTM) could accurately and effectively predict different crime types occurring across New York City. The ultimate aim was to build an enforcement prediction model to test strategies for effectiveness before deploying resources. Three separate phases were completed to assess the effectiveness and applicability of RTM to New York City and the NYPD. A total of four boroughs (Manhattan, Brooklyn, the Bronx, Queens), four patrol boroughs (Brooklyn North, Brooklyn South, Queens North, Queens South), and four precincts (24th, 44th, 73rd, 110th) were examined in 6-month time periods between 2014 and 2015. Across each time period, a total of three different crime types were analyzed: street robberies, felony assaults, and shootings. The study includes three shapefiles relating to New York City Boundaries, four shapefiles relating to criminal offenses, and 40 shapefiles relating to risk factors.

This data collection is one of three quantitative databases comprising the Commercial Theft Studies component of the Study of the Causes of Crime for Gain, which focuses on patterns of commercial theft and characteristics of commercial thieves. This data collection contains information on methods used to commit commericial thefts involving cargo. The data include incident and missing cargo characteristics, suspect characteristics and punishments, and type and value of stolen property. Cargo thefts that occurred at John F. Kennedy International Airport, LaGuardia Airport, Newark International Airport, and the New York Marine Terminals at Brooklyn, Port Elizabeth, and Port Newark were included in the data, which were collected from the Crime Analysis Unit files of the Port Authorities of New York and New Jersey.