Description: Pursuant to the Sex Offender and Child Murderer Community Notification Law, 730 ILCS 152/101,et seq., the Chicago Police Department maintains a list of sex offenders residing in the City of Chicago who are required to register under the Sex Offender Registration Act, 730 ILCS 150/2, et seq. To protect the privacy of the individuals, addresses are shown at the block level only and specific locations are not identified. The data are extracted from the CLEAR (Citizen Law Enforcement Analysis and Reporting) system developed by the Department. Although every effort is made to keep this list accurate and current, the city cannot guarantee the accuracy of this information. Offenders may have moved and failed to notify the Chicago Police Department as required by law. If any information presented in this web site is known to be outdated, please contact the Chicago Police Department at srwbmstr@chicagopolice.org, or mail to Sex Registration Unit, 3510 S Michigan Ave, Chicago, IL 60653. Disclaimer: This registry is based upon the legislature's decision to facilitate access to publicly available information about persons convicted of specific sexual offenses. The Chicago Police Department has not considered or assessed the specific risk of re-offense with regard to any individual prior to his or her inclusion within this registry, and has made no determination that any individual included within the registry is currently dangerous. Individuals included within this registry are included solely by virtue of their conviction record and Illinois law. The main purpose of providing this data on the internet is to make the information more available and accessible, not to warn about any specific individual. Anyone who uses information contained in the Sex Offender Database to commit a criminal act against another person is subject to criminal prosecution. Data Owner: Chicago Police Department. Frequency: Data is updated daily. Related Applications: CLEARMAP (http://j.mp/lLluSa).

Sex Offender work and home locations, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. If users want to obtain more information about sex offenders, they should go to the Sex Offender Mapping Application (https://sexoffender.dc.gov/) and download the “More Details” PDF. Data provided by the Court Services and Offender Supervision Agency identified sex offender registry providing location at the block level. https://www.csosa.gov/.

Interactive map and search tools for display of high risk, delinquent, and lifetime sex offenders in North Dakota

Constraints:
Not to be used for navigation, for informational purposes only. See full disclaimer for more information

The purpose of the project was to (1) determine whether the combined dynamic (SOTIPS) and static risk assessment (Static-99R) tools better predicted sexual recidivism than either alone, and (2) determine whether the tools could be implemented successfully in more representative populations. Previous research has established a "status quo" for risk assessments. This study was set within the context of the developing sexual offender risk prediction field, where investigators explored reliable and valid means to assess what have been termed "dynamic risk factors." Instruments that identify the specific psychological risk factors present in the individual offender ought to allow treatment for that individual to be tailored to these specific needs, thus increasing its effectiveness. Thus, instruments have been designed to: Assess psychological factors that are empirically related to sexual recidivism, thus creating a basis for selecting treatment targets Show robust incremental predictive validity relative to Static-99R or other measures of static risk factors Measure change in a way that is convincingly related to sexual recidivism Incorporate and point risk managers towards some of the factors identified in the desistance literature Improve the effectiveness of treatment in reducing sexual recidivism Enrollment of sex offenders in the evaluation study began in April 2013. To be included, offenders needed to be Static-99R eligible (an adult male convicted of a contact or non-contact sex offense with an identifiable victim), mentally cognizant, released to community supervision, and at least 18 years old in January 2013 in Maricopa County and April 2013 in New York City.

The UNIFORM CRIME REPORTING PROGRAM DATA: OFFENSES KNOWN AND CLEARANCES BY ARREST, 2020 dataset is a compilation of offenses reported to law enforcement agencies in the United States. Due to the vast number of categories of crime committed in the United States, the FBI has limited the type of crimes included in this compilation to those crimes which people are most likely to report to police and those crimes which occur frequently enough to be analyzed across time. Crimes included are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. Much information about these crimes is provided in this dataset. The number of times an offense has been reported, the number of reported offenses that have been cleared by arrests, and the number of cleared offenses which involved offenders under the age of 18 are the major items of information collected.

In 2023, Texas had the highest number of forcible rape cases in the United States, with 15,097 reported rapes. Delaware had the lowest number of reported forcible rape cases at 194. Number vs. rate It is perhaps unsurprising that Texas and California reported the highest number of rapes, as these states have the highest population of states in the U.S. When looking at the rape rate, or the number of rapes per 100,000 of the population, a very different picture is painted: Alaska was the state with the highest rape rate in the country in 2023, with California ranking as 30th in the nation. The prevalence of rape Rape and sexual assault are notorious for being underreported crimes, which means that the prevalence of sex crimes is likely much higher than what is reported. Additionally, more than a third of women worry about being sexually assaulted, and most sexual assaults are perpetrated by someone the victim knew.

This project undertook the systematic collection of first-generation data concerning the nature, extent, and seriousness of child sexual exploitation (CSE) in the United States. The project was organized around the following research objectives: (1) identification of the nature, extent, and underlying causes of CSE and the commercial sexual exploitation of children (CSEC) occurring in the United States, (2) identification of those subgroups of children that were at the greatest risk of being sexually exploited, (3) identification of subgroups of adult perpetrators of sex crimes against children, and (4) identification of the modes of operation and other methods used by organized criminal units to recruit children into sexually exploitative activities. The study involved surveying senior staff members of nongovernment organizations (NGOs) and government organizations (GOs) in the United States known to be dealing with persons involved in the transnational trafficking of children for sexual purposes. Part 1 consists of survey data from nongovernment organizations. These were local child and family agencies serving runaway and homeless youth. Part 2 consists of survey data from government organizations. These organizations were divided into local, state, and federal agencies. Local organizations included municipal law enforcement, county law enforcement, prosecutors, public defenders, and corrections. State organizations included state child welfare directors, prosecutors, and public defenders. Federal organizations included the Federal Bureau of Investigation, Federal Public Defenders, Immigration and Naturalization Service, United States Attorneys, United States Customs, and the United States Postal Service. Variables in Parts 1 and 2 include the organization's city, state, and ZIP code, the type of services provided or type of law enforcement agency, how the agency was funded, the scope of the agency's service area, how much emphasis was placed on CSEC as a policy issue or a service issue, conditions that might influence the number of CSEC cases, how staff were trained to deal with CSEC cases, how victims were identified, the number of children that experienced child abuse, sexual abuse, pornography, or other exploitation in 1999 and 2000 by age and gender, methods of recruitment, family history of victims, gang involvement, and substance abuse history of victims.

The National Crime Victimization Survey (NCVS), previously called the National Crime Survey (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. Beginning in 1992, the survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual assault, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations. This dataset represents the concatenated version of the NCVS on a collection year basis for 1992-2023. A collection year contains records from interviews conducted in the 12 months of the given year. Under the collection year format, victimizations are counted in the year the interview is conducted, regardless of the year when the crime incident occurred.For additional information on the dataset, please see the documentation for the data from the most current year of the NCVS, ICPSR Study 38962.

Since 2001, Vermont Department of Corrections (DOC) sex offender treatment providers and probation and parole officers have scored every adult male sex offender under community supervision on three measures of static (unchangeable) risk at intake (i.e., Static-99, RRASOR-Rapid Risk Assessment for Sex Offence Recidivism, and VASOR-Vermont Assessment of Sex Offender Risk) and one measure of dynamic (changeable) risk (i.e, SOTNPS) at intake and then every six months thereafter. This project conducted record reviews to verify the accuracy of the risk assessment scores and examined how scores on the Sex Offender Treatment Needs and Progress Scale (SOTNPS) and one or more of these static risk instruments can be combined into an overall model of risk assessment. An empirically derived decision-making model was created to assist correctional administrators, probation and parole officers, and treatment providers in allocating and delivering supervision and treatment services based on an individual's treatment needs and risk to sexually re-offend.

Three hypotheses were tested. First, it was expected that one or more static risk measures (Static-99R, Static-2002R and VASOR) would predict sexual recidivism with moderate accuracy in the sample. Second, a dynamic risk measure, Sex Offender Treatment Needs and Progress Scale (SOTNPS), or a subset of risk factors contained in this measure would also predict sexual recidivism with moderate accuracy and be sensitive to the changes in dynamic risk over time. Third, a combined static and dynamic risk measure would predict sexual recidivism more accurate than either measure alone.

Version 5 release notes:
Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.
Version 4 release notes:
Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
Version 3 release notes:
Add 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 Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.
All the data was downloaded from NACJD 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. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998.

To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns.

To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros.

I created 9 arrest categories myself. The categories are:
Total Male JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the data are just agency identifier columns). Because this "simple" data set need fewer columns, I include all offenses.

As the arrest data is very granular, and each category of arrest is its own column, there are dozens of columns per crime. To keep the data somewhat manageable, there are nine different files, eight which contain different crimes and the "simple" file. Each file contains the data for all years. The eight categories each have crimes belonging to a major crime category and do not overlap in crimes other than with the index offenses. Please note that the crime names provided below are not the same as the column names in the data. Due to Stata limiting column names to 32 characters maximum, I have abbreviated the crime names in the data. The files and their included crimes are:

Index Crimes
MurderRapeRobberyAggravated AssaultBurglaryTheftMotor Vehicle TheftArsonAlcohol CrimesDUIDrunkenness
LiquorDrug CrimesTotal DrugTotal Drug SalesTotal Drug PossessionCannabis PossessionCannabis SalesHeroin or Cocaine PossessionHeroin or Cocaine SalesOther Drug PossessionOther Drug SalesSynthetic Narcotic PossessionSynthetic Narcotic SalesGrey Collar and Property CrimesForgeryFraudStolen PropertyFinancial CrimesEmbezzlementTotal GamblingOther GamblingBookmakingNumbers LotterySex or Family CrimesOffenses Against the Family and Children
Other Sex Offenses
ProstitutionRapeViolent CrimesAggravated AssaultMurderNegligent ManslaughterRobberyWeapon Offenses
Other CrimesCurfewDisorderly ConductOther Non-trafficSuspicion
VandalismVagrancy
Simple
This data set has every crime and only the arrest categories that I created (see above).
If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

***Starting on March 7th, 2024, the Los Angeles Police Department (LAPD) will adopt a new Records Management System for reporting crimes and arrests. This new system is being implemented to comply with the FBI's mandate to collect NIBRS-only data (NIBRS — FBI - https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs). During this transition, users will temporarily see only incidents reported in the retiring system. However, the LAPD is actively working on generating new NIBRS datasets to ensure a smoother and more efficient reporting system. ***

******Update 1/18/2024 - LAPD is facing issues with posting the Crime data, but we are taking immediate action to resolve the problem. We understand the importance of providing reliable and up-to-date information and are committed to delivering it.

As we work through the issues, we have temporarily reduced our updates from weekly to bi-weekly to ensure that we provide accurate information. Our team is actively working to identify and resolve these issues promptly.

We apologize for any inconvenience this may cause and appreciate your understanding. Rest assured, we are doing everything we can to fix the problem and get back to providing weekly updates as soon as possible. ******

This dataset reflects incidents of crime in the City of Los Angeles dating back to 2020. This data is transcribed from original crime reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). In the late 1970s, the law enforcement community called for a thorough evaluative study of the UCR with the objective of recommending an expanded and enhanced UCR program to meet law enforcement needs into the 21st century. The FBI fully concurred with the need for an updated program to meet contemporary needs and provided its support, formulating a comprehensive redesign effort. Following a multiyear study, a "Blueprint for the Future of the Uniform Crime Reporting Program" was developed. Using the "Blueprint," and in consultation with local and state law enforcement executives, the FBI formulated new guidelines for the Uniform Crime Reports. The National Incident-Based Reporting System (NIBRS) was implemented to meet these guidelines. NIBRS data as formatted by the FBI are stored in a single file. These data are organized by various segment levels (record types). There are six main segment levels: administrative, offense, property, victim, offender, and arrestee. Each segment level has a different length and layout. There are other segment levels which occur with less frequency than the six main levels. Significant computing resources are necessary to work with the data in its single-file format. In addition, the user must be sophisticated in working with data in complex file types. While it is convenient to think of NIBRS as a hierarchical file, its structure is more similar to a relational database in that there are key variables that link the different segment levels together. NIBRS data are archived at ICPSR as 11 separate data files per year, which may be merged by using linkage variables. Prior to 2013 the data were archived and distributed as 13 separate data files, including three separate batch header record files. Starting with the 2013 data, the FBI combined the three batch header files into one file. Consequently, ICPSR instituted new file numbering for the data. NIBRS data focus on a variety of aspects of a crime incident. Part 2 (formerly Part 4), Administrative Segment, offers data on the incident itself (date and time). Each crime incident is delineated by one administrative segment record. Also provided are Part 3 (formerly Part 5), Offense Segment (offense type, location, weapon use, and bias motivation), Part 4 (formerly Part 6), Property Segment (type of property loss, property description, property value, drug type and quantity), Part 5 (formerly Part 7), Victim Segment (age, sex, race, ethnicity, and injuries), Part 6 (formerly Part 8), Offender Segment (age, sex, and race), and Part 7 (formerly Part 9), Arrestee Segment (arrest date, age, sex, race, and weapon use). The Batch Header Segment (Part 1, formerly Parts 1-3) separates and identifies individual police agencies by Originating Agency Identifier (ORI). Batch Header information, which is contained on three records for each ORI, includes agency name, geographic location, and population of the area. Part 8 (formerly Part 10), Group B Arrest Report Segment, includes arrestee data for Group B crimes. Window Segments files (Parts 9-11, formerly Parts 11-13) pertain to incidents for which the complete Group A Incident Report was not submitted to the FBI. In general, a Window Segment record will be generated if the incident occurred prior to January 1 of the previous year or if the incident occurred prior to when the agency started NIBRS reporting. As with the UCR, participation in NIBRS is voluntary on the part of law enforcement agencies. The data are not a representative sample of crime in the United States. Recognizing many differences in computing resources and that many users will be interested in only one or two segment levels, ICPSR has decided to make the data available as multiple files. Each NIBRS segment level in the FBI's single-file format has been made into a separate rectangular ASCII data file. Linkage (key) variables are used to perform analyses that involve two or more segment levels. If the user is interested in variables contained in one segment level, then the data are easy to work with since each segment level file is simply a rectangular ASCII data file. Setup files are available to read each segment level. Also, with only one segment level, the issue of

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 15 release notes:Adds 2021 data.Version 14 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Arrests by Age, Sex, and Race data they release. Version 13 release notes:Changes R files from .rda to .rds.Fixes bug where the number_of_months_reported variable incorrectly was the largest of the number of months reported for a specific crime variable. For example, if theft was reported Jan-June and robbery was reported July-December in an agency, in total there were 12 months reported. But since each crime (and let's assume no other crime was reported more than 6 months of the year) only was reported 6 months, the number_of_months_reported variable was incorrectly set at 6 months. Now it is the total number of months reported of any crime. So it would be set to 12 months in this example. Thank you to Nick Eubank for alerting me to this issue.Adds rows even when a agency reported zero arrests that month; all arrest values are set to zero for these rows.Version 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.Version 5 release notes: Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.Version 4 release notes: Changes column names from "p

The National Crime Survey (NCS), a study of personal and household victimization, measures victimization for six selected crimes, including attempts. The NCS was designed to achieve three primary objectives: to develop detailed information about the victims and consequences of crime, to estimate the number and types of crimes not reported to police, and to provide uniform measures of selected types of crime. The surveys cover the following types of crimes, including attempts: rape, robbery, assault, burglary, larceny, and auto or motor vehicle theft. Crimes such as murder, kidnapping, shoplifting, and gambling are not covered. Questions designed to obtain data on the characteristics and circumstances of the victimization were asked in each incident report. Items such as time and place of occurrence, injuries suffered, medical expenses incurred, number, age, race, and sex of offender(s), relationship of offender(s) to victim (stranger, casual acquaintance, relative, etc.), and other detailed data relevant to a complete description of the incident were included. Legal and technical terms, such as assault and larceny, were avoided during the interviews. Incidents were later classified in more technical terms based upon the presence or absence of certain elements. In addition, data were collected in the study to obtain information on the victims' education, migration, labor force status, occupation, and income. Full data for each year are contained in Parts 101-110. Incident-level extract files (Parts 1-10, 41) are available to provide users with files that are easy to manipulate. The incident-level datasets contain each incident record that appears in the full sample file, the victim's person record, and the victim's household information. These data include person and household information for incidents only. Subsetted person-level files also are available as Parts 50-79. All of the variables for victims are repeated for a maximum of four incidents per victim. There is one person-level subset file for each interview quarter of the complete national sample from 1973 through the second interview quarter in 1980.

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