In the United States between 2005 and 2020, of the ** nonfederal police officers convicted following their arrest for murder due to an on-duty shooting, only **** ended up being convicted of murder. The most common offense these officers were convicted of was the lesser charge of manslaughter, with ** convictions.

As of July 2022, three charges were filed against police officers who had killed a fleeing suspect in the United States, and no convictions. As of that date, *** people who were fleeing were killed by police.

Each record in this dataset shows information about an arrest executed by the Chicago Police Department (CPD). Source data comes from the CPD Automated Arrest application. This electronic application is part of the CPD CLEAR (Citizen Law Enforcement Analysis and Reporting) system, and is used to process arrests Department-wide.

A more-detailed version of this dataset is available to media by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Arrests Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup. New data fields may be added to this public dataset in the future. Requests for individual arrest reports or any other related data other than access to the more-detailed dataset should be directed to CPD, through contact information on that site or a Freedom of Information Act (FOIA) request.

The data is limited to adult arrests, defined as any arrest where the arrestee was 18 years of age or older on the date of arrest. The data excludes arrest records expunged by CPD pursuant to the Illinois Criminal Identification Act (20 ILCS 2630/5.2).

Department members use charges that appear in Illinois Compiled Statutes or Municipal Code of Chicago. Arrestees may be charged with multiple offenses from these sources. Each record in the dataset includes up to four charges, ordered by severity and with CHARGE1 as the most severe charge. Severity is defined based on charge class and charge type, criteria that are routinely used by Illinois court systems to determine penalties for conviction. In case of a tie, charges are presented in the order that the arresting officer listed the charges on the arrest report. By policy, Department members are provided general instructions to emphasize seriousness of the offense when ordering charges on an arrest report.

Each record has an additional set of columns where a charge characteristic (statute, description, type, or class) for all four charges, or fewer if there were not four charges, is concatenated with the | character. These columns can be used with the Filter function's "Contains" operator to find all records where a value appears, without having to search four separate columns.

Users interested in learning more about CPD arrest processes can review current directives, using the CPD Automated Directives system (http://directives.chicagopolice.org/directives/). Relevant directives include:

• Special Order S06-01-11 – CLEAR Automated Arrest System: describes the application used by Department members to enter arrest data. • Special Order S06-01-04 – Arrestee Identification Process: describes processes related to obtaining and using CB numbers. • Special Order S09-03-04 – Assignment and Processing of Records Division Numbers: describes processes related to obtaining and using RD numbers. • Special Order 06-01 – Processing Persons Under Department Control: describes required tasks associated with arrestee processing, include the requirement that Department members order charges based on severity.

Since 2014, Eurostat and the UNODC have launched a joint annual data collection on crime and criminal justice statistics, using the UN crime trends questionnaire and complementary Eurostat requests

for specific areas of interest to the European Commission. The data and metadata are collected from National Statistical Institutes or other relevant authorities (mainly police and justice departments) in each EU Member State, EFTA country and EU potential members. On the Eurostat website, data are available for 41 jurisdictions since 2008 until 2018 data and for 38 jurisdictions since 2019 data (EU-27, Iceland, Liechtenstein, Norway, Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia, Turkey, Kosovo(1)), having drop the data for the United Kingdom separately owing to three separate jurisdictions England and Wales, Scotland, Northern Ireland.

This joint data collection and other data collections carried out by Eurostat allows to gather information on:

• police-recorded offences by type of crime
• police-recorded offences by NUTS3 region
• intentional homicide and sexual violence victims and perpetrators (suspected, prosecuted, convicted) by sex
• intentional homicide victims by age, sex, and relationship to the offender
• intentional homicide victims and offences in largest cities
• offenders by justice legal status (suspected, prosecuted, convicted), age, sex, and citizenship
• persons brought before criminal courts by legal status (convicted persons/acquitted)
• personnel by institution (police, courts, and prisons) by sex
• legal cases in first instance courts by type and stage
• prisoners by age, sex, citizenship, and status of the trial process
• prison capacity and occupancy
• people involved in human trafficking by legal status (victims, suspected and convicted traffickers) and victims of human trafficking by all forms of exploitation and citizenship

Where available, data are broken down by sex, age groups (adults/juveniles), country of citizenship (foreigners or nationals) and other relevant variables. National data are available and for intentional homicide offences, city level data (largest cities) are available for some countries. Regional data at NUTS3 level are also available for some police-recorded offences.

Some historical series are available:

• Number of police-recorded crimes by type (intentional homicide, violence, robbery, home burglary, car thefts, and drug crimes) for the period 1993 – 2007
• Number of police-recorded homicide in cities for the period 1993 – 2007
• Number of police officers for the period 1993 – 2007
• Prison population for the period 1993 – 2007

Total number of police-recorded crimes for the period 1950 – 2000

(1) under United Nations Security Council Resolution 1244/99

The number of people killed by police while fleeing has risen since 2015. In 2015, there were *** people killed by police while they were fleeing, which increased to *** people in 2021. As of *********, *** people who were fleeing were killed by police. Despite this, criminal convictions for police officers who kill fleeing individuals remain low.

This study used a mixed-methods approach to pursue five interrelated objectives: (1) to document the extent of case attrition and to identify the stages of the criminal justice process where attrition is most likely to occur; (2) to identify the case complexities and evidentiary factors that affect the likelihood of attrition in sexual assault cases; (3) to identify the predictors of case outcomes in sexual assault cases; (4) to provide a comprehensive analysis of the factors that lead police to unfound the charges in sexual assault cases; and (5) to identify the situations in which sexual assault cases are being cleared by exceptional means. Toward this end, three primary data sources were used: (1) quantitative data on the outcomes of sexual assaults reported to the Los Angeles Police Department (LAPD) and the Los Angeles County Sheriff's Department (LASD) from 2005 to 2009, (2) qualitative data from interviews with detectives and with deputy district attorneys with the Los Angeles District Attorney's Office who handled sexual assault cases during this time period, and (3) detailed quantitative and qualitative data from case files for a sample of cases reported to the two agencies in 2008. The complete case files for sexual assaults that were reported to the Los Angeles Police Department and the Los Angeles County Sheriff's Department in 2008 were obtained by members of the research team and very detailed information (quantitative and qualitative data) was extracted from the files on each case in Dataset 1 (Case Outcomes and Characteristics: Reports from 2008). The case file included the crime report prepared by the patrol officer who responded to the crime and took the initial report from the complainant, all follow-up reports prepared by the detective to whom the case was assigned for investigation, and the detective's reasons for unfounding the report or for clearing the case by arrest or by exceptional means. The case files also included either verbatim accounts or summaries of statements made by the complainant, by witnesses (if any), and by the suspect (if the suspect was interviewed); a description of physical evidence recovered from the alleged crime scene, and the results of the physical exam (Sexual Assault Response Team (SART) exam) of the victim (if the victim reported the crime within 72 hours of the alleged assault). Members of the research team read through each case file and recorded data in an SPSS data file. There are 650 cases and 261 variables in the data file. The variables in the data file include administrative police information and charges listed on the police report. There is also information related to the victim, the suspect, and the case. Datasets 2-5 were obtained from the district attorney's office and contain outcome data that resulted in the arrest of a suspect. The outcome data obtained from the agency was for the following sex crimes: rape, attempted rape, sexual penetration with a foreign object, oral copulation, sodomy, unlawful sex, and sexual battery. Dataset 3 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - Adult Arrests) is a subset of Dataset 2 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - All Cases) in that it only contains cases that resulted in the arrest of at least one adult suspect. Dataset 2 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - All Cases) contains 10,832 cases and 29 variables. Dataset 3 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Police Department - Adult Arrests) contains 891 cases and 45 variables. Similarly, Dataset 5 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - Adult Arrests) is a subset of Dataset 4 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - All Cases) in that it only contains cases that resulted in the arrest of at least one adult suspect. Dataset 4 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - All Cases) contains 3,309 cases and 33 variables. Dataset 5 (Sexual Assault Case Attrition: 2005 to 2009, Los Angeles Sheriff's Department - Adult Arrests) contains 904 cases and 47 variables.

Abstract The aim of this paper is to identify the factors associated with the performance of police internal affairs divisions. Little is known about the performance determinants of organizations crafted to prevent deviant behavior of civil servants. To reach our goal, we analyzed 797 administrative proceedings against 1.195 military police of a given Brazilian state between 2005 and 2012. Our quantitative analysis demonstrated that cases involving more experienced police officers and crimes with increased complexity are less likely to be concluded. Our results also suggest that social norms and internal codes inherent to police forces affects the extent of punishment to implicated officers: some crimes seem to be tolerated while others are abhorred by the police force. Surprisingly, an increased centralization in the investigative activity is not associated with increased efficiency of the investigation process. Actually, investigation commissions formed by police officers specialized in investigating their own peers are less likely to have their cases concluded or to condemn police officers. Our results contribute to theoretical debates on the design of watchdog organizations and the literature on organizational performance of accountability bodies.

Related Tables / Normalized VersionThis dataset provides demographic information related to arrests made by the Tempe Police Department. Demographic fields include race and ethnicity, age range at the time of arrest, and gender for each party. The data is sourced from the Police Department’s Records Management System (RMS) and supports analysis of patterns related to arrests, enforcement activity, and demographic trends over time. This information is a component of ongoing efforts to promote transparency and provide context for law enforcement within the community.For detailed guidance on interpreting arrest counts and demographic breakdowns, please refer to the User Guide: Understanding the Arrest Demographic Datasets - Related Tables.Why this Dataset is Organized this Way?The related tables such as persons, charges, and locations follow a normalized data model. This structure is often preferred by data professionals for more advanced analysis, filtering, or joining with external datasets.Providing this format supports a wide range of users, from casual data explorers to experienced analysts.Understanding the Arrests Data (as related tables)The related tables represent different parts of the arrest data. Each one focuses on a different type of information, like the officers, individuals arrested, charges, and arrest details.All of these tables connect back to the arrests table, which acts as the central record for each event. This structure is called a normalized model and is often used to manage data in a more efficient way. Visit the User Guide: Understanding the Arrest Demographic Datasets - Related Tables for more details outlining the relationships between the related tables.Data DictionaryAdditional InformationContact Email: PD_DataRequest@tempe.govContact Phone: N/ALink: N/AData Source: Versaterm RMSData Source Type: SQL ServerPreparation Method: Automated processPublish Frequency: DailyPublish Method: Automatic

A table listing recommended and final penalties for each officer with a substantiated complaint of misconduct since the year 2000. Non-charges cases go through the Department Advocate's Office (DAO) and are recorded as non-APU where relevant in the table, while charges cases are prosecuted by the Administrative Prosecution Unit (APU) and are marked as APU penalties and recommendations. In all cases the NYPD Commissioner will issue a final penalty, labeled as "NYPD Officer Penalty" in the dataset. The dataset is part of a database of all public police misconduct records the Civilian Complaint Review Board (CCRB) maintains on complaints against New York Police Department uniformed members of service received in CCRB's jurisdiction since the year 2000, when CCRB's database was first built. This data is published as four tables: Civilian Complaint Review Board: Police Officers Civilian Complaint Review Board: Complaints Against Police Officers Civilian Complaint Review Board: Allegations Against Police Officers Civilian Complaint Review Board: Penalties A single complaint can include multiple allegations, and those allegations may include multiple subject officers and multiple complainants. Public records exclude complaints and allegations that were closed as Mediated, Mediation Attempted, Administrative Closure, Conciliated (for some complaints prior to the year 2000), or closed as Other Possible Misconduct Noted. This database is inclusive of prior datasets held on Open Data (previously maintained as "Civilian Complaint Review Board (CCRB) - Complaints Received," "Civilian Complaint Review Board (CCRB) - Complaints Closed," and "Civilian Complaint Review Board (CCRB) - Allegations Closed") but includes information and records made public by the June 2020 repeal of New York Civil Rights law 50-a, which precipitated a full revision of what CCRB data could be considered public.

In 2022, acquittals for police officers convicted of brutality under Section 3 of Article 286 of the Criminal Code of Russia were issued in nearly five percent of cases. That was ** times more often than the share of such verdicts for all articles of the Criminal Code. Almost ** percent of the convicts from national enforcement authorities avoided a real sentence by being given a suspended sentence or fine.

This study was an outcome evaluation of the effects of the Duluth Domestic Abuse Intervention Project Training Model for Law Enforcement Response on police officer attitudes toward domestic violence. Data on the effectiveness of the training were collected by means of an attitude survey of law enforcement officers (Part 1). Additionally, two experimental designs (Part 2) were implemented to test the effects of the Duluth model training on (1) time spent by police officers at the scene of a domestic violence incident, and (2) the number of convictions. Variables for Part 1 include the assigned research group and respondents' level of agreement with various statements, such as: alcohol is the primary cause of family violence, men are more likely than women to be aggressive, only mentally ill people batter their families, mandatory arrest of offenders is the best way to reduce repeat episodes of violence, family violence is a private matter, law enforcement policies are ineffective for preventing family violence, children of single-parent, female-headed families are abused more than children of dual-parent households, and prosecution of an offender is unlikely regardless of how well a victim cooperates. Index scores calculated from groupings of various variables are included as well as whether the respondent found training interesting, relevant, well-organized, and useful. Demographic variables for each respondent include race, gender, age, and assignment and position in the police department. Variables for Part 2 include whether the domestic violence case occurred before or after training, to which test group the case belongs, the amount of time in minutes spent on the domestic violence scene, and whether the case resulted in a conviction.

The objective of this survey is to collect baseline information on police personnel and expenditures to enable detection of historical trends as well as permit comparisons at the provincial/territorial and municipal levels. This survey collects data from police services across Canada under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19. All municipal, provincial and federal police services in Canada are surveyed. Respondents provide the number of police officers employed by the service (full-time equivalents), categorized by rank (senior officers, non-commissioned officers, and constables) and by sex. Information on hirings, departures, and eligibility to retire is provided, as are data on the years of service, age, level of education at time of hire, and Aboriginal and visible minority status of police officers, and the official and non-official languages they speak. Police services also report their number of civilian employees, categorized by job type and by sex. Other questions collect data on operating expenditures broken down into salaries/wages, benefits, and other operating expenditures. Data from this survey provide information on total expenditures on policing and the number of officers in each province and in Canada as a whole, as well as the number of officers per 100,000 population. The data are intended for police services, for officials with responsibility for police budgets, for policy-makers with policing-related responsibilities, and for members of the general public.

This is the City of Raleigh Police Incident Data after 2005 and prior to June 1, 2014. Each row represents a report made by the police officer, but not all reports may have resulted in arrests or convictions. The locations provided with this dataset DO NOT reflect the exact locations where the incidents occurred. The locations provided represent a randomized location within the general neighborhood area where the incident was reported. To protect the privacy of victims and their families further, the Raleigh Police Department (RPD) has redacted all location information associated with incidents involving sexual assault, child abuse, juvenile incidents, domestic abuse and other related incidents. The column heading "LCR DESC" represents the description of Incident Type. The column heading "LCR" is the local code used by police to categorize the Incident Type Years covered: 2005 - June 1, 2014. This data is collected and presented according to the Uniform Crime Reporting (UCR) for the Summary Reporting System (SRS) standard set by the FBI. Find out more about the standards here: https://ucr.fbi.gov/ucr-program-data-collections#National & detailed information about Summary UCR method here: https://ucr.fbi.gov/nibrs/summary-reporting-system-srs-user-manualUpdate Frequency: NeverTime Period: 2005 - June 2014

The RMS Crime Incidents dataset consists of crime reports from the Detroit Police Department Records Management System (RMS). This data reflects criminal offenses reported in the City of Detroit that DPD was involved in from December 2016 to present. Note that records are included in the dataset based on when an incident is reported which could result in an occurrence date before December 2016. Incident data is typically entered into mobile devices by the officer in the field when responding to an incident. Incidents that occurred in Detroit but in a location that is under the jurisdiction of the Michigan State Police (MSP) or Wayne State University Police Department (WSUPD), such as on an expressway, Belle Isle, or around Wayne State University, are included only if the incident is handled by DPD. Such records are reviewed in a monthly audit to ensure that the incidents are counted by one and only one agency (MSP or DPD). This data is updated daily. For each crime incident, one or more offense charges are recorded, and each row in the dataset corresponds with one of these charges. An example could be a domestic assault where property was also vandalized. Offense charges that occurred at the same crime incident share a common incident number. For each offense charge record (rows)details include when and where the incident occurred, the nature of the offense, DPD precinct or detail, and the case investigation status. Locations of incidents associated with each call are reported based on the nearest intersection to protect the privacy of individuals.RMS Crime Incident data complies with Michigan Incident Crime Reporting (MICR) standards. More information about MICR standards is available via the MICR Website. The Manual and Arrest Charge Code Card may be especially helpful. There may be small differences between RMS Crime Incident data shared here and data shared through MICR given data presented here is updated here more frequently which results in a difference in a cadence of status updates. Additionally, this dataset includes crime incidents that following an investigation are coded with a case status of ‘Unfounded’. In most cases, this means that the incident occurred outside the jurisdiction of DPD or otherwise was reported in error. The State of Michigan, through the MICR program, reports data to the National Incident-Based Reporting System (NIBRS).Yearly Datasets for RMS Crime Incidents have been added to the ODP. This is to improve the user's experience in handling the large file size of the records in the comprehensive dataset. You may download each year separately, which significantly reduces the size and records for each file. In addition to the past years, we have also included a year-to-date dataset. This captures all RMS Crime Incidents from January 1, 2025, to present.Should you have questions about this dataset, you may contact the Commanding Officer of the Detroit Police Department's Crime Data Analytics at 313-596-2250 or CrimeIntelligenceBureau@detroitmi.gov.

Since 2014, Eurostat and the UNODC have launched a joint annual data collection on crime and criminal justice statistics, using the UN crime trends questionnaire and complementary Eurostat requests

for specific areas of interest to the European Commission. The data and metadata are collected from National Statistical Institutes or other relevant authorities (mainly police and justice departments) in each EU Member State, EFTA country and EU potential members. On the Eurostat website, data are available for 41 jurisdictions since 2008 until 2018 data and for 38 jurisdictions since 2019 data (EU-27, Iceland, Liechtenstein, Norway, Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia, Turkey, Kosovo(1)), having drop the data for the United Kingdom separately owing to three separate jurisdictions England and Wales, Scotland, Northern Ireland.

This joint data collection and other data collections carried out by Eurostat allows to gather information on:

• police-recorded offences by type of crime
• police-recorded offences by NUTS3 region
• intentional homicide and sexual violence victims and perpetrators (suspected, prosecuted, convicted) by sex
• intentional homicide victims by age, sex, and relationship to the offender
• intentional homicide victims and offences in largest cities
• offenders by justice legal status (suspected, prosecuted, convicted), age, sex, and citizenship
• persons brought before criminal courts by legal status (convicted persons/acquitted)
• personnel by institution (police, courts, and prisons) by sex
• legal cases in first instance courts by type and stage
• prisoners by age, sex, citizenship, and status of the trial process
• prison capacity and occupancy
• people involved in human trafficking by legal status (victims, suspected and convicted traffickers) and victims of human trafficking by all forms of exploitation and citizenship

Where available, data are broken down by sex, age groups (adults/juveniles), country of citizenship (foreigners or nationals) and other relevant variables. National data are available and for intentional homicide offences, city level data (largest cities) are available for some countries. Regional data at NUTS3 level are also available for some police-recorded offences.

Some historical series are available:

• Number of police-recorded crimes by type (intentional homicide, violence, robbery, home burglary, car thefts, and drug crimes) for the period 1993 – 2007
• Number of police-recorded homicide in cities for the period 1993 – 2007
• Number of police officers for the period 1993 – 2007
• Prison population for the period 1993 – 2007

Total number of police-recorded crimes for the period 1950 – 2000

(1) under United Nations Security Council Resolution 1244/99

This study examined the role of stalking in domestic violence crime reports produced by the Colorado Springs Police Department (CSPD). It provided needed empirical data on the prevalence of stalking in domestic violence crime reports, risk factors associated with intimate partner stalking, and police responses to reports of intimate partner stalking. The study was conducted jointly by the Justice Studies Center (JSC) at the University of Colorado at Colorado Springs and the Denver-based Center for Policy Research (CPR). JSC staff generated the sample and collected the data, and CPR staff processed and analyzed the data. The sample was generated from CSPD Domestic Violence Summons and Complaint (DVSC) forms, which were used by CSPD officers to investigate crime reports of victims and suspects who were or had been in an intimate relationship and where there was probable cause to believe a crime was committed. During January to September 1999, JSC staff reviewed and entered information from all 1998 DVSC forms into a computerized database as part of the evaluation process for Domestic Violence Enhanced Response Team (DVERT), a nationally recognized domestic violence prevention program. A subfile of reports initiated during April to September 1998 was generated from this database and formed the basis for the study sample. The DVSC forms contained detailed information about the violation including victim and suspect relationship, type of violation committed, and specific criminal charges made by the police officer. The DVSC forms also contained written narratives by both the victim and the investigating officer, which provided detailed information about the events precipitating the report, including whether the suspect stalked the victim. The researchers classified a domestic violence crime report as having stalking allegations if the victim and/or police narrative specifically stated that the victim was stalked by the suspect, or if the victim and/or police narrative mentioned that the suspect engaged in stalking-like behaviors (e.g., repeated following, face-to-face confrontations, or unwanted communications by phone, page, letter, fax, or e-mail). Demographic variables include victim-suspect relationship, and age, race, sex, and employment status of the victim and suspect. Variables describing the violation include type of violation committed, specific criminal charges made by the police officer, whether the alleged violation constituted a misdemeanor or a felony crime, whether a suspect was arrested, whether the victim sustained injuries, whether the victim received medical attention, whether the suspect used a firearm or other type of weapon, whether items were placed in evidence, whether the victim or suspect was using drugs and/or alcohol at the time of the incident, number and ages of children in the household, whether children were in the home at the time of the incident, and whether there was a no-contact or restraining order in effect against the suspect at the time of the incident.

Since 2014, Eurostat and the UNODC have launched a joint annual data collection on crime and criminal justice statistics, using the UN crime trends questionnaire and complementary Eurostat requests

for specific areas of interest to the European Commission. The data and metadata are collected from National Statistical Institutes or other relevant authorities (mainly police and justice departments) in each EU Member State, EFTA country and EU potential members. On the Eurostat website, data are available for 41 jurisdictions since 2008 until 2018 data and for 38 jurisdictions since 2019 data (EU-27, Iceland, Liechtenstein, Norway, Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia, Turkey, Kosovo(1)), having drop the data for the United Kingdom separately owing to three separate jurisdictions England and Wales, Scotland, Northern Ireland.

This joint data collection and other data collections carried out by Eurostat allows to gather information on:

• police-recorded offences by type of crime
• police-recorded offences by NUTS3 region
• intentional homicide and sexual violence victims and perpetrators (suspected, prosecuted, convicted) by sex
• intentional homicide victims by age, sex, and relationship to the offender
• intentional homicide victims and offences in largest cities
• offenders by justice legal status (suspected, prosecuted, convicted), age, sex, and citizenship
• persons brought before criminal courts by legal status (convicted persons/acquitted)
• personnel by institution (police, courts, and prisons) by sex
• legal cases in first instance courts by type and stage
• prisoners by age, sex, citizenship, and status of the trial process
• prison capacity and occupancy
• people involved in human trafficking by legal status (victims, suspected and convicted traffickers) and victims of human trafficking by all forms of exploitation and citizenship

Where available, data are broken down by sex, age groups (adults/juveniles), country of citizenship (foreigners or nationals) and other relevant variables. National data are available and for intentional homicide offences, city level data (largest cities) are available for some countries. Regional data at NUTS3 level are also available for some police-recorded offences.

Some historical series are available:

• Number of police-recorded crimes by type (intentional homicide, violence, robbery, home burglary, car thefts, and drug crimes) for the period 1993 – 2007
• Number of police-recorded homicide in cities for the period 1993 – 2007
• Number of police officers for the period 1993 – 2007
• Prison population for the period 1993 – 2007

Total number of police-recorded crimes for the period 1950 – 2000

(1) under United Nations Security Council Resolution 1244/99

Table containing detailed information related to the Uniformed Member of Service’s disciplinary history. This dataset includes individual charges for each UMOS. For any given incident, multiple charges may be filed against a UMOS.

This dataset is for RMS Crime Incidents for 2025 For the comprehensive dataset which includes all records please refer to the RMS Crime Incidents dataset. The RMS Crime Incidents dataset consists of crime reports from the Detroit Police Department Records Management System (RMS). This data reflects criminal offenses reported in the City of Detroit that DPD was involved in the 2025 calendar year. Incident data is typically entered into mobile devices by the officer in the field when responding to an incident. Incidents that occurred in Detroit but in a location that is under the jurisdiction of the Michigan State Police (MSP) or Wayne State University Police Department (WSUPD), such as on an expressway, Belle Isle, or around Wayne State University, are included only if the incident is handled by DPD. Such records are reviewed in a monthly audit to ensure that the incidents are counted by one and only one agency (MSP or DPD). This data is updated daily. For each crime incident, one or more offense charges are recorded, and each row in the dataset corresponds with one of these charges. An example could be a domestic assault where property was also vandalized. Offense charges that occurred at the same crime incident share a common incident number. For each offense charge record (rows) details include when and where the incident occurred, the nature of the offense, DPD precinct or detail, and the case investigation status. Locations of incidents associated with each call are reported based on the nearest intersection to protect the privacy of individuals.RMS Crime Incident data complies with Michigan Incident Crime Reporting (MICR) standards. More information about MICR standards is available via the MICR Website. The Manual and Arrest Charge Code Card may be especially helpful. There may be small differences between RMS Crime Incident data shared here and data shared through MICR given data presented here is updated here more frequently which results in a difference in a cadence of status updates. Additionally, this dataset includes crime incidents that following an investigation are coded with a case status of ‘Unfounded’. In most cases, this means that the incident occurred outside the jurisdiction of DPD or otherwise was reported in error. The State of Michigan, through the MICR program, reports data to the National Incident-Based Reporting System (NIBRS).Yearly Datasets for RMS Crime Incidents have been added to the ODP. This is to improve the user's experience in handling the large file size of the records in the comprehensive dataset. You may download each year separately, which significantly reduces the size and records for each file. In addition to the past years, we have also included a year-to-date dataset. This captures all RMS Crime Incidents from January 1, 2025, to present.Should you have questions about this dataset, you may contact the Commanding Officer of the Detroit Police Department's Crime Data Analytics at 313-596-2250 or CrimeIntelligenceBureau@detroitmi.gov.

Since 2014, Eurostat and the UNODC have launched a joint annual data collection on crime and criminal justice statistics, using the UN crime trends questionnaire and complementary Eurostat requests

for specific areas of interest to the European Commission. The data and metadata are collected from National Statistical Institutes or other relevant authorities (mainly police and justice departments) in each EU Member State, EFTA country and EU potential members. On the Eurostat website, data are available for 41 jurisdictions since 2008 until 2018 data and for 38 jurisdictions since 2019 data (EU-27, Iceland, Liechtenstein, Norway, Bosnia and Herzegovina, Montenegro, North Macedonia, Albania, Serbia, Turkey, Kosovo(1)), having drop the data for the United Kingdom separately owing to three separate jurisdictions England and Wales, Scotland, Northern Ireland.

This joint data collection and other data collections carried out by Eurostat allows to gather information on:

• police-recorded offences by type of crime
• police-recorded offences by NUTS3 region
• intentional homicide and sexual violence victims and perpetrators (suspected, prosecuted, convicted) by sex
• intentional homicide victims by age, sex, and relationship to the offender
• intentional homicide victims and offences in largest cities
• offenders by justice legal status (suspected, prosecuted, convicted), age, sex, and citizenship
• persons brought before criminal courts by legal status (convicted persons/acquitted)
• personnel by institution (police, courts, and prisons) by sex
• legal cases in first instance courts by type and stage
• prisoners by age, sex, citizenship, and status of the trial process
• prison capacity and occupancy
• people involved in human trafficking by legal status (victims, suspected and convicted traffickers) and victims of human trafficking by all forms of exploitation and citizenship

Where available, data are broken down by sex, age groups (adults/juveniles), country of citizenship (foreigners or nationals) and other relevant variables. National data are available and for intentional homicide offences, city level data (largest cities) are available for some countries. Regional data at NUTS3 level are also available for some police-recorded offences.

Some historical series are available:

• Number of police-recorded crimes by type (intentional homicide, violence, robbery, home burglary, car thefts, and drug crimes) for the period 1993 – 2007
• Number of police-recorded homicide in cities for the period 1993 – 2007
• Number of police officers for the period 1993 – 2007
• Prison population for the period 1993 – 2007

Total number of police-recorded crimes for the period 1950 – 2000

(1) under United Nations Security Council Resolution 1244/99