Sadly, the trend of fatal police shootings in the United States seems to only be increasing, with a total 1,173 civilians having been shot, 248 of whom were Black, as of December 2024. In 2023, there were 1,164 fatal police shootings. Additionally, the rate of fatal police shootings among Black Americans was much higher than that for any other ethnicity, standing at 6.1 fatal shootings per million of the population per year between 2015 and 2024. Police brutality in the U.S. In recent years, particularly since the fatal shooting of Michael Brown in Ferguson, Missouri in 2014, police brutality has become a hot button issue in the United States. The number of homicides committed by police in the United States is often compared to those in countries such as England, where the number is significantly lower. Black Lives Matter The Black Lives Matter Movement, formed in 2013, has been a vocal part of the movement against police brutality in the U.S. by organizing “die-ins”, marches, and demonstrations in response to the killings of black men and women by police. While Black Lives Matter has become a controversial movement within the U.S., it has brought more attention to the number and frequency of police shootings of civilians.
This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.
This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.
The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.
How does this dataset classify victims?
The methodology by which this dataset classifies victims of violent crime differs by victimization type:
Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.
To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.
For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:
Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:
Note: All businesses identified as victims in CPD data have been removed from this dataset.
Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”
Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).
Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.
This dataset contains individual-level homicide and non-fatal shooting victimizations, including homicide data from 1991 to the present, and non-fatal shooting data from 2010 to the present (2010 is the earliest available year for shooting data). This dataset includes a "GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized-access dataset, but with "UNKNOWN" in the shooting column.
Each row represents a single victimization, i.e., a unique event when an individual became the victim of a homicide or non-fatal shooting. Each row does not represent a unique victim—if someone is victimized multiple times there will be multiple rows for each of those distinct events.
The dataset is refreshed daily, but excludes the most recent complete day to allow the Chicago Police Department (CPD) time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.
A version of this dataset with additional crime types is available by request. To make a request, please email dataportal@cityofchicago.org with the subject line: Violence Reduction Victims Access Request. Access will require an account on this site, which you may create at https://data.cityofchicago.org/signup.
How does this dataset classify victims?
The methodology by which this dataset classifies victims of violent crime differs by victimization type:
Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.
To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset. For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:
Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:
Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.” Officer-involved shootings are not included.
Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.
Note: In some instances, CPD's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most reliable crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).
Note: Homicide victims names are delayed by two weeks to allow time for the victim’s family to be notified of their passing.
Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.
Note: This dataset includes variables referencing administrative or political boundaries that are subject to change. These include Street Outreach Organization boundary, Ward, Chicago Police Department District, Chicago Police Department Area, Chicago Police Department Beat, Illinois State Senate District, and Illinois State House of Representatives District. These variables reflect current geographic boundaries as of November 1st, 2021. In some instances, current boundaries may conflict with those that were in place at the time that a given incident occurred in prior years. For example, the Chicago Police Department districts 021 and 013 no longer exist. Any historical violent crime victimization that occurred in those districts when they were in existence are marked in this dataset as having occurred in the current districts that expanded to replace 013 and 021."
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.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
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A. SUMMARY This dataset contains public information on police misconduct cases split up by unique allegations. This includes case numbers, dates of receipt, incident locations, police districts involved, allegations made, investigation findings, issuance dates of findings, and case closure dates. Its purpose is to enhance transparency and accountability by providing accessible details about alleged misconduct to the public. By offering insights into the scope and outcomes of investigations, it enables citizens to stay informed about incidents in their communities, fostering trust and facilitating discussions on police accountability and reform efforts. Access to this dataset empowers individuals to monitor the progress of cases and engage in constructive dialogue to promote positive change in law enforcement practices.
B. HOW THE DATASET IS CREATED The data set is created when a complaint is made to DPA. DPA opens the case and date is developed through out the life of the case.
C. UPDATE PROCESS The data is downloaded once a week from DPA’s Case Management System.
D. HOW TO USE THIS DATASET The dataset includes information on complaints received and closed in the current calendar year. Some cases will display received dates in previous years due to them being closed out in the current year.
E. RELATED DATASETS
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
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These Bloomington Police Department cases have been identified as Domestic Battery using the State Statue definition of 'domestic'.
Key code for Race:
A- Asian/Pacific Island, Non-Hispanic B- African American, Non-Hispanic C- Hawaiian/Other Pacific Island, Hispanic H- Hawaiian/Other Pacific Island, Non-Hispanic I- Indian/Alaskan Native, Non-Hispanic K- African American, Hispanic L- Caucasian, Hispanic N- Indian/Alaskan Native, Hispanic P- Asian/Pacific Island, Hispanic S- Asian, Non-Hispanic T- Asian, Hispanic U- Unknown W- Caucasian, Non-Hispanic
Key Code for Reading Districts:
Example: LB519
L for Law call or incident B stands for Bloomington 5 is the district or beat where incident occurred All numbers following represents a grid sector.
Disclaimer: The Bloomington Police Department takes great effort in making open data as accurate as possible, but there is no avoiding the introduction of errors in this process, which relies on data provided by many people and that cannot always be verified. Information contained in this dataset may change over a period of time. The Bloomington Police Department is not responsible for any error or omission from this data, or for the use or interpretation of the results of any research conducted.
https://louisville-metro-opendata-lojic.hub.arcgis.com/pages/terms-of-use-and-licensehttps://louisville-metro-opendata-lojic.hub.arcgis.com/pages/terms-of-use-and-license
This dataset consists of gun violence within Jefferson county that may fall within LMPDs radar, including non-fatal shootings, homicides, as well as shot-spotter data. The mapping data points where there are victims have been obfuscated to maintain privacy, while still being accurate enough to be placed in its correct boundaries, particularly around, neighborhoods, ZIP Codes, Council districts, and police divisions. The data also excludes any victim information that could be used to identify any individual. this data is used to make the public aware of what is going on in their communities. The data consists of only criminal incidents, excluding any cases that are deemed non-criminal.Field NameField DescriptionCase numberPolice report number. For ShotSpotter detections, it is the ShotSpotter ID.DateTimeDate and time in which the original incident occurred. Time is rounded down.AddressAddress rounded down to the one hundred block of where the initial incident occured. Unless it is an intersection.NeighborhoodNeighborhood in which the original incident occurred.Council DistrictCouncil district in which the original incident occurred.LatitudeLatitude coordinate used to map the incidentLongitudeLongitude coordinate used to map the incidentZIP CodeZIP Code in which the original incident occurred.Crime Typea distinction between incidents, whether it is a non-fatal shooting, homicide, or a ShotSpotter detection.CauseUsed to differentiate on the cause of death for homicide victims.SexGender of the victim of the initial incident.RaceRace/Ethnicity of the victim in a given incident.Age GroupCategorized age groups used to anonymize victim information.Division NamePolice division or department where the initial incident occurred.Crime report data is provided for Louisville Metro Police Divisions only; crime data does not include smaller class cities, unless LMPD becomes involved in smaller agency incident.The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Contact:Ivan Benitez, Ph.D.Gun Violence Data FellowOffice for Safe and Healthy Neighborhoodsivan.benitez@louisvilleky.gov
The Politbarometer has been conducted since 1977 on an almost monthly basis by the Research Group for Elections (Forschungsgruppe Wahlen) for the Second German Television (ZDF). Since 1990, this database has also been available for the new German states. The survey focuses on the opinions and attitudes of the voting population in the Federal Republic on current political topics, parties, politicians, and voting behavior. From 1990 to 1995 and from 1999 onward, the Politbarometer surveys were conducted separately in the eastern and western federal states (Politbarometer East and Politbarometer West). The separate monthly surveys of a year are integrated into a cumulative data set that includes all surveys of a year and all variables of the respective year. The Politbarometer short surveys, collected with varying frequency throughout the year, are integrated into the annual cumulation starting from 2003.
U.S. Government Workshttps://www.usa.gov/government-works
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Police Incidents represent all calls for police responses. These include burglary and trespass reports, assaults, drug related calls, thefts, vandalism, and reports of suspicious activity. Officer initiated activity such as traffic stops is also included in police incidents.
Addresses and Geolocation have been partially obfuscated to the nearest 100 block to protect the privacy of individuals. When mapped this may make the data appear to happen at an intersection when it actually happens within that block. For incidents that do not have an exact block number or the address was typed in by dispatch incorrectly the obfuscation method may place the item much further away than the actual location.
Some incidents will result in a police report, which can be found in the dataset "Police Cases." Case reports involving domestic violence, child abuse, or sex offenses involving minors have been removed from this data set.
Incidents designated as Collisions are included in this dataset and should be classified as what was reported to the 911 dispatcher. In order to track verified collisions a Police Traffic Collision Report (PTCR) is completed, which results in a case being created.
This project provided the first large-scale examination of the police response to intimate partner violence and of the practice known as "dual arrest." The objectives of the project were: (1) to describe the prevalence and context of dual arrest in the United States, (2) to explain the variance in dual arrest rates throughout the United States, (3) to describe dual arrest within the full range of the police response to intimate partner violence, (4) to analyze the factors associated with no arrest, single arrest, and dual arrest, (5) to examine the reasons why women are arrested in intimate partner cases, and (6) to describe how the criminal justice system treats women who have been arrested for domestic violence. Data for the project were collected in two phases. In Phase I, researchers examined all assault and intimidation cases in the year 2000 National Incident-Based Reporting System (NIBRS) database (NATIONAL INCIDENT-BASED REPORTING SYSTEM, 2000 [ICPSR 3449]) to investigate the extent to which dual arrest is occurring nationwide, the relationship between incident and offender characteristics, and the effect of state laws on police handling of these cases for all relationship types. Because the NIBRS dataset contained a limited number of incident-specific variables that helped explain divergent arrest practices, in Phase II, researchers collected more detailed information on a subset of NIBRS cases from 25 police departments of varying sizes across four states. This phase of the study was restricted to intimate partner and other domestic violence cases. Additional data were collected for these cases to evaluate court case outcomes and subsequent re-offending. This phase also included an assessment of how closely department policy reflected state law in a larger sample of agencies within five states. The data in Part 1 (Phase I Data) contain 577,862 records from the NIBRS. This includes information related to domestic violence incidents such as the most serious offense against the victim, the most serious victim injury, the assault type, date of incident, and the counts of offenses, offenders, victims, and arrests for the incident. The data also include information related to the parties involved in the incident including demographics for the victim(s) and arrestee(s) and the relationship between victim(s) and arrestee(s). There is also information related to the jurisdiction in which the incident occurred such as population, urban/rural classification, and whether the jurisdiction is located in a metropolitan area. There are also variables pertaining to whether a weapon was used, the date of arrest, and the type of arrest. Also included are variables regarding the police department such as the number of male and female police officers and civilians employed. The data in Part 2 (Phase II Data) contain 4,388 cases and include all of the same variables as those in Part 1. In addition to these variables, there are variables such as whether the offender was on the scene when the police arrived, who reported the incident, the exact nature of injuries suffered by the involved parties, victim and offender substance use, offender demeanor, and presence of children. Also included are variables related to the number of people including police and civilians who were on the scene, the number of people who were questioned, whether there were warrants for the victim(s) or offender(s), whether citations were issued, whether arrests were made, whether any cases were prosecuted, the number of charges filed and against whom, and the sentences for prosecuted cases that resulted in conviction. The data in Part 3 (Police Department Policy Data) contain 282 cases and include variables regarding whether the department had a domestic violence policy, what the department's arrest policy was, whether a police report needed to be made, whether the policy addressed mutual violence, whether the policy instructed how to determine the primary aggressor, and what factors were taken into account in making a decision to arrest. There is also information related to the proportion of arrests involving intimate partners, the proportion of arrests involving other domestics, the proportion of arrests involving acquaintances, and the proportion of arrests involving strangers.
https://www.icpsr.umich.edu/web/ICPSR/studies/3166/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/3166/terms
This project was an 18-month long research-practitioner partnership to conduct a process evaluation of the State College Police Department's implementation of a grant to encourage arrest policies for domestic violence. The general goals of the process evaluation were to assess how and to what extent the State College Police Department's proposed activities were implemented as planned, based on the rationale that such activities would enhance the potential for increasing victim safety and perpetrator accountability systemically. As part of the grant, the police department sought to improve case tracking and services to victims by developing new specialized positions for domestic violence, including: (1) a domestic violence arrest coordinator from within the State College Police Department who was responsible for monitoring case outcomes through the courts and updating domestic violence policies and training (Part 1, Victim Tracking Data from Domestic Violence Coordinator), (2) a victims service attorney from Legal Services who was responsible for handling civil law issues for domestic violence victims, including support, child custody, employment, financial, consumer, public benefits, and housing issues (Part 2, Victim Tracking Data From Victim Services Attorney), and (3) an intensive domestic violence probation officer from the Centre County Probation and Parole Department who was responsible for providing close supervision and follow-up of batterers (Part 3, Offender Tracking Data). Researchers worked with practitioners to develop databases suitable for monitoring service provision by the three newly-created positions for domestic violence cases. Major categories of data collected on the victim tracking form (Parts 1 and 2) included location of initial contact, type of initial contact, referral source, reason for initial contact, service/consultation provided at initial contact, meetings, and referrals out. Types of services provided include reporting abuse, filing a Protection from Abuse order, legal representation, and assistance with court procedures. Major categories of data collected on the offender tracking form (Part 3) included location of initial contact, type of initial contact, referral source, reason for initial contact, service/consultation provided, charges, sentence received, relationship between the victim and perpetrator, marital status, children in the home, referrals out, presentencing investigation completed, prior criminal history, and reason for termination. Types of services provided include pre-sentence investigation, placement on supervision, and assessment and evaluation. In addition to developing these new positions, the police department also sought to improve how officers handled domestic violence cases through a two-day training program. The evaluation conducted pre- and post-training assessments of all personnel training in 1999 and conducted follow-up surveys to assess the long-term impact of training. For Part 4, Police Training Survey Data, surveys were administered to law enforcement personnel participating in a two-day domestic violence training program. Surveys were administered both before and after the training program and focused on knowledge about domestic violence policies and protocols, attitudes and beliefs about domestic violence, and the background and experience of the officers. Within six months after the training, the same participants were contacted to complete a follow-up survey. Variables in Part 4 measure how well officers knew domestic violence arrest policies, their attitudes toward abused women and how to handle domestic violence cases, and their opinions about training. Demographic variables in Part 4 include age, sex, race, education, and years in law enforcement.
Complaints received by the Chicago Police Department Bureau of Internal Affairs (BIA). BIA investigates complaints of police misconduct that do not fall under the jurisdiction of the Civilian Office of Police Accountability (COPA). Types of misconduct investigated by BIA include the following (not a complete list):
A case will generate multiple rows, sharing the same LOG_NO if there are multiple officers. Each row in this dataset is an officer in a specific case.
To file a complaint with either BIA or COPA, please see https://www.chicagocopa.org/complaints.
The Commission has, since its inception, and in accordance with its Executive Order, released annual reports detailing its activities for each past year. The annual reports also include follow-up reviews of recommendations made in Commission studies, including the Department's ongoing handling of off-duty misconduct cases and discipline of officers who have made false official statements.
Categories of NYPD Internal Affairs Bureau cases reviewed by CCPC, that were closed during the 2017 and 2018 calendar years.
For CCPC's 19th Annual Report, CCPC reviewed 133 NYPD Internal Affairs Bureau (IAB) investigations that were closed during the 2017 and 2018 calendar years. CCPC analyzed these cases and evaluated whether they were fair, thoroguh, accurate, and impartial. This dataset reflects the most serious allegation and the number of cases for each category.NOTE: CCPC only reviewed a sample of IAB’s closed cases.
The purpose of the study was to better understand the factors associated with police decisions to make an arrest or not in cases of heterosexual partner violence and how these decisions vary across jurisdictions. The study utilized data from three large national datasets: the National Incident-Based Reporting System (NIBRS) for the year 2003, the Law Enforcement Management and Administrative Statistics (LEMAS) for the years 2000 and 2003, and the United States Department of Health and Human Services Area Resource File (ARF) for the year 2003. Researchers also developed a database of domestic violence state arrest laws including arrest type (mandatory, discretionary, or preferred) and primary aggressor statutes. Next, the research team merged these four databases into one, with incident being the unit of analysis. As a further step, the research team conducted spatial analysis to examine the impact of spatial autocorrelation in arrest decisions by police organizations on the results of statistical analyses. The dependent variable for this study was arrest outcome, defined as no arrest, single male arrest, single female arrest, and dual arrest for an act of violence against an intimate partner. The primary independent variables were divided into three categories: incident factors, police organizational factors, and community factors.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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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.
These data were collected for the second study on public support for vigilantism. The data collection was carried out by Leiden University criminology students, for the 'Research methods' course. Respondents received 1 vignette about vigilantism and a questionnaire. There were 4 versions of the vignette, based on 2 experimental factors: amount of vigilante violence (low/high) and police responsiveness to the precipitating crime (low/high). Vigilantism consisted of violence after an alleged case of shoplifting. The questionnaire consists of 17 items about the case of vigilantism, 27 items about the justice system, and 3 items about general concern over crime. Demographic information was also collected. To study the causes of public support for vigilantism. There are two main hypotheses: 1. Confidence hypothesis: support for vigilantism is caused by a low/lack of confidence in the criminal justice system 2. Situation hypothesis: support for vigilantism depends on situational characteristics of vigilantism itself Specific hypotheses for this particular study: a) the more vigilante violence, the less support for vigilantism b) the more police responsiveness, the less support for vigilantism c) the more confidence in the criminal justice system, the less support for vigilantism d) the more general concern over crime, the less support for vigilantism.
https://www.icpsr.umich.edu/web/ICPSR/studies/3026/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/3026/terms
The specific aim of this project was to evaluate the usefulness of the Seattle Police Department's (SPD) Lethality Scale in identifying misdemeanor cases that might be high risk for escalating violence and subsequent felony incidents. Data provide information on 11,972 unique couples with incidents occurring between January 1, 1995, and December 31, 1997, involving intimate couples in which the suspect was at least 18 years old and the victim was at least 16, with no age restriction for cases referred to the juvenile division. The researchers reformatted the Domestic Violence Unit's (DVU) database to reflect a three-year history of violence between unique couple members. Only intimate couples were considered, meaning suspects and victims who were married, divorced, had a child in common, or were dating. The Lethality Scale was derived from the data in the DVU database. It was composed of six incident characteristic components (offense score, weapon score, location score, injury score, personal score, and incident/other score) with varying values that contributed to an overall score. The Total Lethality Score was the sum of the values from these six components. The lethality score referred to an individual only and did not reflect information about other people involved in the incident. To interpret the score, the DVU specified a period of time--for example, six months--and computed lethality score values for every person involved in an incident during this period. Information on individuals with a Total Lethality Score over a certain cut-off was printed and reviewed by a detective. Data are provided for up to 25 incidents per unique couple. Incident variables in the dataset provide information on number of persons involved in the incident, time and weekday of the incident, beat, precinct, census tract, and place where the incident occurred, type of primary and secondary offenses, if a warrant was served, charges brought, final disposition, weapon type used, arrests made, court order information, if evidence was collected, if statements or photos were taken by the DVU, and sergeant action. Dates were converted to time intervals and provide the number of days between the incident date and the date the file was sent to the prosecutor, the date charges were brought, and the date the case was officially closed. Time intervals were also calculated for days between each incident for that couple. Personal information on the two persons in a couple includes age, gender, injuries and treatment, relationship and cohabitation status of the individuals, pregnancy status of each individual, alcohol and drug use at the time of the incident, and role of the individual in the incident (victim, suspect, victim/suspect). Lethality scale scores are included as well as the number of incidents in which the unique couple was involved in 1995 and 1996, and 1989 median household income for the census tract.
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A. SUMMARY This dataset contains public information on sheriff misconduct cases split up by unique allegations. This includes case numbers, dates of receipt, allegations made, investigation findings, issuance dates of findings, and case closure dates. Its purpose is to enhance transparency and accountability by providing accessible details about alleged misconduct to the public. By offering insights into the scope and outcomes of investigations, it enables citizens to stay informed.
B. HOW THE DATASET IS CREATED The data set is created when a complaint is made to the Office of Sheriff’s Inspector General (OSIG). OSIG opens the case and date is developed through out the life of the case.
C. UPDATE PROCESS The data is downloaded once a week from OSIG’s Case Management System.
D. HOW TO USE THIS DATASET The dataset includes information on complaints received and closed in the current calendar year. Some cases will display received dates in previous years due to them being closed out in the current year.
E. RELATED DATASETS
📊 Dataset Description This dataset contains detailed information on rape cases, covering various aspects such as incident details, outcomes, and associated legal codes. It aims to support research into the legal and social contexts of sexual violence. 🕵️♀️📑 🔑 Key Features of the Dataset Data on reported rape cases in [Country/Region] during the [year range]. 📋 Columns included in the dataset: headline 📰 - The headline or title of the report. district-tag 🏘️ - The district where the incident occurred. division-tag 📍 - The division categorizing the area of the incident. subdistrict-tag 🏠 - The subdistrict where the incident took place. id 🔢 - A unique identifier for each case. url 🌐 - The link to the full report, if available. last-published-at 📅 - The date when the report was last published. offset ⏳ - A data offset used for indexing or time information. content 📝 - The detailed content of the case report. meta_data 🗂️ - Metadata associated with each case, including additional tags or notes. legal_codes 📜 - A list of legal codes that were applied to the case. 📜 Includes a range of LEGAL_CODES that specify the legal framework applied to each case, such as: child_protection_act 👶 - Addresses crimes involving children. sexual_harassment_prevention_act 🚫 - Laws to prevent sexual harassment. domestic_violence_act 🏠💔 - Deals with domestic violence cases. criminal_assault_act 🥊 - Covers criminal assault offenses. public_safety_and_morality_act 🚨 - Enforces public safety and moral standards. womens_protection_act 👩🛡️ - Focuses on safeguarding women's rights and protection. kidnapping_and_coercion_act 🧑🤝🧑 - Pertains to abduction and coercion incidents. drug_and_alcohol_misuse_involvement 🍻 - Includes cases involving misuse of drugs or alcohol. attempt_to_murder 🔪 - Covers attempted murder offenses. murder_act ☠️ - Laws related to murder cases. unlawful_assembly 👥 - Addresses the unlawful gathering of people. psychological_torture_prevention 🧠⚖️ - Covers cases involving psychological abuse. witness_tampering_act 👀⚠️ - Addresses incidents of witness tampering. arbitration_and_mediation_misuse ⚖️ - Misuse of legal arbitration and mediation processes. police_misconduct_act 👮♂️🚫 - Deals with cases of police misconduct. 🧠 Research Applications Researchers can use this dataset for: 🔍 Investigating patterns of sexual violence and associated legal responses. 📊 Analyzing the social and legal dynamics surrounding these cases. ⚖️ Comparative studies between different LEGAL_CODES to understand their impact.
This study was conducted by the Police Foundation and the American Enterprise Institute to document municipal and county law enforcement agencies' policies for dealing with child abuse, neglect, and sexual assault and exploitation, and to identify emerging police practices. The researchers investigated promising approaches for dealing with child abuse and also probed for areas of weakness that are in need of improvement. Data were collected from 122 law enforcement agencies on topics including interagency reporting and case screening procedures, the existence and organizational location of specialized units for conducting child abuse investigations, actual procedures for investigating various types of child abuse cases, factors that affect the decision to arrest in physical and sexual abuse cases, the scope and nature of interagency cooperative agreements practices and relations, the amount of training received by agency personnel, and ways to improve agency responses to child abuse and neglect cases.
Sadly, the trend of fatal police shootings in the United States seems to only be increasing, with a total 1,173 civilians having been shot, 248 of whom were Black, as of December 2024. In 2023, there were 1,164 fatal police shootings. Additionally, the rate of fatal police shootings among Black Americans was much higher than that for any other ethnicity, standing at 6.1 fatal shootings per million of the population per year between 2015 and 2024. Police brutality in the U.S. In recent years, particularly since the fatal shooting of Michael Brown in Ferguson, Missouri in 2014, police brutality has become a hot button issue in the United States. The number of homicides committed by police in the United States is often compared to those in countries such as England, where the number is significantly lower. Black Lives Matter The Black Lives Matter Movement, formed in 2013, has been a vocal part of the movement against police brutality in the U.S. by organizing “die-ins”, marches, and demonstrations in response to the killings of black men and women by police. While Black Lives Matter has become a controversial movement within the U.S., it has brought more attention to the number and frequency of police shootings of civilians.