This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

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.

We use bootstrap data envelopment analysis, adjusting for endogeneity, to examine police efficiency in detecting crime in Hong Kong. We address the following: (i) is there a correlation between the detection of crime and triad influence? (ii) does the level of triad influence affect the efficiency in translating inputs (police strength) into outputs (crime detection)? and (iii) how can the allocation of policing resources be adjusted to improve crime detection? We find that nighty-eight percent of Hong Kong police districts in our sample were found to be inefficient in the detection of crime. Variation was found across districts regarding the detection of violent, property and other crimes. Most inefficiencies and potential improvements in the detection of crime were found in the categories violent and other crimes. We demonstrate how less efficient police districts can modify police resourcing decisions to better detect certain crime types while maintaining current levels of resourcing. Finally, we highlight how the method we outline improves efficiency estimation by adjusting for endogeneity and measuring the conditional efficiency of each district (i.e. the efficiency of crime detection taking the instrumental variables (e.g. influence of triads) into consideration). The use of frontier models to assist in evaluating policing performance can lead to improved efficiency, transparency, and accountability in law enforcement, ultimately resulting in better public safety outcomes and publicly funded resource allocation.

The Police-Public Contact Survey (PPCS) provides detailed information on the nature and characteristics of face-to-face contacts between police and the public, including the reason for and outcome of the contact and the respondent's satisfaction with the contact. The data can be used to estimate the likelihood of different types of contact for residents with different demographic characteristics, including contacts involving the use of nonfatal force by police. The PPCS is used to collect data from a nationally representative sample of U.S. residents age 16 or older as a supplement to the National Crime Victimization Survey. To date, the PPCS has been conducted eight times by BJS: 1. 1996. Described in the BJS publication Police Use of Force: Collection of National Data (NCJ 165040). 2. 1999. Described in Contacts between Police and the Public: Findings from the 1999 National Survey (NCJ 184957). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 1999: UNITED STATES. 3. 2002. Described in Contacts between Police and the Public: Findings from the 2002 National Survey (NCJ 207845). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2002: UNITED STATES. 4. 2005. Described in the BJS publication Contacts between Police and the Public, 2005 (NCJ 215243). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2005: UNITED STATES. 5. 2008. Described in the BJS publication Contacts between Police and the Public, 2008 (NCJ 234599). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2008 (ICPSR 32022). 6. 2011. Split sample design due to instrument changes. New instrument findings described in two publications: Police Behavior During Traffic and Street Stops, 2011 (NCJ 242937) and Requests for Police Assistance, 2011 (NCJ 242938). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2011 (ICPSR 34276). 7. 2015. Described in the BJS publication Contacts between Police and Public, 2015 (NCJ 251145). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2015 (ICPSR 36653). 8. 2018.Described in the BJS publication Contacts between Police and Public, 2018. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2018 (ICPSR 37916).

The Police-Public Contact Survey (PPCS) provides detailed information on the nature and characteristics of face-to-face contacts between police and the public, including the reason for and outcome of the contact and the respondent's satisfaction with the contact. The data can be used to estimate the likelihood of different types of contact for residents with different demographic characteristics, including contacts involving the use of nonfatal force by police. The PPCS is used to collect data from a nationally representative sample of U.S. residents age 16 or older as a supplement to the National Crime Victimization Survey. To date, the PPCS has been conducted ten times by BJS: 1. 1996. Described in the BJS publication Police Use of Force: Collection of National Data (NCJ 165040). 2. 1999. Described in Contacts between Police and the Public: Findings from the 1999 National Survey (NCJ 184957). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 1999: UNITED STATES. 3. 2002. Described in Contacts between Police and the Public: Findings from the 2002 National Survey (NCJ 207845). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2002: UNITED STATES. 4. 2005. Described in the BJS publication Contacts between Police and the Public, 2005 (NCJ 215243). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2005: UNITED STATES. 5. 2008. Described in the BJS publication Contacts between Police and the Public, 2008 (NCJ 234599). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2008 (ICPSR 32022). 6. 2011. Split sample design due to instrument changes. New instrument findings described in two publications: Police Behavior During Traffic and Street Stops, 2011 (NCJ 242937) and Requests for Police Assistance, 2011 (NCJ 242938). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2011 (ICPSR 34276). 7. 2015. Described in the BJS publication Contacts between Police and Public, 2015 (NCJ 251145). These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2015 (ICPSR 36653). 8. 2018. Described in the BJS publication Contacts between Police and Public, 2018. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2018 (ICPSR 37916). 9. 2020. Described in the BJS publication Contacts between Police and Public, 2020. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2020 (ICPSR 38320). 10. 2022. Described in the BJS publication Contacts between Police and Public, 2022. These data are archived as POLICE-PUBLIC CONTACT SURVEY, 2022 (ICPSR 38872).

This dataset is published by the City of Dallas for research purposes only. The authoritative source for crime data is the Crime Analytics Dashboard.

This dataset represents the Dallas Police Public Data - RMS Incidents beginning June 1, 2014 to current-date. The Dallas Police Department strives to collect and disseminate police report information in a timely, accurate manner. This information reflects crimes as reported to the Dallas Police Department as of the current date. Crime classifications are based upon preliminary information supplied to the Dallas Police Department by the reporting parties and the preliminary classifications may be changed at a later date based upon additional investigation. Therefore, the Dallas Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information contained herein and the information should not be used for comparison purposes over time. The Dallas Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information.

This online site is an attempt to make it easier for citizens to access offense reports. In disseminating this crime information, we must also comply with current laws that regulate the release of potentially sensitive and confidential information. To ensure that privacy concerns are protected and legal standards are met, report data is "filtered" prior to being made available to the public. Among the exclusions are:

1.) Sexually oriented offenses

2.) Offenses where juveniles or children (individuals under 17 years of age) are the victim or suspect

3.) Listing of property items that are considered evidence

4.) Social Service Referral offenses

5.) Identifying vehicle information in certain offenses

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This collection was part of a larger two-phase project funded by the National Institute of Justice (NIJ). Phase I focused on the development and estimation of predictive crime models in Shreveport, Louisiana and Chicago, Illinois. Phase II involved the implementation of a prevention model using the predictive model. To evaluate the two predictive policing pilot programs funded by NIJ, RAND evaluated the predictive and preventative models employed by the Shreveport Police Department titled Predictive Intelligence Led Operational Targeting (PILOT). RAND evaluated whether PILOT was associated with a measurable reduction in crime. The data were used to determine whether or not there was a statistically significant reduction in property crime counts in treated districts versus control districts in Shreveport. The collection includes 1 Excel file (Shreveport_Predictve_Policing_Evaluation_Experiment_Data.xlsx (n=91; 8 variables)) related only to the property crime aspect of the study. Neither data used to perform the outcomes evaluation for the Chicago Police Department experiment nor qualitative data used to help perform the prediction and prevention model evaluations are available.

The killing of Tyre Nichols in January 2023 by Memphis Police Officers has reignited debates about police brutality in the United States. Between 2013 and 2024, over 1,000 people have been killed by police in every year. Some of the most infamous examples include the murder of George Floyd in May 2020, and the shooting of Breonna Taylor earlier that year. Within this time period, the most people killed by police in the United States was in 2023, at 1,353 people. Police Violence in the U.S. Police violence is defined as any instance where a police officer’s use of force results in a civilian’s death, regardless of whether it is considered justified by the law. While many people killed by police in the U.S. were shot, other causes of death have included tasers, vehicles, and physical restraints or beatings. In the United States, the rate of police shootings is much higher for Black Americans than it is for any other ethnicity and recent incidents of police killing unarmed Black men and women in the United States have led to widespread protests against police brutality, particularly towards communities of color. America’s Persistent Police Problem Despite increasing visibility surrounding police violence in recent years, police killings have continued to occur in the United States at a consistently high rate. In comparison to other countries, police in the U.S. have killed people at a rate three times higher than police in Canada, and 60 times the rate of police in England. While U.S. police have killed people in almost all 50 states, as well as the District of Columbia, New Mexico was reported to have the highest rate of people killed by the police in the United States, with 8.03 people per million inhabitants killed by police.

The FIO program encompasses a wide range of interactions between the Boston Police Department (BPD) and private individuals. By releasing the records of these interactions, BPD hopes to add transparency to the execution of the program while still protecting the privacy of the individuals involved. These records are now sourced from three different record management systems titled: (OLD RMS) (NEW RMS) and (MARK43). The differences between the resulting files are described below.

About the FIO Records (Mark43) Files (Sept 29 2023 - Dec 31 2024)

These records are compiled from the BPD’s new Records Management System (RMS) on the BPD's FIO program. MARK43 went live September 29, 2019 and the FIO information has been structured into two separate tables. These tables are the same titles as (NEW RMS) but include new or different data points as retrieved from MARK43.

• FieldContact, which lists each contact between BPD and one or more individuals
• FieldContact_Name, which lists each individual involved in these contacts.

A FIO Data Key has also been created and posted to help distinguish the data categories (Data Key (Mark43)).

Lastly, FIOs are maintained in a live database and information related to each individual may change overtime. The data provided here should be considered a static representation of the Field Interaction and/or Observation that occurred in 2019.

NULL indicates no entry was made for an optional field.

About the FIO Records 2015 (New RMS) and 2016, 2017, 2018, 2019, and 2020 (Jan 1 - Sept 29 2020) Files

These records are compiled from the BPD’s new Records Management System (RMS) on the BPD's FIO program. The new RMS, which went live in June, 2015, structures the FIO information into two separate tables:

• FieldContact, which lists each contact between BPD and one or more individuals
• FieldContact_Name, which lists each individual involved in these contacts

While these two tables align on the field contact number (fc_num) column, it is not methodologically correct to join the two datasets for the purpose of generating aggregate statistics on columns from the FieldContact table. Doing so would lead to incorrect estimates stemming from contacts with multiple individuals. As noted in the Data Key (New RMS) file, several of the columns in the FieldContact table apply to the contact as a whole, but may not necessarily apply to each individual involved in the contact. These include:

• frisked
• searchperson
• summonsissued
• circumstances
• basis
• contact_reason

For example, the frisked column contains a value of Y if any of the individuals involved in a contact were frisked, but it would be inaccurate to assume that all individuals were frisked during that contact. As such, extrapolating from the frisked column for a contact to each individual and then summing across them would give an artificially high estimate of the number of people frisked in total. Likewise, the summonsissued column indicates when someone involved in a contact was issued a summons, but this does not imply that everyone involved in a contact was issued a summons.

For a detailed listing of columns in each table, see both tables of the Data Key (New RMS) file below.

About the FIO Records 2011 - 2015 (Old RMS) File

These records are sourced from BPD's older RMS, which was retired in June, 2015. This system (which stored all records in a single table, rather than the two tables in the newer system) captures similar information to the new RMS, but users should note that the fields are not identical and exercise care when comparing or combining records from each system.

Additional Notes

• The data provided is FIO information entered into the new system from June, 2015 through December, 2016, which includes some interactions which occurred before June, 2015 which were entered after the transition from the old system. For comprehensive analyses of interactions prior to the introduction of the new RMS, users will need to include data on interactions prior to June, 2015 from the 2015 (New RMS) file.
• These files are extracted from live databases which may have records added or updated at any time. As such, the number and content of records shared here may differ slightly from versions used to produce analyses such as those linked below, due to subsequent revisions to the underlying database records.
• A contact can consist of an observation of a vehicle, without direct contact with a person. This would create a record where no person-level details are recorded.

For more information on the FIO Program, please visit:

Boston Police Commissioner Announces Field Interrogation and Observation (FIO) Study Results

Commissioner Evans Continues Efforts to Increase Transparency and Accountability of Policing Activities to the Public

Boston Police Department Releases Latest Field Interrogation Observation Data

With a crime rate of 132.4 per 1,000 people Cleveland, in North East England, had the highest crime rate of all the police force areas in England and Wales in 2023/24. High crime rates are evident in other areas of northern England, such as West Yorkshire and Greater Manchester at 121.7 and 117.7 respectively. In the English capital, London, the crime rate was 105.1 per 1,000 people. The lowest crime rate in England was in the relatively rural areas of Wiltshire in South West England, as well as North Yorkshire. Overall crime on the in England and Wales The number of crimes in England and Wales reached approximately 6.74 million in 2022/23, falling slightly to 6.66 million in 2023/24. Overall crime has been rising steadily across England and Wales for almost a decade, even when adjusted for population rises. In 2022/23, for example, the crime rate in England and Wales was 93.6, the highest since 2006/07. When compared with the rest of the United Kingdom, England and Wales is something of an outlier, as crime rates for Scotland and Northern Ireland have not followed the same trajectory of rising crime. Additionally, there has been a sharp increase in violent crimes and sexual offences since the mid-2010s in England and Wales. While theft offences have generally been falling, the number of shoplifting offences reached a peak of 440,000 in 2023/24. Troubled justice system under pressure Alongside rising crime figures, many indicators also signal that the justice system is getting pushed to breaking point. The percentage of crimes that are solved in England and Wales was just 5.7 percent in 2023, with sexual offences having a clearance rate of just 3.6 percent. Crimes are also taking far longer than usual to pass through the justice system. In 2023, it took an average of 676 days for a crown court case to reach a conclusion from the time of the offence. This is most likely related to the large backlog of cases in crown courts, which reached over 62,200 in 2023. Furthermore, prisons in England and Wales are dangerously overcrowded, with just 1,458 spare prison places available as of June 2024.

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models. The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months. A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study. The statistical datasets consist of Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases. The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

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.

In addition to police-reported incidents that involve a hate crime motivation, there are four specific offences listed as hate propaganda and hate crimes in the Criminal Code of Canada: advocating genocide; incitement of hatred in a public place that is likely to lead to a breach of the peace [public incitement of hatred]; willful promotion of hatred, all when directed against an identifiable group, and; mischief motivated by hate in relation to property primarily used for religious worship.Depending on the level of evidence at the time of the incident, police can record the incident as either a “suspected” or “confirmed” hate-motivated crime. As more information is gathered, incidents are reviewed and verified and as a result, their status may be reclassified. Suspected hate crimes may include criminal incidents that cannot be confirmed as hate crimes, but for which there is sufficient evidence to suspect that they are motivated by hate, e.g., hate graffiti where no accused has been identified.To ensure personal privacy, occurrence locations have been aggregated to the corresponding neighbourhoods and Statistics Canada census tract areas. The crime statistics published are accurate on the day that they were produced. Due to ongoing police investigations and internal data quality control efforts, this information is subject to change, including addition, deletion and reclassification of any and all data.Date created: July 27th, 2023Update frequency: AnnuallyAccuracy: The Ottawa Police provides this information in good faith but provides no warranty, nor accepts any liability arising from any incorrect, incomplete or misleading information or its improper use.Attributes: The attributes in this table represent fields in the Ottawa Police Records Management System (RMS). NOTE: In partnership with Statistics Canada and the Canadian Association of Chief’s of Police, hate crime type and motivation variables are in the process of being updated to better reflect the nature of the incident and modernize language to current standards. 1. ID 2. Year 3. Reported Date4. Reported Time 5. Reported Weekday6. Occurrence Date7. Occurrence Time 8. Occurrence Weekday 9. Hate Crime Type:AgeSex ReligionLanguageRace/ Ethnicity Sexual OrientationImmigrants/ Newcomers to CanadaUnknown Motivation10. Primary Hate Crime Motivation:AgeChildren (0-14)Youth (15-24)Adults (25-64)Seniors (65 years and over)Unknown AgeSex MaleFemale Other SexUnknown SexReligionCatholicJewishMuslimOther ReligionUnknown ReligionLanguageEnglishFrenchOther Language Unknown LanguageDisabilityMentalPhysicalOther DisabilityUnknown Disability Race / EthnicityIndigenousArab (West Asian, Middle Eastern and North African Origins)/West AsianBlackChineseEast and Southeast AsianIndia/ Pakistan/ South AsianSouth AsianWhiteMultiple Races/EthnicitiesOther Race/EthnicityUnknown Race/EthnicitySexual-Orientation BisexualHeterosexual Homosexual (Lesbian or Gay)LGBTQ2+Other Sexual OrientationUnknown Sexual OrientationOther Similar Factor (including motivations not otherwise stated above, such as profession or political beliefs)11. Hate Crime IndicatorHC Confirmed (Confirmed hate crime incident)HC Suspected (Suspected hate crime incident)12. Primary UCR13. Primary Offence14. CCJS ClearanceCleared (Solved):Cleared by chargeSuicide of CSCDeath of CSC (not suicide)Death of complainant or witnessReason beyond control of department (policy)Diplomatic immunityCSC under 12 years of ageCommittal of the CSC to a mental health facilityCSC outside Canada, cannot be returnedVictim/complainant requests that no further action is taken CSC involved in other incidentsCSC already sentencedDepartmental discretionDiversionary ProgramIncident cleared by a lesser structureIncident cleared by another agencyNot Cleared – (Unsolved):Insufficient evidence to proceedVictim/complainant declines to proceedOpen (still under investigation)15. ONS Neighbourhood 16. Ottawa Police Sector 17. Ottawa Police Division 18. Census Tract Unique ID19. Census Tract NameAuthor: Ottawa Police ServiceAuthor email: info@ottawapolice.caMaintainer Organization: Business Performance Unit

In anticipation of the FBI transitioning to NIBRS by January 2021, the Raleigh Police Department was one of the first agencies in North Carolina to convert from the Uniform Crime Reporting (UCR) program Summary Reporting System (SRS) to the UCR - National Incident Based Reporting System (NIBRS) in June 2014.NIBRS now collects each offense, victim, offender, property, and arrestee information on 52 unique offenses and up to 10 offenses per incident. These new categories can be more defined and increasingly vary at the local level. As a result, these differences can make it difficult to compare statistics.For more information about NIBRS, go to FBI website: https://ucr.fbi.gov/nibrs-overviewUpdate Frequency: DailyTime Period: Previous Day

The purpose of this systematic review was to compile and synthesize published and unpublished empirical studies of the effects of second responder programs on repeat incidents of family violence. The researchers employed multiple strategies to search for literature that met the eligibility criteria. A keyword search was performed on a variety of online databases. Researchers reviewed the bibliographies of all second responder studies they located. Researchers performed hand searches of leading journals in the field and searched the Department of Justice Office of Violence Against Women Web site for a listing of federally-funded second responded programs and any evaluations conducted on those programs. A total of 22 studies that discussed second responder programs were found by the research team. Of these, 12 were eliminated from the sample because they did not meet the inclusion criteria, leaving a final sample of 10 studies. After collecting an electronic or paper copy of each article or report, researchers extracted pertinent data from each eligible article using a detailed coding protocol. Two main outcome measures were available for a sufficient number of studies to permit meta-analysis. One outcome was based on police data (Part 1: Police Data, n=9), for example whether a new domestic violence incident was reported to the police in the form of a crime report within six months of the triggering incident. The second outcome was based on survey data (Part 2: Interview Data, n=8), for example whether a new domestic violence incident occurred and was reported to a researcher during an interview within six months of the triggering incident. Several of studies (n=7) included in the meta-analysis had both outcome measures.

In 2023, murder and manslaughter charges had the highest crime clearance rate in the United States, with 57.8 percent of all cases being cleared by arrest or so-called exceptional means. Motor vehicle theft cases had the lowest crime clearance rate, at 8.2 percent. What is crime clearance? Within the U.S. criminal justice system, criminal cases can be cleared (or closed) one of two ways. The first is through arrest, which means that at least one person has either been arrested, charged with an offense, or turned over to the court for prosecution. The second way a case can be closed is through what is called exceptional means, where law enforcement must have either identified the offender, gathered enough evidence to arrest, charge, and prosecute someone, identified the offender’s exact location, or come up against a circumstance outside the control of law enforcement that keeps them from arresting and prosecuting the offender. Crime in the United States Despite what many people may believe, crime in the United States has been on the decline. Particularly in regard to violent crime, the violent crime rate has almost halved since 1990, meaning that the U.S. is safer than it was almost 30 years ago. However, due to the FBI's recent transition to a new crime reporting system in which law enforcement agencies voluntarily report crime data, it is possible that figures do not accurately reflect the total amount of crime in the country.

In 1996 the Institute for Law and Justice (ILJ) began an evaluation of the law enforcement and prosecution components of the "STOP Violence Against Women" grant program authorized by the Violence Against Women Act of 1994. This data collection constitutes one component of the evaluation. The researchers chose to evaluate two specialized units and two multi-agency team projects in order to study the local impact of STOP on victim safety and offender accountability. The two specialized units reflected typical STOP funding, with money being used for the addition of one or two dedicated professionals in each community. The Dane County, Wisconsin, Sheriff's Office used STOP funds to support the salaries of two domestic violence detectives. This project was evaluated through surveys of domestic violence victims served by the Dane County Sheriff's Office (Part 1). In Stark County, Ohio, the Office of the Prosecutor used STOP funds to support the salary of a designated felony domestic violence prosecutor. The Stark County project was evaluated by tracking domestic violence cases filed with the prosecutor's office. The case tracking system included only cases involving intimate partner violence, with a male offender and female victim. All domestic violence felons from 1996 were tracked from arrest to disposition and sentence (Part 2). This pre-grant group of felons was compared with a sample of cases from 1999 (Part 3). In Hillsborough County, New Hampshire, a comprehensive evaluation strategy was used to assess the impact of the use of STOP funds on domestic violence cases. First, a sample of 1996 pre-grant and 1999 post-grant domestic violence cases was tracked from arrest to disposition for both regular domestic violence cases (Part 4) and also for dual arrest cases (Part 5). Second, a content analysis of police incident reports from pre- and post-grant periods was carried out to gauge any changes in report writing (Part 6). Finally, interviews were conducted with victims to document their experiences with the criminal justice system, and to better understand the factors that contribute to victim safety and well-being (Part 7). In Jackson County, Missouri, evaluation methods included reviews of prosecutor case files and tracking all sex crimes referred to the Jackson County Prosecutor's Office over both pre-grant and post-grant periods (Part 8). The evaluation also included personal interviews with female victims (Part 9). Variables in Part 1 (Dane County Victim Survey Data) describe the relationship of the victim and offender, injuries sustained, who called the police and when, how the police responded to the victim and the situation, how the detective contacted the victim, and services provided by the detective. Part 2 (1996 Stark County Case Tracking Data), Part 3 (1999 Stark County Case Tracking Data), Part 4 (Hillsborough County Regular Case Tracking Data), Part 5 (Hillsborough County Dual Arrest Case Tracking Data), and Part 8 (Jackson County Case Tracking Data) include variables on substance abuse by victim and offender, use of weapons, law enforcement response, primary arrest offense, whether children were present, injuries sustained, indictment charge, pre-sentence investigation, victim impact statement, arrest and trial dates, disposition, sentence, and court costs. Demographic variables include the age, sex, and ethnicity of the victim and the offender. Variables in Part 6 (Hillsborough County Police Report Data) provide information on whether there was an existing protective order, whether the victim was interviewed separately, severity of injuries, seizure of weapons, witnesses present, involvement of children, and demeanor of suspect and victim. In Part 7 (Hillsborough County Victim Interview Data) variables focus on whether victims had prior experience with the court, type of physical abuse experienced, injuries from abuse, support from relatives, friends, neighbors, doctor, religious community, or police, assistance from police, satisfaction with police response, expectations about case outcome, why the victim dropped the charges, contact with the prosecutor, criminal justice advocate, and judge, and the outcome of the case. Demographic variables include age, race, number of children, and occupation. Variables in Part 9 (Jackson County Victim Interview Data) relate to when victims were sexually assaulted, if they knew the perpetrator, who was contacted to help, victims' opinions about police and detectives who responded to the case, contact with the prosecutor and victim's advocate, and aspects of the medical examination. Demographic variables include age, race, and marital status.

This study was undertaken to provide current information on work and family issues from the police officer's perspective, and to explore the existence and prevalence of work and family training and intervention programs offered nationally by law enforcement agencies. Three different surveys were employed to collect data for this study. First, a pilot study was conducted in which a questionnaire, designed to elicit information on work and family issues in law enforcement, was distributed to 1,800 law enforcement officers representing 21 municipal, suburban, and rural police agencies in western New York State (Part 1). Demographic information in this Work and Family Issues in Law Enforcement (WFILE) questionnaire included the age, gender, ethnicity, marital status, highest level of education, and number of years in law enforcement of each respondent. Respondents also provided information on which agency they were from, their job title, and the number of children and step-children they had. The remaining items on the WFILE questionnaire fell into one of the following categories: (1) work and family orientation, (2) work and family issues, (3) job's influence on spouse/significant other, (4) support by spouse/significant other, (5) influence of parental role on the job, (6) job's influence on relationship with children, (7) job's influence on relationships and friendships, (8) knowledge of programs to assist with work and family issues, (9) willingness to use programs to assist with work and family issues, (10) department's ability to assist officers with work and family issues, and (11) relationship with officer's partner. Second, a Police Officer Questionnaire (POQ) was developed based on the results obtained from the pilot study. The POQ was sent to over 4,400 officers in police agencies in three geographical locations: the Northeast (New York City, New York, and surrounding areas), the Midwest (Minneapolis, Minnesota, and surrounding areas), and the Southwest (Dallas, Texas, and surrounding areas) (Part 2). Respondents were asked questions measuring their health, exercise, alcohol and tobacco use, overall job stress, and the number of health-related stress symptoms experienced within the last month. Other questions from the POQ addressed issues of concern to the Police Research and Education Project -- a sister organization of the National Association of Police Organizations -- and its membership. These questions dealt with collective bargaining, the Law Enforcement Officer's Bill of Rights, residency requirements, and high-speed pursuit policies and procedures. Demographic variables included gender, age, ethnicity, marital status, highest level of education, and number of years employed in law enforcement. Third, to identify the extent and nature of services that law enforcement agencies provided for officers and their family members, an Agency Questionnaire (AQ) was developed (Part 3). The AQ survey was developed based on information collected from previous research efforts, the Violent Crime Control and Law Enforcement Act of 1994 (Part W-Family Support, subsection 2303 [b]), and from information gained from the POQ. Data collected from the AQ consisted of whether the agency had a mission statement, provided any type of mental health service, and had a formalized psychological services unit. Respondents also provided information on the number of sworn officers in their agency and the gender of the officers. The remaining questions requested information on service providers, types of services provided, agencies' obstacles to use of services, agencies' enhancement of services, and the organizational impact of the services.