In 2023, the FBI reported that there were 9,284 Black murder victims in the United States and 7,289 white murder victims. In comparison, there were 554 murder victims of unknown race and 586 victims of another race. Victims of inequality? In recent years, the role of racial inequality in violent crimes such as robberies, assaults, and homicides has gained public attention. In particular, the issue of police brutality has led to increasing attention following the murder of George Floyd, an African American who was killed by a Minneapolis police officer. Studies show that the rate of fatal police shootings for Black Americans was more than double the rate reported of other races. Crime reporting National crime data in the United States is based off the Federal Bureau of Investigation’s new crime reporting system, which requires law enforcement agencies to self-report their data in detail. Due to the recent implementation of this system, less crime data has been reported, with some states such as Delaware and Pennsylvania declining to report any data to the FBI at all in the last few years, suggesting that the Bureau's data may not fully reflect accurate information on crime in the United States.

These data provide information on the number of arrests reported to the Federal Bureau of Investigation's (FBI) Uniform Crime Reporting (UCR) Program each month by police agencies in the United States. Although not as well known as the "Crimes Known to the Police" data drawn from the Uniform Crime Report's Return A form, the arrest reports by age, sex, and race provide valuable data on 43 offenses including violent, drug, gambling, and larceny crimes. The data received by ICPSR were structured as a hierarchical file containing (per reporting police agency) an agency header record, and 1 to 12 monthly header reports, and 1 to 43 detail offense records containing the counts of arrests by age, sex, and race for a particular offense. ICPSR restructured the original data to a rectangular format.

Investigator(s): Federal Bureau of Investigation Since 1930, the Federal Bureau of Investigation (FBI) has compiled the Uniform Crime Reports (UCR) to serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. With the 1977 data, the title was expanded to Uniform Crime Reporting Program Data. Each year, participating law enforcement agencies contribute reports to the FBI either directly or through their state reporting programs. ICPSR archives the UCR data as five separate components: (1) summary data, (2) county-level data, (3) incident-level data (National Incident-Based Reporting System [NIBRS]), (4) hate crime data, and (5) various, mostly nonrecurring, data collections. Summary data are reported in four types of files: (a) Offenses Known and Clearances by Arrest, (b) Property Stolen and Recovered, (c) Supplementary Homicide Reports (SHR), and (d) Police Employee (LEOKA) Data (Law Enforcement Officers Killed or Assaulted). The county-level data provide counts of arrests and offenses aggregated to the county level. County populations are also reported. In the late 1970s, new ways to look at crime were studied. The UCR program was subsequently expanded to capture incident-level data with the implementation of the National Incident-Based Reporting System. The NIBRS data focus on various aspects of a crime incident. The gathering of hate crime data by the UCR program was begun in 1990. Hate crimes are defined as crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity. In September 1994, disabilities, both physical and mental, were added to the list. The fifth component of ICPSR's UCR holdings is comprised of various collections, many of which are nonrecurring and prepared by individual researchers. These collections go beyond the scope of the standard UCR collections provided by the FBI, either by including data for a range of years or by focusing on other aspects of analysis. NACJD has produced resource guides on UCR and on NIBRS data.

In 2023, 8,842 murderers in the United States were white, while 6,405 were Black. A further 461 murderers were of another race, including American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander. However, not all law enforcement agencies submitted homicide data to the FBI in 2023, meaning there may be more murder offenders of each race than depicted. While the majority of circumstances behind murders in the U.S. are unknown, narcotics, robberies, and gang killings are most commonly identified.

In 2023, the violent crime rate in the United States was 363.8 cases per 100,000 of the population. Even though the violent crime rate has been decreasing since 1990, the United States tops the ranking of countries with the most prisoners. In addition, due to the FBI's transition to a new crime reporting system in which law enforcement agencies voluntarily submit crime reports, data may not accurately reflect the total number of crimes committed in recent years. Reported violent crime rate in the United States The United States Federal Bureau of Investigation tracks the rate of reported violent crimes per 100,000 U.S. inhabitants. In the timeline above, rates are shown starting in 1990. The rate of reported violent crime has fallen since a high of 758.20 reported crimes in 1991 to a low of 363.6 reported violent crimes in 2014. In 2023, there were around 1.22 million violent crimes reported to the FBI in the United States. This number can be compared to the total number of property crimes, roughly 6.41 million that year. Of violent crimes in 2023, aggravated assaults were the most common offenses in the United States, while homicide offenses were the least common. Law enforcement officers and crime clearance Though the violent crime rate was down in 2013, the number of law enforcement officers also fell. Between 2005 and 2009, the number of law enforcement officers in the United States rose from around 673,100 to 708,800. However, since 2009, the number of officers fell to a low of 626,900 officers in 2013. The number of law enforcement officers has since grown, reaching 720,652 in 2023. In 2023, the crime clearance rate in the U.S. was highest for murder and non-negligent manslaughter charges, with around 57.8 percent of murders being solved by investigators and a suspect being charged with the crime. Additionally, roughly 46.1 percent of aggravated assaults were cleared in that year. A statistics report on violent crime in the U.S. can be found here.

An annual publication in which the FBI provides data on the number of incidents, offenses, victims, and offenders in reported crimes that were motivated in whole or in part by a bias against the victim as perceived race, religion, sexual orientation, ethnicity, gender, disability, and gender identity.

In response to a growing concern about hate crimes, the United States Congress enacted the Hate Crime Statistics Act of 1990. The Act requires the attorney general to establish guidelines and collect, as part of the Uniform Crime Reporting (UCR) Program, data "about crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity, including where appropriate the crimes of murder and non-negligent manslaughter, forcible rape, aggravated assault, simple assault, intimidation, arson, and destruction, damage or vandalism of property." Hate crime data collection was required by the Act to begin in calendar year 1990 and to continue for four successive years. In September 1994, the Violent Crime Control and Law Enforcement Act amended the Hate Crime Statistics Act to add disabilities, both physical and mental, as factors that could be considered a basis for hate crimes. Although the Act originally mandated data collection for five years, the Church Arson Prevention Act of 1996 amended the collection duration "for each calendar year," making hate crime statistics a permanent addition to the UCR program. As with the other UCR data, law enforcement agencies contribute reports either directly or through their state reporting programs. Information contained in the data includes number of victims and offenders involved in each hate crime incident, type of victims, bias motivation, offense type, and location type.

These data provide information on the number of arrests reported to the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program each year by police agencies in the United States. These arrest reports provide data on 43 offenses including violent crime, drug use, gambling, and larceny. The data received by ICPSR were structured as a hierarchical file containing, per reporting police agency: an agency header record, and 1 to 49 detail offense records containing the counts of arrests by age, sex, and race for a particular offense. ICPSR restructured the original data to logical record length format with the agency header record variables copied onto the detail records. Consequently, each record contains arrest counts for a particular agency-offense.

In 2022, the prevalence of violent crime increased for all races in the United States in comparison to the previous year. In that year, around 1.23 percent of White Americans experienced one or more violent victimizations and approximately 1.39 percent of Black or African American people were the victims of a violent crime.

The Hate Crime Statistics dataset provides annual statistics on the number of incidents, offenses, victims, and offenders in reported crimes that are motivated in whole, or in part, by an offender’s bias against the victim’s perceived race, gender, gender identity, religion, disability, sexual orientation, or ethnicity. Hate crime data is captured by indicating the element of bias present in offenses already being reported to the UCR Program.

All law enforcement agencies, whether they submit Summary Reporting System (SRS) or National Incident-Based Reporting System (NIBRS) reports, can contribute hate crime data to the UCR Program using forms specified to collect such information.

Please see the UCR resources provided by the FBI for for more information on hate crime. Download this dataset to see totals for hate crimes across the country from 1991–2014.

In 2023, the FBI knew of 2,586 perpetrators of anti-Black or African American hate crimes conducted in the United States in that year. Furthermore, another 802 perpetrators of anti-White hate crimes were known to the FBI in that year.

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

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

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

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

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

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.

Version 5 release notes:
Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. Version 4 release notes:
Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent.
Made the 'population' column which is the total population in that agency.

Version 3 release notes:
Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes:
Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.

Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency.
Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.).

The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). In the late 1970s, the law enforcement community called for a thorough evaluative study of the UCR with the objective of recommending an expanded and enhanced UCR program to meet law enforcement needs into the 21st century. The FBI fully concurred with the need for an updated program to meet contemporary needs and provided its support, formulating a comprehensive redesign effort. Following a multiyear study, a "Blueprint for the Future of the Uniform Crime Reporting Program" was developed. Using the "Blueprint" and in consultation with local and state law enforcement executives, the FBI formulated new guidelines for the Uniform Crime Reports. The National Incident-Based Reporting System (NIBRS) is being implemented to meet these guidelines. NIBRS data are archived at ICPSR as 13 separate data files, which may be merged by using linkage variables. The data focus on a variety of aspects of a crime incident. Part 4, Administrative Segment, offers data on the incident itself (date and time). Each crime incident is delineated by one administrative segment record. Also provided are Part 5, Offense Segment (offense type, location, weapon use, and bias motivation), Part 6, Property Segment (type of property loss, property description, property value, drug type and quantity), Part 7, Victim Segment (age, sex, race, ethnicity, and injuries), Part 8, Offender Segment (age, sex, and race), and Part 9, Arrestee Segment (arrest date, age, sex, race, and weapon use). The Batch Header Segment (Parts 1-3) separates and identifies individual police agencies by Originating Agency Identifier (ORI). Batch Header information, which is contained on three records for each ORI, includes agency name, geographic location, and population of the area. Part 10, Group B Arrest Report Segment, includes arrestee data for Group B crimes. Window Segments files (Parts 11-13) pertain to incidents for which the complete Group A Incident Report was not submitted to the FBI. In general, a Window Segment record will be generated if the incident occurred prior to January 1 of the previous year or if the incident occurred prior to when the agency started NIBRS reporting. As with UCR, participation in NIBRS is voluntary on the part of law enforcement agencies. The data are not a representative sample of crime in the United States. For 1993, eight states, fully or partially participating in NIBRS, were included in the dataset.

Version 5 release notes:

• Removes support for SPSS and Excel data.
• Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
  
• Adds in agencies that report 0 months of the year.
• Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.
• Removes data on runaways.
  

Version 4 release notes:

• Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
  

Version 3 release notes:

• Add data for 2016.
• Order rows by year (descending) and ORI.

Version 2 release notes:

• Fix bug where Philadelphia Police Department had incorrect FIPS county code.

The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.

All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

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

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

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

I created 9 arrest categories myself. The categories are:

• Total Male Juvenile
• Total Female Juvenile
• Total Male Adult
• Total Female Adult
• Total Ma

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

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 Vi

The Tempe Police Department prides itself in its continued efforts to reduce harm within the community and is providing this dataset on hate crime incidents that occur in Tempe.The Tempe Police Department documents the type of bias that motivated a hate crime according to those categories established by the FBI. These include crimes motivated by biases based on race and ethnicity, religion, sexual orientation, disability, gender and gender identity.The Bias Type categories provided in the data come from the Bias Motivation Categories as defined in the Federal Bureau of Investigation (FBI) National Incident-Based Reporting System (NIBRS) manual, version 2020.1 dated 4/15/2021. The FBI NIBRS manual can be found at https://www.fbi.gov/file-repository/ucr/ucr-2019-1-nibrs-user-manua-093020.pdf with the Bias Motivation Categories found on pages 78-79.Although data is updated monthly, there is a delay by one month to allow for data validation and submission.Information about Tempe Police Department's collection and reporting process for possible hate crimes is included in https://storymaps.arcgis.com/stories/a963e97ca3494bfc8cd66d593eebabaf.Additional InformationSource: Data are from the Law Enforcement Records Management System (RMS)Contact: Angelique BeltranContact E-Mail: angelique_beltran@tempe.govData Source Type: TabularPreparation Method: Data from the Law Enforcement Records Management System (RMS) are entered by the Tempe Police Department into a GIS mapping system, which automatically publishes to open data.Publish Frequency: MonthlyPublish Method: New data entries are automatically published to open data. Data Dictionary

In 2023, a total of 5,439 white Americans were arrested for arson in the United States in comparison to 1,876 Americans who were Black or African American.

In 2023, the District of Columbia had the highest reported violent crime rate in the United States, with 1,150.9 violent crimes per 100,000 of the population. Maine had the lowest reported violent crime rate, with 102.5 offenses per 100,000 of the population. Life in the District The District of Columbia has seen a fluctuating population over the past few decades. Its population decreased throughout the 1990s, when its crime rate was at its peak, but has been steadily recovering since then. While unemployment in the District has also been falling, it still has had a high poverty rate in recent years. The gentrification of certain areas within Washington, D.C. over the past few years has made the contrast between rich and poor even greater and is also pushing crime out into the Maryland and Virginia suburbs around the District. Law enforcement in the U.S. Crime in the U.S. is trending downwards compared to years past, despite Americans feeling that crime is a problem in their country. In addition, the number of full-time law enforcement officers in the U.S. has increased recently, who, in keeping with the lower rate of crime, have also made fewer arrests than in years past.