In 2023, there were ***** incidents of hate crimes for which the motivation was anti-Black or African American sentiment, making it the leading cause of hate crimes in the United States in that year. A further ***** hate crimes had an anti-Jewish motivation, and ***** had an anti-gay male motivation.

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.

In 2023, there were ***** victims of anti-Black or African American hate crimes in the United States, making it the racially motivated hate crime with the most victims in that year. The second most common racially motivated hate crime, anti-Hispanic or Latino crimes, had ***** victims in that year.

In 2023, ***** hate crime offenses were reported in California, the most out of any state. New Jersey, New York, Washington, and Massachusetts rounded out the top five states for hate crime offenses in that year.

This Dataset contains year, US state, offender race, offender ethnicity, offence name, bias description and victim type-wise total number of hate crime victims in USA

In 2023, *** juveniles fell victim to anti-Black or African American hate crimes in the United States. A further *** juveniles were the victims of anti-Hispanic or Latino hate crimes, and another ** juveniles were victimized by anti-White hate crimes in that same year.

The graph illustrates the number of victims of race-based hate crimes in the United States in 2023. The x-axis lists various ethnic groups, while the y-axis represents the corresponding number of victims. The data reveals that Anti-Black hate crimes were the most prevalent, with 3224 victims, followed by Anti-Hispanic and Anti-Asian crimes with 861and 430 victims respectively. Other categories include Anti-Other Race (418), Anti-American Indian (112), Anti-Arab (154), and Anti-Native Pacific (15). The data indicates a significant disparity in the number of victims across different ethnic groups, with Anti-Black hate crimes being the most prominent.

This Dataset contains year, date of incident, US State and location wise total number of adult and juvenile victims and offenders. The dataset also has data based on offender race, offender ethnicity, offense name, bias description and victim type level

In 2023, ***** people fell victim to hate crimes in the United States for which the motivation was race, ethnicity, and/or ancestry. In total, there were ****** hate crime victims across the country in that year.

The graph illustrates the number of hate crime incidents against white people in the United States from 1991 to 2023. The x-axis represents the years, spanning from '91 to '23, while the y-axis indicates the annual number of incidents. Over this 33-year period, the number of incidents ranges from a low of 528 in 2011 to a high of 1,480 in 1993. Notable figures include 841 incidents in 1991, a decline to 539 in 2009, and a recent increase to 868 in 2023. The data shows a general downward trend in hate crime incidents from the early 1990s through the mid-2010s, followed by a significant rise in the latter years. This information is presented in a line graph format, effectively highlighting the long-term decrease and recent resurgence in hate crime incidents against white individuals in the United States.

In the United States, a number of challenges prevent an accurate assessment of the prevalence of hate crimes in different areas of the country. These challenges create huge gaps in knowledge about hate crime--who is targeted, how, and in what areas--which in turn hinder appropriate policy efforts and allocation of resources to the prevention of hate crime. In the absence of high-quality hate crime data, online platforms may provide information that can contribute to a more accurate estimate of the risk of hate crimes in certain places and against certain groups of people. Data on social media posts that use hate speech or internet search terms related to hate against specific groups has the potential to enhance and facilitate timely understanding of what is happening offline, outside of traditional monitoring (e.g., police crime reports). This study assessed the utility of Twitter data to illuminate the prevalence of hate crimes in the United States with the goals of (i) addressing the lack of reliable knowledge about hate crime prevalence in the U.S. by (ii) identifying and analyzing online hate speech and (iii) examining the links between the online hate speech and offline hate crimes. The project drew on four types of data: recorded hate crime data, social media data, census data, and data on hate crime risk factors. An ecological framework and Poisson regression models were adopted to study the explicit link between hate speech online and hate crimes offline. Risk terrain modeling (RTM) was used to further assess the ability to identify places at higher risk of hate crimes offline.

In 2023, there were ***** victims of anti-Black or African American intimidation hate crimes in the United States. A further *** people were the victims of anti-Black or African American simple assault hate crimes in that year.

This dataset contains the yearly statistics on the number of participating agencies, population covered, number of incidents reported, and agencies submitting incident reports.

This dataset contains the yearly statistics on the offense types by participating state, territory and federal agencies. Major categories of offense types include crimes against persons, crimes against property and crimes against society. Each offense type is further categorized by type of crime such as murder, rape, trafficking, robbery etc. and given in terms of State, territory and federal agencies.

According to a poll conducted in March 2021 in the United States, 39 percent of respondents said that hate crimes in their town over the past 12 months are at about the same level as compared to 10 years ago. In the same survey, 17 percent of respondents said that hate crimes were at a somewhat higher level in their town then they were 10 years ago.

In 2023, the FBI knew of ***** people who perpetrated hate crimes in the United States motivated by race, ethnicity and/or ancestry. A further ***** known hate crime offenders were motivated by the sexual orientation of their victims.

Anti-Jewish attacks were the most common form of anti-religious group hate crimes in the United States in 2023, with ***** cases. Anti-Islamic hate crimes were the second most common anti-religious hate crimes in that year, with *** incidents.

As of 2023, there were ** U.S. states with laws requiring law enforcement training pertaining to hate crimes. Among these, ** included crimes based on sexual orientation and gender identity. The majority of states did not have any mandated hate crime training for law enforcement.

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 6 release notes:Adds 2018 dataVersion 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.

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 victimÆs perceived race, religion, sexual orientation, ethni