In 2022, a total of ******* Hispanic/Latino victims experienced one or more violent crime. This was an increase from the previous year, when there were ******* Hispanic or Latino victims of violent crime.

In 2024, the highest homicide rate among 22 Latin American and Caribbean countries surveyed was in Haiti, with around 62 murders committed per 100,000 inhabitants. Trinidad and Tobago came in second, with a homicide rate of 46, while Honduras ranked seventh, with 25. In the same year, the lowest rate was recorded in El Salvador, with a homicide rate of 1.9 per 100,000 inhabitants. A violence-ridden region Violence and crime are some of the most pressing problems affecting Latin American society nowadays. More than 40 of the 50 most dangerous cities in the world are located in this region, as well as one of the twenty countries with the least peace in the world according to the Global Peace Index. Despite governments’ large spending on security and high imprisonment rates, drug and weapon trafficking, organized crime, and gangs have turned violence into an epidemic that affects the whole region and a solution to this issue appears to be hardly attainable. The cost of violence in Mexico Mexico stands out as an example of the great cost that violence inflicts upon a country, since beyond claiming human lives, it also affects everyday life and has a negative impact on the economy. Mexicans have a high perceived level of insecurity, as they do not only fear becoming victims of homicide, but also of other common crimes, such as assault or rape. Such fear prevents people from performing everyday activities, for instance, going out at night, taking a taxi or going to the movies or the theater. Furthermore, the economic toll of violence in Mexico is more than considerable. For example, the cost of homicide and violent crime amounted to 2099.8 and 1778.1 billion Mexican pesos in 2023, respectively.

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of cert

Several countries located in Central America and the Caribbean registered some of the highest homicide rates in the Latin American region in 2022. Jamaica ranked first, with around 53.34 homicides committed per 100,000 inhabitants. Saint Vincent and the Grenadines came second, with 40.41 homicides per 100,000 people. The source defines intentional homicide as the unlawful death inflicted upon a person with the intent to cause death or serious injury. When it comes to the total case count, Brazil was the Latin American country with the largest number of homicide victims. Central America and the falling rates El Salvador was commonly named the murder capital of the world for a few years. The inability of previous governments to control organized crime and gangs resulted in the highest homicide rate in the world for a couple of years. Nonetheless, the current administration and the measures applied during the Emergency State had an incredibly positive impact in terms of the security of the Salvadorean citizens. But not only El Salvador has seen a considerable reduction in its murder rate in Central America. Honduras and Guatemala are also two great examples of crime reduction, introducing new policies, institutions, and changes to their judicial system to achieve better results.
The Caribbean still ridden by crime Some islands in the Caribbean are not only known as tax heavens, as some nations in the region are considered the main enablers of tax evasion in the world, but also for being ridden by crime. Haiti is one example of the still rising levels of criminality. As a country with precarious conditions and extreme food insecurity, the homicide rate has been on the rise for almost four consecutive years. Another one is Jamaica, the top of the Latin American ranking, that has also seen an increase in the youth involved in organized crime due to lack of employment and economic conditions.

description: This set of raw data contains information from Bloomington Police Department Hate Crime data. # Key code for Race: - A- Asian/Pacific Island, Non-Hispanic - B- African American, 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 - 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. A map of the five districts can be located on Raidsonline.com, under the tab labeled Agency Layers . Disclaimer: The Bloomington Police Department takes great effort in making Hate Crime data as accurate as possible, but there is no avoiding the introduction of errors in this process, which relies on data provided 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.; abstract: This set of raw data contains information from Bloomington Police Department Hate Crime data. # Key code for Race: - A- Asian/Pacific Island, Non-Hispanic - B- African American, 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 - 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. A map of the five districts can be located on Raidsonline.com, under the tab labeled Agency Layers . Disclaimer: The Bloomington Police Department takes great effort in making Hate Crime data as accurate as possible, but there is no avoiding the introduction of errors in this process, which relies on data provided 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.

Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2024.

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 **** percent of White Americans experienced one or more violent victimizations and approximately **** percent of Black or African American people were the victims of a violent crime.

In 2024, the Mexican city of Colima was the second most deadly city in the world, with a murder rate of ****** per 100,000 inhabitants. * out of the top 10 cities with over ******* habitants and the highest homicide rates were located in Mexico.

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 study used the National Incident-Based Reporting System (NIBRS) to explore whether changes in the 2000-2010 decade were associated with changes in the prevalence and nature of violence between and among Whites, Blacks, and Hispanics. This study also aimed to construct more accessible NIBRS cross-sectional and longitudinal databases containing race/ethnic-specific measures of violent victimization, offending, and arrest. Researchers used NIBRS extract files to examine the influence of recent social changes on violence for Whites, Blacks, and Hispanics, and used advanced imputation techniques to account for missing values on race/ethnic variables. Data for this study was also drawn from the National Historical Geographic Information System, the Census Gazetteer, and Law Enforcement Officers Killed or Assaulted (LEOKA). The collection includes 1 Stata data file with 614 cases and 159 variables and 2 Stata syntax files.

Mexican cartels lose many members due to conflict with other cartels and arrests. Yet, despite their losses, cartels managed to increase violence for years. We address this puzzle by leveraging data on homicides, missing persons and arrests in Mexico for the past decade, along with information on cartel interactions. We model recruitment, state incapacitation, conflict and saturation as sources of cartel size variation. Results show that by 2022, cartels counted 160,000–185,000 units, becoming a top employer. Recruiting at least 350 people per week is essential to avoid their collapse due to aggregate losses. Furthermore, we show that increasing incapacitation would increase both homicides and cartel members. Conversely, reducing recruitment could substantially curtail violence and lower cartel size., Data obtained from Plataforma de Proyección de Datos Abierta, was processed to obtain a network structure. https://ppdata.politicadedrogas.org/ Trends were produced by solving a set of differential equations., Datasets are in a CSV format. Code is available for RStudio or R.

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This dataset accompanies the study Crime Metrics in Ibiza: Alternative Models and the Impact of the Floating Population, which analyzes crime trends in Ibiza between 2019 and 2024. The dataset provides comprehensive information on crime rates, population fluctuations, and alternative methodologies for calculating crime incidence in a region characterized by strong seasonal variations. Traditional crime rates are usually calculated based on census population data, which does not account for temporary residents, tourists, or seasonal workers. To address this limitation, two alternative methodologies were applied, incorporating floating population estimates to refine crime rate calculations and provide a more accurate representation of criminal activity on the island.

The dataset is structured into multiple sheets, each containing specific variables related to crime and population estimates. It includes official census population data sourced from the Spanish National Statistics Institute (INE) and crime rates derived from these figures. Additionally, the dataset contains estimated figures for tourism accommodation, based on statistics from the Balearic Institute of Statistics (IBESTAT). Using these estimates, a floating population adjustment has been applied, which allows for a recalculated crime rate that considers the significant impact of tourism on the island’s demographics.

A second approach within the dataset estimates the population using urban waste production data, sourced from the Consell d’Eivissa. Since the amount of waste generated is closely linked to population density, this methodology provides an alternative way to estimate the real number of people present on the island at any given time. The crime rates have been recalculated accordingly, providing an additional perspective on the relationship between demographic fluctuations and crime trends.

The dataset is derived from multiple authoritative sources, including official crime statistics from the Spanish Ministry of the Interior, census population data from INE, and detailed tourism and accommodation figures from IBESTAT. The urban waste methodology is based on data provided by the Consell d’Eivissa, which records the volume of waste generated by municipalities on a yearly basis. By integrating these diverse data sources, the dataset offers a more precise and adaptable model for understanding crime dynamics in a tourism-dependent region.

The methodologies applied in this dataset highlight the importance of accounting for floating populations when analyzing crime rates. The traditional crime rate model, which only considers permanent residents, tends to overestimate crime levels in regions with large seasonal populations. The tourism-based adjustment method corrects this by incorporating official and unofficial accommodation figures, while the urban waste-based method offers an alternative approach by estimating the real-time population based on resource consumption. Both approaches reveal significant differences between conventional crime rates and adjusted figures, emphasizing the need for policymakers to adopt more refined methodologies when developing crime prevention strategies.

This dataset is released under the Creative Commons Attribution 4.0 (CC-BY 4.0) license, allowing for its free use, redistribution, and modification, provided that proper attribution is given. Researchers, policymakers, and criminologists are encouraged to use this dataset to further explore crime trends in tourism-heavy regions and to develop more accurate statistical models for crime analysis.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 11 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last SHR data they release. Changes .rda file to .rds.Version 10 release notes:Changes release notes description, does not change data.Version 9 release notes:Adds 2019 data.Version 8 release notes:Adds 2018 data.Changes source of data for years 1985-2018 to be directly from the FBI. 2018 data was received via email from the FBI, 2016-2017 is from the FBI who mailed me a DVD, and 1985-2015 data is from the FBI's Crime Data Explorer site (https://crime-data-explorer.fr.cloud.gov/downloads-and-docs).Adds .csv version of the data.Makes minor changes to value labels for consistency and to fix grammar. Version 7 release notes:Changes project name to avoid confusing this data for the ones done by NACJD.Version 6 release notes:Adds 2017 data.Version 5 release notes:Adds 2016 data.Standardizes the "group" column which categorizes cities and counties by population.Arrange rows in descending order by year and ascending order by ORI. Version 4 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. Version 3 Release Notes:Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Change column names for relationship variables from offender_n_relation_to_victim_1 to victim_1_relation_to_offender_n to better indicate that all relationship are victim 1's relationship to each offender. Reorder columns.This is a single file containing all data from the Supplementary Homicide Reports from 1976 to 2018. The Supplementary Homicide Report provides detailed information about the victim, offender, and circumstances of the murder. Details include victim and offender age, sex, race, ethnicity (Hispanic/not Hispanic), the weapon used, circumstances of the incident, and the number of both offenders and victims. Years 1976-1984 were downloaded from NACJD, while more recent years are from the FBI. All files came as ASCII+SPSS Setup files and were cleaned using R. The "cleaning" just means that column names were standardized (different years have slightly different spellings for many columns). Standardization of column names is necessary to stack multiple years together. Categorical variables (e.g. state) were also standardized (i.e. fix spelling errors, have terminology be the same across years). The following is the summary of the Supplementary Homicide Report copied from ICPSR's 2015 page for the data.The Uniform Crime Reporting Program Data: Supplementary Homicide Reports (SHR) provide detailed information on criminal homicides reported to the police. These homicides consist of murders; non-negligent killings also called non-negligent manslaughter; and justifiable homicides. UCR Program contributors compile and submit their crime data by one of two means: either directly to the FBI or through their State UCR Programs. State UCR Programs frequently impose mandatory reporting requirements which have been effective in increasing both the number of reporting agencies as well as the number and accuracy of each participating agency's reports. Each agency may be identified by its numeric state code, alpha-numeric agency ("ORI") code, jurisdiction population, and population group. In addition, each homicide incident is identified by month of occurrence and situation type, allowing flexibility in creating aggregations and subsets.

Analysis of ‘Hate Crimes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/48b29dad-d452-4b28-b601-43790d5c0685 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Information from Bloomington Police Department cases where a hate or bias crime has been reported.

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.

--- Original source retains full ownership of the source dataset ---

This study surveyed immigrant and non-immigrant populations residing in high Latino population communities in order to: Assess the nature and pattern of bias motivated victimization. Explore the co-occurrence of bias motivated victimization with other forms of victimization. Measure reporting and help-seeking behaviors of individuals who experience bias motivated victimization. Identify cultural factors which may contribute to the risk of bias victimization. Evaluate the effect of bias victimization on negative psychosocial outcomes relative to other forms of victimization. The study's sample was a community sample of 910 respondents which included male and female Latino adults across three metropolitan areas within the conterminous United States. These respondents completed the survey in one of two ways. One set of respondents completed the survey on a tablet with the help of the research team, while the other group self-administered the survey on their own mobile device. The method used to complete the survey was randomly selected. A third option (paper and pencil with an administrator) was initially included but was removed early in the survey's deployment. The survey was administered from May 2018 to March 2019 in the respondent's preferred language (English or Spanish). This collection contains 1,620 variables, and includes derived variables for several scales used in the questionnaire. Bias victimization measures considered both hate crimes (e.g. physical assault) and non-criminal bias events (e.g. racial slurs) and allowed the respondent to report multiple incidents, perpetrators, and types of bias victimization. The respondents were asked about their help-seeking and reporting behaviors for the experience of bias victimization they considered to be the most severe and the measures considered both formal (e.g. contacting the police) and informal (e.g. communicating with family) help-seeking behaviors. The victimization scale measured exposure to traumatic events (e.g. witnessing a murder) as well as experiences of victimization (e.g. physical assault). Acculturation and enculturation scales measured topics such as the respondent's use of Spanish and English and their consumption of media in both languages. The variables pertaining to acculturative stress considered factors such as feelings of social isolation, experiences of racism, and conflict with family members. The variables for mental health outcomes measured symptoms of anger, anxiety, depression, and disassociation.

The purpose of the study was to assess the impact of Latino ethnicity on pretrial release decisions in large urban counties. The study examined two questions: Are Latino defendants less likely to receive pretrial releases than non-Latino defendants? Are Latino defendants in counties where the Latino population is rapidly increasing less likely to receive pretrial releases than Latino defendants in counties where the Latino population is not rapidly increasing? The study utilized the State Court Processing Statistics (SCPS) Database (see STATE COURT PROCESSING STATISTICS, 1990-2004: FELONY DEFENDANTS IN LARGE URBAN COUNTIES [ICPSR 2038]). The SCPS collects data on felony cases filed in state courts in 40 of the nation's 75 largest counties over selected sample dates in the month of May of every even numbered year, and tracks a representative sample of felony case defendants from arrest through sentencing. Data in the collection include 118,556 cases. Researchers supplemented the SCPS with county-level information from several sources: Federal Bureau of Investigation Uniform Crime Reporting Program county-level data series of index crimes reported to the police for the years 1988-2004 (see UNIFORM CRIME REPORTS: COUNTY-LEVEL DETAILED ARREST AND OFFENSE DATA, 1998 [ICPSR 9335], UNIFORM CRIME REPORTING PROGRAM DATA [UNITED STATES]: COUNTY-LEVEL DETAILED ARREST AND OFFENSE DATA, 1990 [ICPSR 9785], 1992 [ICPSR 6316], 1994 [ICPSR 6669], 1996 [ICPSR 2389], 1998 [ICPSR 2910], 2000 [ICPRS 3451], 2002 [ICPSR 4009], and 2004 [ICPSR 4466]). Bureau of Justice Statistics Annual Survey of Jails, Jurisdiction-Level data series for the years 1988-2004 (see ANNUAL SURVEY OF JAILS: JURISDICTION-LEVEL DATA, 1990 [ICPSR 9569], 1992 [ICPSR 6395], 1994 [ICPSR 6538], 1996 [ICPSR 6856], 1998 [ICPSR 2682], 2000 [ICPSR 3882], 2002 [ICPSR 4428], and 2004 [ICPSR 20200]). Bureau of Justice Statistics National Prosecutors Survey/Census data series 1990-2005 (see NATIONAL PROSECUTORS SURVEY, 1990 [ICPSR 9579], 1992 [ICPSR 6273], 1994 [ICPSR 6785], 1996 [ICPSR 2433], 2001 census [ICPSR 3418], and 2005 [ICPSR 4600]). United States Census Bureau State and County Quickfacts. National Center for State Courts, State Court Organization reports, 1993 (see NCJ 148346), 1998 (see NCJ 178932), and 2004 (see NCJ 212351). Bureau of Justice Statistics Felony Defendants in Large Urban Counties reports, 1992 (see NCJ 148826), 1994 (see NCJ 164616), 1996 (see NCJ 176981), 1998 (see NJC 187232), 2000 (see NCJ 202021), and 2002 (see NJC 210818). The data include defendant level variables such as most serious current offense charge, number of charges, prior felony convictions, prior misdemeanor convictions, prior incarcerations, criminal justice status at arrest, prior failure to appear, age, gender, ethnicity, and race. County level variables include region, crime rate, two year change in crime rate, caseload rate, jail capacity, two year change in jail capacity, judicial selection by election or appointment, prosecutor screens cases, and annual expenditure on prosecutor's office. Racial threat stimuli variables include natural log of the percentage of the county population that is Latino, natural log of the percentage of the county population that is African American, change in the percentage of the county population that is Latino over the last six years and change in the percentage of the county population that is African American over the last six years. Cross-level interaction variables include percentage minority (Latino/African American) population zero percent to 15 percent, percentage minority (Latino/African American) population 16 percent to 30 percent, and percentage minority (Latino/African American) population 31 percent or higher.

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

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.

Brown, Ryan, Montalva, Verónica, Thomas, Duncan, and Velásquez, Andrea, (2019) "Impact of Violent Crime on Risk Aversion: Evidence from the Mexican Drug War." Review of Economics and Statistics 101:5, 892-904.

This dataset is the main survey created for the publication "Past, Present, and Future of Crime and Violence Observations in Latin America and the Caribbean" (Related Publication Only Available in Spanish). This study examines the creation and evolution of crime and violence observatories in Latin America and the Caribbean, starting from the early 1990s. It explores their historical and conceptual development, maps out 66 observatories across 18 countries, and provides recommendations for their improved design, monitoring, and evaluation. The goal is to enhance their effectiveness in supporting public policies aimed at reducing crime and violence in the region.​

Since 1976, the United States has witnessed a steady and precipitous decline in intimate partner homicides. This study builds on the work of Dugan et al. (1999, 2000) and Browne and Williams (1989) by examining, in greater detail, the relationship between intimate partner homicide and gender, race, criminal justice system response, and domestic violence services. Specifically, the study examines the net effect of criminal justice system response and federally-funded domestic violence shelters on victimization of white, African American, and Hispanic males and females. This study used aggregated data from the 58 counties in California from 1987 to 2000. Homicide data were gathered by the State of California Department of Justice, Criminal Justice Statistics Center. Data on domestic violence resources were obtained from the Governor's Office of Criminal Justice Planning, Domestic Violence Branch, in the form of detailed reports from domestic violence shelters in the state. Based on these records, the researchers computed the number of federally-funded shelter-based organizations in a given county over time. Data on criminal justice responses at the county level were gathered from the State of California Department of Justice, Criminal Justice Statistics Center. These data included domestic violence arrests and any convictions and incarceration that followed those arrests. The researchers disaggregated these criminal justice system measures by race and gender. In order to account for population differences and changes over time, rates were computed per 100,000 adults (age 18 and older).