DOI: 10.3886/e103500v12-188101
title: Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Hate Crime Data 1991-2024

description: <b>!!!WARNING~~~This dataset has a large number of flaws and is unable to properly answer many questions that people generally use it to answer, such as whether national hate crimes are changing (or at least they use the data so improperly that they get the wrong answer). A large number of people using this data (academics, advocates, reporting, US Congress) do so inappropriately and get the wrong answer to their questions as a result. Indeed, many published papers using this data should be retracted. Before using this data I highly recommend that you thoroughly read my book on UCR data, particularly the chapter on hate crimes (</b><b>https://ucrbook.com/hate-crimes.html</b><b>) as well as the FBI's own manual on this data. The questions you could potentially answer well are relatively narrow and generally exclude any causal relationships. </b><b>~~~</b><b>WARNING!!!</b><br><b>For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.com<br></b><br><b>Version 12 release notes:</b>Adds .parquet file format<b>Version 11 release notes:</b>Adds 2023-2024 data<b>Version 10 release notes:</b>Adds 2022 data<br><b>Version 9 release notes:<br></b>Adds 2021 data.<br><b>Version 8 release notes:</b>Adds 2019 and 2020 data. <br>Please
 note that the FBI has retired UCR data ending in 2020 data so this will
 be the last UCR hate crime data they release. <br>Changes .rda file to .rds.<b>Version 7 release notes:</b>Changes release notes description, does not change data.<b>Version 6 release notes:</b><b></b>Adds 2018 data<br><b>Version 5 release notes:<br></b>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. <br><b></b><b>Version 4 release notes: <br></b>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. <br>Made the 'population' column which is the total population in that agency. <br><b>Version 3 release notes: <b><b><br></b></b></b>Adds data for 2016.Order rows by year (descending) and ORI.<b>Version 2 release notes: <b><br></b></b>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.<br><br>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. <br>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.). <br><br>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), made all character values lower case, reordered columns. I also generated incident month, weekday, and month-day variables from the incident date variable included in the original data.<br><br>
description: Smallest Geographic Unit: police agency