DOI: 10.3886/e102263v4-7941
title: Uniform Crime Reporting (UCR) Program Data: Arrests by Age, Sex, and Race, 1980-2016

description: Version 4 release notes: <br>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. <br>Version 3 release notes: <br>Add data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: <br>Fix bug where Philadelphia Police Department had incorrect FIPS county code. <br>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. <br>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.<br><br>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 0: 10000, 20000, 30000, 40000, 50000, 99999, 99998. <br><br>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. <br><br>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. <br><br>I created 9 arrest categories myself. The categories are: <br>Total Male JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the data are just agency identifier columns). Because this "simple" data set need fewer columns, I include all offenses.<br><br>As the arrest data is very granular, and each category of arrest is its own column, there are dozens of columns per crime. To keep the data somewhat manageable, there are six different files, five which contain different crimes and the "simple" file. Each file contains the data for all years. The five categories each have crimes belonging to a major crime category and do not overlap in crimes other than the index crimes and the violent/sex crimes categories. Please note that the crime names provided below are not the same as the column names in the data. Due to Stata limiting column names to 32 characters maximum, I have abbreviated the crime names in the data. The files and their including crimes are:<br><br>Index Crimes<br>MurderRapeRobberyAggravated AssaultBurglaryTheftMotor Vehicle TheftArson<br>Drug and Liquor Crimes<br>Total DrugsSale DrugsPossession DrugsPossession CannabisSale CannabisPossession Heroin or CocaineSale Heroin or CocaineSale Other DrugPossession Synthetic NarcoticSale Synthetic NarcoticDUILiquor LawsDrunkenness<br>Financial Crimes<br>ForgeryFraudEmbezzlementGambling Total<br>Other GamblingBookmakingNumber Lottery<br>Violent or Sex Crimes<br>MurderRapeRobberyAggravated AssaultOther AssaultWeaponsProstitutionNon-Rape Sex OffenseManslaughter by Negligence<br>Other Crimes<br>VandalismFamily OffensesDisorderly ConductOther Non-TrafficCurfewStolen PropertyVagrancyRunawaySuspicionSimple<br>This data set has every crime and only the arrest categories that I created (see above). <br>If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.<br>
description: Police agencies in the United States<br>Smallest Geographic Unit: Police agency jurisdiction