Uniform Crime Reporting (UCR) Program Data: Arrests by Age, Sex, and Race, 1980-2016
Principal Investigator(s): View help for Principal Investigator(s) Jacob Kaplan, University of Pennsylvania
Version: View help for Version V3
Name | File Type | Size | Last Modified |
---|---|---|---|
asr_1980_2016_csv.zip | application/zip | 322.6 MB | 07/08/2018 06:09:PM |
asr_1980_2016_dta.zip | application/zip | 471.3 MB | 07/08/2018 05:34:PM |
asr_1980_2016_rda.zip | application/zip | 441.1 MB | 07/08/2018 05:20:PM |
asr_1980_2016_sav.zip | application/zip | 475.7 MB | 07/08/2018 05:20:PM |
Project Citation:
Kaplan, Jacob. Uniform Crime Reporting (UCR) Program Data: Arrests by Age, Sex, and Race, 1980-2016. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2018-07-08. https://doi.org/10.3886/E102263V3
Project Description
Summary:
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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 0: 10000, 20000, 30000, 40000, 50000, 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:
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:
Index Crimes
If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.
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 0: 10000, 20000, 30000, 40000, 50000, 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 Male
- Total Female
- Total Juvenile
- Total Adult
- Total Arrests
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:
Index Crimes
- Murder
- Rape
- Robbery
- Aggravated Assault
- Burglary
- Theft
- Motor Vehicle Theft
- Arson
- Total Drugs
- Sale Drugs
- Possession Drugs
- Possession Cannabis
- Sale Cannabis
- Possession Cocaine
- Sale Cocaine
- Sale Other Drug
- Possession Addicting Narcotic
- Sale Addicting Narcotic
- DUI
- Liquor Laws
- Drunkenness
- Forgery
- Fraud
- Embezzlement
- Gambling Total
- Other Gambling
- Bookmaking
- Number Lottery
- Murder
- Rape
- Robbery
- Aggravated Assault
- Other Assault
- Weapons
- Prostitution
- Non-Rape Sex Offense
- Manslaughter by Negligence
- Vandalism
- Family Offenses
- Disorderly Conduct
- Other Non-Traffic
- Curfew
- Stolen Property
- Vagrancy
- Runaway
- Suspicion
- This data set has every crime and only the arrest categories that I created (see above).
If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.
Scope of Project
Subject Terms:
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arrest;
arrest rates;
Uniform Crime Reports;
FBI;
UCR;
crime;
crime statistics;
arrest statistics
Geographic Coverage:
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United States
Time Period(s):
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1980 – 2015
Universe:
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Police agencies in the United States
Data Type(s):
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administrative records data
Methodology
Data Source:
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United States Department of Justice. Federal Bureau of Investigation
Unit(s) of Observation:
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Police Agency
Geographic Unit:
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Police agency jurisdiction
Related Publications
This study is un-published. See below for other available versions.
Published Versions
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