Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Property Stolen and Recovered (Supplement to Return A) 1960-2017
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 |
---|---|---|---|
ucr_property_stolen_recovered_monthly_1960_2017_csv.zip | application/zip | 504.4 MB | 07/15/2019 08:51:PM |
ucr_property_stolen_recovered_monthly_1960_2017_dta.zip | application/zip | 653.7 MB | 07/15/2019 09:11:PM |
ucr_property_stolen_recovered_monthly_1960_2017_rda.zip | application/zip | 523.6 MB | 07/15/2019 09:05:PM |
ucr_property_stolen_recovered_yearly_1960_2017_csv.zip | application/zip | 75.2 MB | 07/16/2019 04:23:AM |
ucr_property_stolen_recovered_yearly_1960_2017_dta.zip | application/zip | 97.8 MB | 07/15/2019 08:53:PM |
ucr_property_stolen_recovered_yearly_1960_2017_rda.zip | application/zip | 84.9 MB | 07/15/2019 08:52:PM |
Project Citation:
Kaplan, Jacob. Jacob Kaplan’s Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Property Stolen and Recovered (Supplement to Return A) 1960-2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-07-16. https://doi.org/10.3886/E105403V3
Project Description
Summary:
View help for Summary
For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.
Version 3 release notes:
Version 3 release notes:
- Adds data in the following formats: Excel.
- Changes project name to avoid confusing this data for the ones done by NACJD.
Version 2 release notes:
- Adds data for 2017.
- Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.
Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery).
The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported).
All the data was received directly from the FBI as text or .DTA files. I created a setup file based on the documentation provided by the FBI and read the data 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. The Word document file available for download is the guidebook the FBI
provided with the raw data which I used to create the setup file to read
in data.
There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000,
40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."
For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.
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. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.
Scope of Project
Subject Terms:
View help for Subject Terms
stolen property;
stolen property recovery;
recovered property;
larceny;
theft;
property crime statistics;
crime statistics;
Uniform Crime Reports;
UCR
Geographic Coverage:
View help for Geographic Coverage
United States
Time Period(s):
View help for Time Period(s)
1960 – 2017
Data Type(s):
View help for Data Type(s)
administrative records data;
aggregate data
Methodology
Data Source:
View help for Data Source
Federal Bureau of Investigation
Collection Mode(s):
View help for Collection Mode(s)
self-enumerated questionnaire
Unit(s) of Observation:
View help for Unit(s) of Observation
Police agency
Geographic Unit:
View help for Geographic Unit
City
Related Publications
This study is un-published. See below for other available versions.
Published Versions
Report a Problem
Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.
This material is distributed exactly as it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.