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Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Property Stolen and Recovered (Supplement to Return A) 1960-2017

Name: Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Property Stolen and Recovered (Supplement to Return A) 1960-2017
Published: 2018-08-14
License: https://creativecommons.org/licenses/by/4.0

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:

For any questions about this data please email me at [email protected]. If you use this data, please cite it.

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: stolen property; stolen property recovery; recovered property; larceny; theft; property crime statistics; crime statistics; Uniform Crime Reports; UCR

Geographic Coverage: United States

Time Period(s): 1960 – 2017

Data Type(s): administrative records data; aggregate data

Methodology

Data Source: Federal Bureau of Investigation

Collection Mode(s): self-enumerated questionnaire

Unit(s) of Observation: Police agency

Geographic Unit: City

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