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: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.

Version 4 release notes:I am retiring this dataset - please do not use it. The reason that I made this dataset is that I had seen a lot of recent articles using the NACJD version of the data and had several requests that I make a concatenated version myself. This data is heavily flawed as noted in the excellent Maltz & Targonski's (2002) paper (see PDF available to download here and important paragraph from that article below) and I was worried that people were using the data without considering these flaws. So the data available here had the warning below this section (originally at the top of these notes so it was the most prominent thing) and had the Maltz & Targonski PDF included in the zip file so people were aware of it. There are two reasons that I am retiring it. First, I see papers and other non-peer reviewed reports still published using this data without addressing the main flaws noted by Maltz and Targonski. I don't want to have my work contribute to research that I think is fundamentally flawed. Second, this data is actually more flawed that I originally understood. The imputation process to replace missing data is based off of a bad design, and Maltz and Targonski talk about this in detail so I won't discuss it too much. The additional problem is that the variable that determines whether an agency has missing data is fatally flawed. That variable is the "number_of_months_reported" variable which is actually just the last month reported. So if you only report in December it'll have 12 months reported instead of 1. So even a good imputation process will be based on such a flawed measure of missingness that it will be wrong. How big of an issue is this? At the moment I haven't looked into it in enough detail to be sure but it's enough of a problem that I no longer want to release this kind of data (within the UCR data there are variables that you can use to try to determine the actual number of months reported but that stopped being useful due to a change in the data in 2018 by the FBI. And even that measure is not always accurate for years before 2018.).!!! Important Note: There are a number of flaws in the imputation process to make these county-level files. Included as one of the files to download (and also in every zip file) is Maltz & Targonski's 2002 paper on these flaws and why they are such an issue. I very strongly recommend that you read this paper in its entirety before working on this data. I am only publishing this data because people do use county-level data anyways and I want them to know of the risks. Important Note !!!The following paragraph is the abstract to Maltz & Targonski's paper: County-level crime data have major gaps, and the imputation schemes for filling in the gaps are inadequate and inconsistent. Such data were used in a recent study of guns and crime without considering the errors resulting from imputation. This note describes the errors and how they may have affected this study. Until improved methods of imputing county-level crime data are developed, tested, and implemented, they should not be used, especially in policy studies.Version 3 release notes: Adds a variable to all data sets indicating the "coverage" which is the proportion of the agencies in that county-year that report complete data (i.e. that aren't imputed, 100 = no imputation, 0 = all agencies imputed for all months in that year.). Thanks to Dr. Monica Deza for the suggestion. The following is directly from NACJD's codebook for county data and is an excellent explainer of this variable.The Coverage Indicator variable represents the proportion of county data that is reported for a given year. The indicator ranges from 0 to 100. A value of 0 indicates that no data for the county were reported and all data have been imputed. A value of 100 indicates that all ORIs in the county reported for all 12 months in the year. Coverage Indicator is calculated as follows: CI_x = 100 * ( 1 - SUM_i { [ORIPOP_i/COUNTYPOP] * [ (12 - MONTHSREPORTED_i)/12 ] } ) where CI = Coverage Indicator x = county i = ORI within countyReorders data so it's sorted by year then county rather than vice versa as before.Version 2 release notes: Fixes bug where Butler University (ORI = IN04940) had wrong FIPS state and FIPS state+county codes from the LEAIC crosswa

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 4 release notes:Adds data for 2018Version 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.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 11 release notes:Adds 2023-2024 dataAdds parquet file formatVersion 10 release notes:Adds 2022 dataVersion 9 release notes:Adds 2021 data.Version 8 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Property Stolen and Recovered data they release. Changes .rda file to .rds.Version 7 release notes:Adds data for 2006.Version 6 release notesChanges release notes description, does not change data.Version 5 release notes:Adds data for 2019Note that the number of months reported variable sharply changes starting in 2018. This is probably due to changes in UCR reporting of the "status" variable which is used to generate the months missing county (the code I used does not change). So pre-2018 and 2018+ years may not be comparable for this variable. Version 4 release notes:Adds data for 2018Version 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. 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.