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Uniform Crime Reporting Program Data: Offenses Known and Clearances by Arrest, 1960-2016
Principal Investigator(s): View help for Principal Investigator(s) Jacob Kaplan, University of Pennsylvania
Version: View help for Version V5
| Name | File Type | Size | Last Modified |
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application/zip | 316.8 MB | 05/29/2018 04:58:AM |
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application/zip | 451.7 MB | 05/28/2018 07:29:PM |
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application/zip | 408.6 MB | 05/28/2018 07:23:PM |
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application/zip | 460.6 MB | 05/28/2018 07:11:PM |
Project Citation:
Kaplan, Jacob. Uniform Crime Reporting Program Data: Offenses Known and Clearances by Arrest, 1960-2016. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2018-05-29. https://doi.org/10.3886/E100707V5
Project Description
Summary:
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Summary: This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. Each zip file contains monthly and yearly data files. The monthly files contain one data file per year (57 total, 1960-2016) as well as a codebook for each year. These files have been read into R using the ASCII and setup files from ICPSR (or from the FBI for 2016 data) using the package asciiSetupReader. The end of the zip folder's name says what data type (R, SPSS, SAS, Microsoft Excel CSV, Stata) the data is in.
The files are lightly cleaned. What this means specifically is that column names and value labels are standardized. In the original data column names were different between years (e.g. the December burglaries cleared column is "DEC_TOT_CLR_BRGLRY_TOT" in 1975 and "DEC_TOT_CLR_BURG_TOTAL" in 1977). The data here have standardized columns so you can compare between years and combine years together. The same thing is done for values inside of columns. For example, the state column gave state names in some years, abbreviations in others. For the code uses to clean and read the data, please see my GitHub file here. https://github.com/jacobkap/crime_data/blob/master/R_code/offenses_known.R
The yearly data set is a single file with all years stacked together. It also contain far fewer descriptive columns about the agencies in an attempt to decrease file size. The data file is aggregated yearly and has already combined every year 1960-2016. For the code I used to do this, see here https://github.com/jacobkap/crime_data/blob/master/R_code/yearly_offenses_known.R.
If you find any mistakes in the data or have any suggestions, please email me at jkkaplan6@gmail.com
As a description of what UCR Offenses Known and Clearances By Arrest data contains, the following is copied from ICPSR's 2015 page for the data.
The Uniform Crime Reporting Program Data: Offenses Known and Clearances By Arrest dataset is a compilation of offenses reported to law enforcement agencies in the United States. Due to the vast number of categories of crime committed in the United States, the FBI has limited the type of crimes included in this compilation to those crimes which people are most likely to report to police and those crimes which occur frequently enough to be analyzed across time. Crimes included are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. Much information about these crimes is provided in this dataset. The number of times an offense has been reported, the number of reported offenses that have been cleared by arrests, and the number of cleared offenses which involved offenders under the age of 18 are the major items of information collected.
V5 release notes:
- Changes the word "larceny" to "theft" in column names - eg. from "act_larceny" to "act_theft."
- Fixes bug where state abbrebation was NA for Washington D.C., Puerto Rico, Guam, and the Canal Zone.
- Fixes bug where officers_killed_by_accident was not appearing in yearly data. Note that 1979 does not have any officers killed (by felony or accident) or officers assaulted data.
- Adds aggravated assault columns to the monthly data. Aggravated assault is the sum of all assaults other than simple assault (assaults using gun, knife, hand/feet, and other weapon). Note that summing all crime columns to get a total crime count will double count aggravated assault as it is already the sum of existing columns.
- Reorder columns to put all month descriptors (e.g. "jan_month_included", "jan_card_1_type") before any crime data.
- Due to extremely irregular data in the unfounded columns for New Orleans (ORI = LANPD00) for years 2014-2016, I have change all unfounded column data for New Orleans for these years to NA. As an example, New Orleans reported about 45,000 unfounded total burglaries in 2016 (the 3rd highest they ever reported). This is 18 times largest than the number of actual total burglaries they reported that year (2,561) and nearly 8 times larger than the next largest reported unfounded total burglaries in any agency or year. Prior to 2014 there were no more than 10 unfounded total burglaries reported in New Orleans in any year.
- There were 10 obvious data entry errors in officers killed by felony/accident that I changed to NA.
- In 1974 the agency "Boston" (ORI = MA01301) reported 23 officers killed by accident during November.
- In 1978 the agency "Pittsburgh" (ORI = PAPPD00) reported 576 officers killed by accident during March.
- In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during April.
- In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during June.
- In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during April.
- In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during June.
- In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by accident during May.
- In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by felony during May.
- In 1996 the agency "Ruston" in Louisiana (ORI = LA03102) reported 30 officers killed by felony during September.
- In 1997 the agency "Washington University" in Missouri (ORI = MO0950E) reported 26 officers killed by felony during March.
V4 release notes:
- Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.
- Makes all column names lowercase.
- Change some variable names
- Makes values in character columns lowercase.
- Adds months_reported variable to yearly data.
- Combines monthly and yearly files into a single zip file (per data type).
- fixes a bug in Version 2 where 1993 data did not properly deal with missing values, leading to enormous counts of crime being reported.
Summary: This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. Each zip file contains monthly and yearly data files. The monthly files contain one data file per year (57 total, 1960-2016) as well as a codebook for each year. These files have been read into R using the ASCII and setup files from ICPSR (or from the FBI for 2016 data) using the package asciiSetupReader. The end of the zip folder's name says what data type (R, SPSS, SAS, Microsoft Excel CSV, Stata) the data is in.
The files are lightly cleaned. What this means specifically is that column names and value labels are standardized. In the original data column names were different between years (e.g. the December burglaries cleared column is "DEC_TOT_CLR_BRGLRY_TOT" in 1975 and "DEC_TOT_CLR_BURG_TOTAL" in 1977). The data here have standardized columns so you can compare between years and combine years together. The same thing is done for values inside of columns. For example, the state column gave state names in some years, abbreviations in others. For the code uses to clean and read the data, please see my GitHub file here. https://github.com/jacobkap/crime_data/blob/master/R_code/offenses_known.R
The yearly data set is a single file with all years stacked together. It also contain far fewer descriptive columns about the agencies in an attempt to decrease file size. The data file is aggregated yearly and has already combined every year 1960-2016. For the code I used to do this, see here https://github.com/jacobkap/crime_data/blob/master/R_code/yearly_offenses_known.R.
If you find any mistakes in the data or have any suggestions, please email me at jkkaplan6@gmail.com
As a description of what UCR Offenses Known and Clearances By Arrest data contains, the following is copied from ICPSR's 2015 page for the data.
The Uniform Crime Reporting Program Data: Offenses Known and Clearances By Arrest dataset is a compilation of offenses reported to law enforcement agencies in the United States. Due to the vast number of categories of crime committed in the United States, the FBI has limited the type of crimes included in this compilation to those crimes which people are most likely to report to police and those crimes which occur frequently enough to be analyzed across time. Crimes included are criminal homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft. Much information about these crimes is provided in this dataset. The number of times an offense has been reported, the number of reported offenses that have been cleared by arrests, and the number of cleared offenses which involved offenders under the age of 18 are the major items of information collected.
Scope of Project
Subject Terms:
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crime;
UCR;
murder;
police;
Uniform Crime Reports;
FBI;
crime rates;
crime reporting;
arrests;
offenses;
sex offenses;
law enforcement;
law enforcement agencies;
violent crime statistics;
crime statistics
Geographic Coverage:
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United States
Time Period(s):
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1960 – 2016
Universe:
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Crimes committed, declared unfounded, and cleared in police agencies in the United States during 1960-2016.
Data Type(s):
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administrative records data;
aggregate 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
Published Versions
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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.