Key Features

How to use this dataset

1. Access Case Details: Explore the descriptions and details of the cases associated with each wanted person. This can help you understand the alleged crimes, background information, and the current status of the cases.

2. Data Analysis: Perform data analysis to identify patterns or trends among wanted persons. You can analyze the data to gain insights into the demographics, crimes, or locations associated with these individuals.

3. Geospatial Analysis: Utilize geographical information to map out the locations related to wanted persons. This can help in visualizing the distribution of cases across different regions.

4. Raise Awareness: Share information about wanted persons with your community or through social media. The dataset can be used to raise awareness and assist in locating and apprehending fugitives.

If you find this dataset useful, give it an upvote – it's a small gesture that goes a long way! Thanks for your support. 😄

Context

The Uniform Crime Reporting (UCR) Program has been the starting place for law enforcement executives, students of criminal justice, researchers, members of the media, and the public at large seeking information on crime in the nation. The program was conceived in 1929 by the International Association of Chiefs of Police to meet the need for reliable uniform crime statistics for the nation. In 1930, the FBI was tasked with collecting, publishing, and archiving those statistics.

Today, four annual publications, Crime in the United States, National Incident-Based Reporting System, Law Enforcement Officers Killed and Assaulted, and Hate Crime Statistics are produced from data received from over 18,000 city, university/college, county, state, tribal, and federal law enforcement agencies voluntarily participating in the program. The crime data are submitted either through a state UCR Program or directly to the FBI’s UCR Program.

This dataset focuses on the crime rates and law enforcement employment data in the state of California.

Content

Crime and law enforcement employment rates are separated into individual files, focusing on offenses by enforcement agency, college/university campus, county, and city. Categories of crimes reported include violent crime, murder and nonnegligent manslaughter, rape, robbery, aggravated assault, property crime, burglary, larceny-theft, motor vehicle damage, and arson. In the case of rape, data is collected for both revised and legacy definitions. In some cases, a small number of enforcement agencies switched definition collection sometime within the same year.

Acknowledgements

This dataset originates from the FBI UCR project, and the complete dataset for all 2015 crime reports can be found here.

Inspiration

• What are the most common types of crimes in California? Are there certain crimes that are more common in a particular place category, such as a college/university campus, compared to the rest of the state?
• How does the number of law enforcement officers compare to the crime rates of a particular area? Is the ratio similar throughout the state, or do certain campuses, counties, or cities have a differing rate?
• How does the legacy vs. refined definition of rape differ, and how do the rape counts compare? If you pulled the same data from FBI datasets for previous years, can you see a difference in rape rates over time?

Law Enforcement Locations Any location where sworn officers of a law enforcement agency are regularly based or stationed. Law Enforcement agencies "are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers". This is the definition used by the US Department of Justice - Bureau of Justice Statistics (DOJ-BJS) for their Law Enforcement Management and Administrative Statistics (LEMAS) survey. Although LEMAS only includes non Federal Agencies, this dataset includes locations for federal, state, local, and special jurisdiction law enforcement agencies. Law enforcement agencies include, but are not limited to, municipal police, county sheriffs, state police, school police, park police, railroad police, federal law enforcement agencies, departments within non law enforcement federal agencies charged with law enforcement (e.g., US Postal Inspectors), and cross jurisdictional authorities (e.g., Port Authority Police). In general, the requirements and training for becoming a sworn law enforcement officer are set by each state. Law Enforcement agencies themselves are not chartered or licensed by their state. County, city, and other government authorities within each state are usually empowered by their state law to setup or disband Law Enforcement agencies. Generally, sworn Law Enforcement officers must report which agency they are employed by to the state. Although TGS's intention is to only include locations associated with agencies that meet the above definition, TGS has discovered a few locations that are associated with agencies that are not publicly funded. TGS deleted these locations as we became aware of them, but some may still exist in this dataset. Personal homes, administrative offices, and temporary locations are intended to be excluded from this dataset; however, some personal homes are included due to the fact that the New Mexico Mounted Police work out of their homes. TGS has made a concerted effort to include all local police; county sheriffs; state police and/or highway patrol; Bureau of Indian Affairs; Bureau of Land Management; Bureau of Reclamation; U.S. Park Police; Bureau of Alcohol, Tobacco, Firearms, and Explosives; U.S. Marshals Service; U.S. Fish and Wildlife Service; National Park Service; U.S. Immigration and Customs Enforcement; and U.S. Customs and Border Protection. This dataset is comprised completely of license free data. FBI entities are intended to be excluded from this dataset, but a few may be included. The Law Enforcement dataset and the Correctional Institutions dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. With the merge of the Law Enforcement and the Correctional Institutions datasets, the NAICS Codes & Descriptions were assigned based on the facility's main function which was determined by the entity's name, facility type, web research, and state supplied data. In instances where the entity's primary function is both law enforcement and corrections, the NAICS Codes and Descriptions are assigned based on the dataset in which the record is located (i.e., a facility that serves as both a Sheriff's Office and as a jail is designated as [NAICSDESCR]="SHERIFFS' OFFICES (EXCEPT COURT FUNCTIONS ONLY)" in the Law Enforcement layer and as [NAICSDESCR]="JAILS (EXCEPT PRIVATE OPERATION OF)" in the Correctional Institutions layer). Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard fields that TGS populated. Double spaces were replaced by single spaces in these same fields. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on the values in this field, the oldest record dates from 08/14/2006 and the newest record dates from 10/23/2009

Investigator(s): Federal Bureau of Investigation Since 1930, the Federal Bureau of Investigation (FBI) has compiled the Uniform Crime Reports (UCR) to serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. With the 1977 data, the title was expanded to Uniform Crime Reporting Program Data. Each year, participating law enforcement agencies contribute reports to the FBI either directly or through their state reporting programs. ICPSR archives the UCR data as five separate components: (1) summary data, (2) county-level data, (3) incident-level data (National Incident-Based Reporting System [NIBRS]), (4) hate crime data, and (5) various, mostly nonrecurring, data collections. Summary data are reported in four types of files: (a) Offenses Known and Clearances by Arrest, (b) Property Stolen and Recovered, (c) Supplementary Homicide Reports (SHR), and (d) Police Employee (LEOKA) Data (Law Enforcement Officers Killed or Assaulted). The county-level data provide counts of arrests and offenses aggregated to the county level. County populations are also reported. In the late 1970s, new ways to look at crime were studied. The UCR program was subsequently expanded to capture incident-level data with the implementation of the National Incident-Based Reporting System. The NIBRS data focus on various aspects of a crime incident. The gathering of hate crime data by the UCR program was begun in 1990. Hate crimes are defined as crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity. In September 1994, disabilities, both physical and mental, were added to the list. The fifth component of ICPSR's UCR holdings is comprised of various collections, many of which are nonrecurring and prepared by individual researchers. These collections go beyond the scope of the standard UCR collections provided by the FBI, either by including data for a range of years or by focusing on other aspects of analysis. NACJD has produced resource guides on UCR and on NIBRS data.

Law Enforcement Locations:Any location where sworn officers of a law enforcement agency are regularly based or stationed. Law Enforcement agencies "are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers". This is the definition used by the US Department of Justice - Bureau of Justice Statistics (DOJ-BJS) for their Law Enforcement Management and Administrative Statistics (LEMAS) survey. Although LEMAS only includes non Federal Agencies, this dataset includes locations for federal, state, local, and special jurisdiction law enforcement agencies.

Law enforcement agencies include, but are not limited to, municipal police, county sheriffs, state police, school police, park police, railroad police, federal law enforcement agencies, departments within non law enforcement federal agencies charged with law enforcement (e.g., US Postal Inspectors), and cross jurisdictional authorities (e.g., Port Authority Police).

In general, the requirements and training for becoming a sworn law enforcement officer are set by each state. Law Enforcement agencies themselves are not chartered or licensed by their state. County, city, and other government authorities within each state are usually empowered by their state law to setup or disband Law Enforcement agencies. Generally, sworn Law Enforcement officers must report which agency they are employed by to the state.

Although TGS's intention is to only include locations associated with agencies that meet the above definition, TGS has discovered a few locations that are associated with agencies that are not publicly funded. TGS deleted these locations as we became aware of them, but some may still exist in this dataset.

Personal homes, administrative offices, and temporary locations are intended to be excluded from this dataset; however, some personal homes of constables are included due to the fact that many constables work out of their homes.

TGS has made a concerted effort to include all local police; county sheriffs; state police and/or highway patrol; Bureau of Indian Affairs; Bureau of Land Management; Bureau of Reclamation; U.S. Park Police; Bureau of Alcohol, Tobacco, Firearms, and Explosives; U.S. Marshals Service; U.S. Fish and Wildlife Service; National Park Service; U.S. Immigration and Customs Enforcement; and U.S. Customs and Border Protection.

This dataset is comprised completely of license free data.

FBI entities are intended to be excluded from this dataset, but a few may be included.

The Law Enforcement dataset and the Correctional Institutions dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes.

With the merge of the Law Enforcement and the Correctional Institutions datasets, the NAICS Codes & Descriptions were assigned based on the facility's main function which was determined by the entity's name, facility type, web research, and state supplied data. In instances where the entity's primary function is both law enforcement and corrections, the NAICS Codes and Descriptions are assigned based on the dataset in which the record is located (i.e., a facility that serves as both a Sheriff's Office and as a jail is designated as [NAICSDESCR]="SHERIFFS' OFFICES (EXCEPT COURT FUNCTIONS ONLY)" in the Law Enforcement layer and as [NAICSDESCR]="JAILS (EXCEPT PRIVATE OPERATION OF)" in the Correctional Institutions layer).

Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries.

"#" and "*" characters were automatically removed from standard fields that TGS populated. Double spaces were replaced by single spaces in these same fields.

Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results.

All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.

The currentness of this dataset is indicated by the [CONTDATE] field. Based on the values in this field, the oldest record dates from 12/07/2006 and the newest record dates from 10/23/2009Use Cases: 1. An assessment of whether or not the total police capability in a given area is adequate.

1. A list of resources to draw upon in surrounding areas when local resources have temporarily been overwhelmed by a disaster - route analysis can help to determine those entities who are able to respond the quickest.

2. A resource for emergency management planning purposes.

3. A resource for catastrophe response to aid in the retrieval of equipment by outside responders in order to deal with the disaster.

4. A resource for situational awareness planning and response for federal government events.

Law Enforcement Locations Any location where sworn officers of a law enforcement agency are regularly based or stationed. Law Enforcement agencies "are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers". This is the definition used by the US Department of Justice - Bureau of Justice Statistics (DOJ-BJS) for their Law Enforcement Management and Administrative Statistics (LEMAS) survey. Although LEMAS only includes non Federal Agencies, this dataset includes locations for federal, state, local, and special jurisdiction law enforcement agencies. Law enforcement agencies include, but are not limited to, municipal police, county sheriffs, state police, school police, park police, railroad police, federal law enforcement agencies, departments within non law enforcement federal agencies charged with law enforcement (e.g., US Postal Inspectors), and cross jurisdictional authorities (e.g., Port Authority Police). In general, the requirements and training for becoming a sworn law enforcement officer are set by each state. Law Enforcement agencies themselves are not chartered or licensed by their state. County, city, and other government authorities within each state are usually empowered by their state law to setup or disband Law Enforcement agencies. Generally, sworn Law Enforcement officers must report which agency they are employed by to the state. Although TGS's intention is to only include locations associated with agencies that meet the above definition, TGS has discovered a few locations that are associated with agencies that are not publicly funded. TGS deleted these locations as we became aware of them, but some may still exist in this dataset. Personal homes, administrative offices, and temporary locations are intended to be excluded from this dataset; however, some personal homes of constables are included due to the fact that many constables work out of their homes. TGS has made a concerted effort to include all local police; county sheriffs; state police and/or highway patrol; Bureau of Indian Affairs; Bureau of Land Management; Bureau of Reclamation; U.S. Park Police; Bureau of Alcohol, Tobacco, Firearms, and Explosives; U.S. Marshals Service; U.S. Fish and Wildlife Service; National Park Service; U.S. Immigration and Customs Enforcement; and U.S. Customs and Border Protection. This dataset is comprised completely of license free data. FBI entities are intended to be excluded from this dataset, but a few may be included. The Law Enforcement dataset and the Correctional Institutions dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. With the merge of the Law Enforcement and the Correctional Institutions datasets, the NAICS Codes & Descriptions were assigned based on the facility's main function which was determined by the entity's name, facility type, web research, and state supplied data. In instances where the entity's primary function is both law enforcement and corrections, the NAICS Codes and Descriptions are assigned based on the dataset in which the record is located (i.e., a facility that serves as both a Sheriff's Office and as a jail is designated as [NAICSDESCR]="SHERIFFS' OFFICES (EXCEPT COURT FUNCTIONS ONLY)" in the Law Enforcement layer and as [NAICSDESCR]="JAILS (EXCEPT PRIVATE OPERATION OF)" in the Correctional Institutions layer). Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard fields that TGS populated. Double spaces were replaced by single spaces in these same fields. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on the values in this field, the oldest record dates from 04/26/2006 and the newest record dates from 10/19/2009

!!!WARNING~~~This dataset has a large number of flaws and is unable to properly answer many questions that people generally use it to answer, such as whether national hate crimes are changing (or at least they use the data so improperly that they get the wrong answer). A large number of people using this data (academics, advocates, reporting, US Congress) do so inappropriately and get the wrong answer to their questions as a result. Indeed, many published papers using this data should be retracted. Before using this data I highly recommend that you thoroughly read my book on UCR data, particularly the chapter on hate crimes (https://ucrbook.com/hate-crimes.html) as well as the FBI's own manual on this data. The questions you could potentially answer well are relatively narrow and generally exclude any causal relationships. ~~~WARNING!!!For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 8 release notes:Adds 2019 and 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last UCR hate crime data they release. Changes .rda file to .rds.Version 7 release notes:Changes release notes description, does not change data.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes 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. Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds 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 Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), made all character values lower case, reordered columns. I also generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. The monthly zip 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, feather, Stata) the data is in. Due to file size limits on open ICPSR, not all file types were included for all the data.

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 zip files labeled "yearly" contain yearly data rather than monthly. These also contain far fewer descriptive columns about the agencies in an attempt to decrease file size. Each zip folder contains two files: a data file in whatever format you choose and a codebook. 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.

The Uniform Crime Reporting (UCR) Program has been the starting place for law enforcement executives, students of criminal justice, researchers, members of the media, and the public at large seeking information on crime in the nation. Part I categorizes incidents in two categories: violent and property crimes. Aggravated assault, forcible rape, murder, and robbery are classified as violent crime, while burglary, larceny-theft, and motor vehicle theft are classified as property crimes. This dataset contains FBI Uniform Crime Reporting (UCR) Part I crime data for the last 40 years in Greensboro, North Carolina. The crime rate or index is calculated on a per 100,000 resident basis.A crime rate describes the number of crimes reported to law enforcement agencies per 100,000 residents. A crime rate is calculated by dividing the number of reported crimes by the total population; the result is multiplied by 100,000. For example, in 2013 there were 496 robberies in Greensboro and the population was 268,176 according to the SBI estimate. This equals a robbery crime rate of 185 per 100,000 general population.496/268,176 = 0.00184953165085615 x 100,000 = 184.95The Greensboro Police Department is comprised of 787 sworn and non-sworn employees dedicated to the mission of partnering to fight crime for a safer Greensboro. We believe that effectively fighting crime requires everyone's effort. With your assistance, we can make our city safer. Wondering what you can do?Take reasonable steps to prevent being victimized. Lock your car and home doors. Be aware of your surroundings. If something or someonefeels out of the ordinary, go to a safe place.Be additional eyes and ears for us. Report suspicious or unusual activity, and provide tips through Crime Stoppers that can help solve crime.Look out for your neighbors. Strong communities with active Neighborhood Watch programs are not attractive to criminals. By taking care of the people around you, you can create safe places to live and work.Get involved! If you have children, teach them how to react to bullying, what the dangers of texting and driving are, and how to safely use the Internet. Talk with your older relatives about scams that target senior citizens.Learn more about GPD. Ride along with us. Participate in the Police Citizens' Academy. Volunteer, apply for an internship, or better yet join us.You may have heard about our philosophy of neighborhood-oriented policing. This is practice in policing that combines data-driven crime analysis with police/citizen partnerships to solve problems.In the spirit of partnership with the community, our goal is to make the Greensboro Police Department as accessible as possible to the people we serve. Policies and procedures, referred to as directives, are rules that all Greensboro Police Department employees must follow in carrying out the mission of the department. We will update the public copy of the directives in a timely manner to remain consistent with new policy and procedure updates.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 17 release notes:Adds data for 2020.Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Offenses Known and Clearances by Arrest data they release. Changes .rda files to .rds. Please note that in 2020 the card_actual_pt variable always returns that the month was reported. This causes 2020 to report that all months are reported for all agencies because I use the card_actual_pt variable to measure how many months were reported. This variable is almost certainly incorrect since it is extremely unlikely that all agencies suddenly always report. However, I am keeping this incorrect value to maintain a consistent definition of how many months are missing (measuring missing months through card_actual_type, for example, gives different results for previous years so I don't want to change this). Version 16 release notes:Changes release notes description, does not change data.Version 15 release notes:Adds data for 2019.Please note that in 2019 the card_actual_pt variable always returns that the month was reported. This causes 2019 to report that all months are reported for all agencies because I use the card_actual_pt variable to measure how many months were reported. This variable is almost certainly incorrect since it is extremely unlikely that all agencies suddenly always report. However, I am keeping this incorrect value to maintain a consistent definition of how many months are missing (measuring missing months through card_actual_type, for example, gives different results for previous years so I don't want to change this). Version 14 release notes:Adds arson data from the UCR's Arson dataset. This adds just the arson variables about the number of arson incidents, not the complete set of variables in that dataset (which include damages from arson and whether structures were occupied or not during the arson.As arson is an index crime, both the total index and the index property columns now include arson offenses. The "all_crimes" variables also now include arson.Adds a arson_number_of_months_missing column indicating how many months were not reporting (i.e. missing from the annual data) in the arson data. In most cases, this is the same as the normal number_of_months_missing but not always so please check if you intend to use arson data.Please note that in 2018 the card_actual_pt variable always returns that the month was reported. This causes 2018 to report that all months are reported for all agencies because I use the card_actual_pt variable to measure how many months were reported. This variable is almost certainly incorrect since it is extremely unlikely that all agencies suddenly always report. However, I am keeping this incorrect value to maintain a consistent definition of how many months are missing (measuring missing months through card_actual_type, for example, gives different results for previous years so I don't want to change this).For some reason, a small number of agencies (primarily federal agencies) had the same ORI number in 2018 and I removed these duplicate agencies. Version 13 release notes: Adds 2018 dataNew Orleans (ORI = LANPD00) data had more unfounded crimes than actual crimes in 2018 so unfounded columns for 2018 are all NA. Version 12 release notes: Adds population 1-3 columns - if an agency is in multiple counties, these variables show the population in the county with the most people in that agency in it (population_1), second largest county (population_2), and third largest county (population_3). Also adds county 1-3 columns which identify which counties the agency is in. The population column is the sum of the three population columns. Thanks to Mike Maltz for the suggestion!Fixes bug in the crosswalk data that is merged to this file that had the incorrect FIPS code for Clinton, Tennessee (ORI = TN00101). Thanks for Brooke Watson for catching this bug!Adds a last_month_reported column which says which month was reported last. This is actually how the FBI defines number_of_months_reported so is a more accurate representation of that. Removes the number_of_months_reported variable as the name is misleading. You should use the last_month_reported or the number_of_months_missing (see below) variable instead.Adds a number_of_months_missin

The UNIFORM CRIME REPORTING PROGRAM DATA: OFFENSES KNOWN AND CLEARANCES BY ARREST, 2022 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.

The UNIFORM CRIME REPORTING PROGRAM DATA: OFFENSES KNOWN AND CLEARANCES BY ARREST, 2016 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.

This version (V3) fixes a bug in Version 2 where 1993 data did not properly deal with missing values, leading to enormous counts of crime being reported. This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. The monthly zip 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, feather, Stata) the data is in. Due to file size limits on open ICPSR, not all file types were included for all the data. 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.RThe zip files labeled "yearly" contain yearly data rather than monthly. These also contain far fewer descriptive columns about the agencies in an attempt to decrease file size. Each zip folder contains two files: a data file in whatever format you choose and a codebook. 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.comAs 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.

Firearms background checks for the USA for 2012 (Jan-Nov) and since 1999.These statistics represent the number of firearm background checks initiated through the NICS. They do not represent the number of firearms sold. NICS is used by Federal Firearms Licensees (FFLs) to instantly determine whether a prospective buyer is eligible to buy firearms or explosives. Before ringing up the sale, cashiers call in a check to the FBI or to other designated agencies to ensure that each customer does not have a criminal record or isn't otherwise ineligible to make a purchase. More than 100 million such checks have been made in the last decade, leading to more than 700,000 denials. More information on NICS - http://www.fbi.gov/about-us/cjis/nics Some really useful informations such as the rate of checks per 1000 people. All data is provided by state. Downloaded from the Guardian Datablog - http://www.guardian.co.uk/news/datablog/2012/dec/17/how-many-guns-us and then joined to USA States data http://geocommons.com/overlays/21424. Gun data originally from FBI http://www.fbi.gov/about-us/cjis/nics. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-12-17 and migrated to Edinburgh DataShare on 2017-02-21.

Version 4 release notes: Add data for 2016.Order rows by year (descending) and ORI.Version 3 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The LEOKA data sets contain highly detailed data about the number of officers/civilians employed by an agency and how many officers were killed or assaulted. Each data set contains over 2,200 columns and has a wealth of information about the circumstances of assaults on officers. All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. It was then cleaned in R. The "cleaning" just means that column names were standardized (different years have slightly different spellings for many columns). Standardization of column names is necessary to stack multiple years together. Categorical variables (e.g. state) were also standardized (i.e. fix spelling errors). About 7% of all agencies in the data report more officers or civilians than population. As such, I removed the officers/civilians per 1,000 population variables. You should exercise caution if deciding to generate and use these variables yourself. I did not make any changes to the numeric columns except for the following. A few years of data had the values "blank" or "missing" as indicators of missing values. Rows in otherwise numeric columns (e.g. jan_asslt_no_injury_knife) with these values were replaced with NA. There were three obvious data entry errors in officers killed by felony/accident that I changed to NA. In 1978 the agency "pittsburgh" (ORI = PAPPD00) reported 576 officers killed by accident during March.In 1979 the agency "metuchen" (ORI = NJ01210) reported 991 officers killed by felony during August.In 1990 the agency "penobscot state police" (ORI = ME010SP) reported 860 officers killed by accident during July.No other changes to numeric columns were made.Each zip file contains all years as individual monthly files of the specified data type It also includes a file with all years aggregated yearly and stacked into a single data set. Please note that each monthly file is quite large (2,200+ columns) so it may take time to download the zip file and open each data file.For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data.The UCR Handbook (https://ucr.fbi.gov/additional-ucr-publications/ucr_handbook.pdf/view) describes the LEOKA data as follows:"The UCR Program collects data from all contributing agencies ... on officer line-of-duty deaths and assaults. Reporting agencies must submit data on ... their own duly sworn officers feloniously or accidentally killed or assaulted in the line of duty. The purpose of this data collection is to identify situations in which officers are killed or assaulted, describe the incidents statistically, and publish the data to aid agencies in developing policies to improve officer safety."... agencies must record assaults on sworn officers. Reporting agencies must count all assaults that resulted in serious injury or assaults in which a weapon was used that could have caused serious injury or death. They must include other assaults not causing injury if the assault involved more than mere verbal abuse or minor resistance to an arrest. In other words, agencies must include in this section all assaults on officers, whether or not the officers sustained injuries."If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com

The Data

Following the implementation of the Brady act in 1994, the Federal Bureau of Investigation (FBI) developed a system to conduct background checks on individuals wanting to obtain a firearm. The system known as the National Instant Criminal Background Check System (NICS) was created in collaboration with the Buereu of Alcohol, Tabacco and Firearms and local law enforcement agencies. Since it's inception in November 1998, the FBI has released monthly data from each state and U.S territory. The FBI claims that over 300 million requests have been aprroved, and 1.5 million have been denied.

Source

The FBI releases the monthly data in pdf format. Thanks to Buzzfeed's Jeremy Singer Vine, a public repository on resides on GitHub containing the pdf data parsed into a csv file. The data csv file can be accessed here: https://raw.githubusercontent.com/BuzzFeedNews/nics-firearm-background-checks/master/data/nics-firearm-background-checks.csv The pdf version of the data can be found here: https://www.fbi.gov/file-repository/nics_firearm_checks_-_month_year_by_state_type.pdf/view

Important Considerations About the Data

The data simply collects the quantity of background checks conducted. The FBI advices agaisnt the use of this data to analyze gun sales, as conducting a background check does not implictly mean that a firearm was purchased. For example, some states require monthly background checks on all their current conceal carry permit holders. Additionally, some states participate in the program more agressively than others. A map displaying the level of compliance by state can be found here: https://www.fbi.gov/file-repository/nics-participation-map.pdf/view

Version 5 release notes:
Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.
Version 4 release notes:
Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
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 NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 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 JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the data are just agency identifier columns). Because this "simple" data set need fewer columns, I include all offenses.

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 nine different files, eight which contain different crimes and the "simple" file. Each file contains the data for all years. The eight categories each have crimes belonging to a major crime category and do not overlap in crimes other than with the index offenses. 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 included crimes are:

Index Crimes
MurderRapeRobberyAggravated AssaultBurglaryTheftMotor Vehicle TheftArsonAlcohol CrimesDUIDrunkenness
LiquorDrug CrimesTotal DrugTotal Drug SalesTotal Drug PossessionCannabis PossessionCannabis SalesHeroin or Cocaine PossessionHeroin or Cocaine SalesOther Drug PossessionOther Drug SalesSynthetic Narcotic PossessionSynthetic Narcotic SalesGrey Collar and Property CrimesForgeryFraudStolen PropertyFinancial CrimesEmbezzlementTotal GamblingOther GamblingBookmakingNumbers LotterySex or Family CrimesOffenses Against the Family and Children
Other Sex Offenses
ProstitutionRapeViolent CrimesAggravated AssaultMurderNegligent ManslaughterRobberyWeapon Offenses
Other CrimesCurfewDisorderly ConductOther Non-trafficSuspicion
VandalismVagrancy
Simple
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.

Three crime data sources were collected and merged for this study. All three crime sources were either only reporting on the U.S. state of Kentucky (KOOL and Louisville Open Data), or filtered to only contain results for the U.S. state of Kentucky (FBI). Each data source contains unique features such as crime classifications, and unique challenges in collection and cleaning.

The United States Federal Bureau of Investigation (FBI) issues a variety of query-able crime related data on their website. This data is sourced from law enforcement agencies across the U.S. as part of their National Incident-Based Reporting System (NIBRS) and its standards. The goal of gathering, standardizing, and providing this information is to facilitate research into crime and law enforcement patterns. The information is provided as a collection of CSV files with instructions and code for importing into a SQL database. For the purposes of this research, we utilized the the crime databases for the years 2017, 2018 and 2019, containing a total of 1,939,990 unique incidents. The NIBRS_code property denotes the type of crime as assigned by the reporting agency. The human trafficking codes are 40A (Prostitution), 40B (Assisting or Promoting Prostitution), and 370 (Pornography/Obscene Material). The drug incidents were found using codes 35A (Drug/Narcotic Violations) and 35B (Drug Equipment Violations).

The Kentucky Department of Corrections, as a service to the public, provides an online lookup of people currently in its custody called Kentucky Offender Online Lookup (KOOL). This web application offers users tools to search for sets of inmates based on features such as name, crime date, crime name, race, and gender. The data that KOOL searches contains only people who are currently under supervision of the state of Kentucky (or should be under supervision in the case of escape).

The Louisville Open Data Initiative (LOD) is a program from the city of Louisville, Kentucky, U.S.A. to increase the transparency of the city government and promote technological innovation. As part of LOD, a dataset of crime reports is made available online. The records contained within the LOD dataset represent any call for police service where a police incident report was generated. This does not necessarily mean a crime was committed, as an incident report can be generated before an investigation has taken place.

Feature layer showing the locations of Sworn Law Enforcement Officer Locations in California.This is the definition used by the US Department of Justice - Bureau of Justice Statistics (DOJ-BJS) for their Law Enforcement Management and Administrative Statistics (LEMAS) survey. Although LEMAS only includes non Federal Agencies, this dataset includes locations for federal, state, local, and special jurisdiction law enforcement agencies. Law enforcement agencies include, but are not limited to, municipal police, county sheriffs, state police, school police, park police, railroad police, federal law enforcement agencies, departments within non law enforcement federal agencies charged with law enforcement (e.g., US Postal Inspectors), and cross jurisdictional authorities (e.g., Port Authority Police). In general, the requirements and training for becoming a sworn law enforcement officer are set by each state. Law Enforcement agencies themselves are not chartered or licensed by their state. County, city, and other government authorities within each state are usually empowered by their state law to setup or disband Law Enforcement agencies. Generally, sworn Law Enforcement officers must report which agency they are employed by to the state. Although TGS's intention is to only include locations associated with agencies that meet the above definition, TGS has discovered a few locations that are associated with agencies that are not publicly funded. TGS deleted these locations as we became aware of them, but some may still exist in this dataset. Personal homes, administrative offices, and temporary locations are intended to be excluded from this dataset; however, some personal homes of constables are included due to the fact that many constables work out of their homes. This also applies to mounted police in New Mexico. TGS has made a concerted effort to include all local police; county sheriffs; state police and/or highway patrol; Bureau of Indian Affairs; Bureau of Land Management; Bureau of Reclamation; U.S. Park Police; Bureau of Alcohol, Tobacco, Firearms, and Explosives; U.S. Marshals Service; U.S. Fish and Wildlife Service; National Park Service; U.S. Immigration and Customs Enforcement; and U.S. Customs and Border Protection in the United States and its territories. This dataset is comprised completely of license free data. At the request of NGA, FBI entities are intended to be excluded from this dataset, but a few may be included. The HSIP Freedom Law Enforcement dataset and the HSIP Freedom Correctional Institutions dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. Please see the process description for the breakdown of how the records were merged. With the merge of the Law Enforcement and the Correctional Institutions datasets, the HSIP Themes and NAICS Codes & Descriptions were assigned based on the facility's main function which was determined by the entity's name, facility type, web research, and state supplied data. In instances where the entity's primary function is both law enforcement and corrections, the NAICS Codes and Descriptions are assigned based on the dataset in which the record is located (i.e., a facility that serves as both a Sheriff's Office and as a jail is designated as [NAICSDESCR]="SHERIFFS' OFFICES (EXCEPT COURT FUNCTIONS ONLY)" in the Law Enforcement layer and as [NAICSDESCR]="JAILS (EXCEPT PRIVATE OPERATION OF)" in the Correctional Institutions layer). Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields that TGS populated. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on the values in this field, the oldest record dates from 12/07/2004 and the newest record dates from 09/10/2009.Use Cases: Use cases describe how the data may be used and help to define and clarify requirements.1. An assessment of whether or not the total police capability in a given area is adequate. 2. A list of resources to draw upon in surrounding areas when local resources have temporarily been overwhelmed by a disaster - route analysis can help to determine those entities who are able to respond the quickest. 3. A resource for emergency management planning purposes. 4. A resource for catastrophe response to aid in the retrieval of equipment by outside responders in order to deal with the disaster. 5. A resource for situational awareness planning and response for federal government events.