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.

This study surveyed police chiefs and data analysts in order to determine the use of data in police departments. The surveys were sent to 1,379 police agencies serving populations of at least 25,000. The survey sample for this study was selected from the 2000 Law Enforcement Management and Administrative Statistics (LEMAS) survey. All police agencies serving populations of at least 25,000 were selected from the LEMAS database for inclusion. Separate surveys were sent for completion by police chiefs and data analysts. Surveys were used to gather information on data sharing and integration efforts to identify the needs and capacities for data usage in local law enforcement agencies. The police chief surveys focused on five main areas of interest: use of data, personnel response to data collection, the collection and reporting of incident-based data, sharing data, and the providing of statistics to the community and media. Like the police chief surveys, the data analyst surveys focused on five main areas of interest: use of data, agency structures and resources, data for strategies, data sharing and outside assistance, and incident-based data. The final total of police chief surveys included in the study is 790, while 752 data analyst responses are included.

"In 2015, The Washington Post began to log every fatal shooting by an on-duty police officer in the United States. In that time there have been more than 5,000 such shootings recorded by The Post. After Michael Brown, an unarmed Black man, was killed in 2014 by police in Ferguson, Mo., a Post investigation found that the FBI undercounted fatal police shootings by more than half. This is because reporting by police departments is voluntary and many departments fail to do so. The Washington Post’s data relies primarily on news accounts, social media postings, and police reports. Analysis of more than five years of data reveals that the number and circumstances of fatal shootings and the overall demographics of the victims have remained relatively constant..." SOURCE ==> Washington Post Article

For more information about this story

This dataset has been prepared by The Washington Post (they keep updating it on runtime) with every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

2016 PoliceKillingUS DATASET
2017 PoliceKillingUS DATASET
2018 PoliceKillingUS DATASET
2019 PoliceKillingUS DATASET
2020 PoliceKillingUS DATASET

Features at the Dataset:

The file fatal-police-shootings-data.csv contains data about each fatal shooting in CSV format. The file can be downloaded at this URL. Each row has the following variables:

• id: a unique identifier for each victim
• name: the name of the victim
• date: the date of the fatal shooting in YYYY-MM-DD format
• manner_of_death: shot, shot and Tasered
• armed: indicates that the victim was armed with some sort of implement that a police officer believed could inflict harm
  • undetermined: it is not known whether or not the victim had a weapon
  • unknown: the victim was armed, but it is not known what the object was
  • unarmed: the victim was not armed
• age: the age of the victim
• gender: the gender of the victim. The Post identifies victims by the gender they identify with if reports indicate that it differs from their biological sex.
  • M: Male
  • F: Female
  • None: unknown
• race:
  • W: White, non-Hispanic
  • B: Black, non-Hispanic
  • A: Asian
  • N: Native American
  • H: Hispanic
  • O: Other
  • None: unknown
• city: the municipality where the fatal shooting took place. Note that in some cases this field may contain a county name if a more specific municipality is unavailable or unknown.
• state: two-letter postal code abbreviation
• signs of mental illness: News reports have indicated the victim had a history of mental health issues, expressed suicidal intentions or was experiencing mental distress at the time of the shooting.
• threat_level: The threat_level column was used to flag incidents for the story by Amy Brittain in October 2015. http://www.washingtonpost.com/sf/investigative/2015/10/24/on-duty-under-fire/ As described in the story, the general criteria for the attack label was that there was the most direct and immediate threat to life. That would include incidents where officers or others were shot at, threatened with a gun, attacked with other weapons or physical force, etc. The attack category is meant to flag the highest level of threat. The other and undetermined categories represent all remaining cases. Other includes many incidents where officers or others faced significant threats.
• flee: News reports have indicated the victim was moving away from officers
  • Foot
  • Car
  • Not fleeing

The threat column and the fleeing column are not necessarily related. For example, there is an incident in which the suspect is fleeing and at the same time turns to fire at gun at the officer. Also, attacks represent a status immediately before fatal shots by police while fleeing could begin slightly earlier and involve a chase. - body_camera: News reports have indicated an officer w...

These data on 19th- and early 20th-century police department and arrest behavior were collected between 1975 and 1978 for a study of police and crime in the United States. Raw and aggregated time-series data are presented in Parts 1 and 3 on 23 American cities for most years during the period 1860-1920. The data were drawn from annual reports of police departments found in the Library of Congress or in newspapers and legislative reports located elsewhere. Variables in Part 1, for which the city is the unit of analysis, include arrests for drunkenness, conditional offenses and homicides, persons dismissed or held, police personnel, and population. Part 3 aggregates the data by year and reports some of these variables on a per capita basis, using a linear interpolation from the last decennial census to estimate population. Part 2 contains data for 267 United States cities for the period 1880-1890 and was generated from the 1880 federal census volume, REPORT ON THE DEFECTIVE, DEPENDENT, AND DELINQUENT CLASSES, published in 1888, and from the 1890 federal census volume, SOCIAL STATISTICS OF CITIES. Information includes police personnel and expenditures, arrests, persons held overnight, trains entering town, and population.

This dataset contains information on fatal police deaths in the United States. The data includes the victim's rank, name, department, date of death, and cause of death. The data spans from 1791 to the present day. This dataset will be updated on monthly basis. Data Scrapped from this website :- https://www.odmp.org/

New Version Features -> With the new web scrapper I have upgraded dataset with more information. 1) The new dataset version is "police_deaths_USA_v6.csv" and "k9_deaths_USA_v6.csv". 2) Splitted the dataset into 2 different datasets 1 for Human Unit and other for K9 Unit. 3) Check out the new web scrapper code in this file "final_scrapper_program_with_comments.ipynb". 4) Also added the correction file which is needed to adjust some data points from K9 dataset. 5) Extended data of Human Unit dataset to 13 Features. 6) Extended data of K9 Unit dataset to 14 Features.

The police_deaths dataset contains 13 variables:

1) Rank -> Rank assigned or achieved by the police throughout their tenure.

2) Name -> The name of the person.

3) Age -> Age of the person.

4) End_Of_Watch -> The death date on which the the person declared as dead.

5) Day_Of_Week -> The day of the week [Sunday, Monday, etc.].

6) Cause -> The cause of the death.

7) Department -> The department's name where the person works.

8) State -> The state where the department is situated.

9) Tour -> The Duration of there Tenure.

10) Badge -> Badge of the person.

11) Weapon -> The Weapon by which the officer has been killed.

12) Offender -> Offender / Killer this says what happened to the offender after the incident was he/she [Arrested, Killed, etc.].

13) Summary -> Summary of the police officer and also the summary of the incident of what happened ? How he/she died ?, etc.

The k9_deaths dataset contains 14 variables:

1) Rank -> Rank assigned or achieved by the K9 throughout their tenure.

2) Name -> The name of the K9.

3) Breed -> Breed of the K9.

4) Gender -> Gender of the K9.

5) Age -> Age of the K9.

6) End_Of_Watch -> The death date on which the the person declared as dead.

7) Day_Of_Week -> The day of the week [Sunday, Monday, etc.].

8) Cause -> The cause of the death.

9) Department -> The department's name where the K9 was assigned.

10) State -> The state where the department is situated.

11) Tour -> The Duration of there Tenure.

12) Weapon -> The Weapon by which the officer has been killed.

13) Offender -> Offender / Killer this says what happened to the offender after the incident was he/she [Arrested, Killed, etc.].

14) Summary -> Summary of the K9 dog and also the summary of the incident of what happened ? How he/she died ?, etc.

Acknowledgements:

The original dataset was collected by FiveThirtyEight and it contains police death data from 1791 to 2016. Here is the link -> https://data.world/fivethirtyeight/police-deaths.

The reason I made this dataset is because it had not been updated since 2016 and the scrapping script was outdated, so I decided to make a new scrapper and update the dataset till present. I got this idea from the FiveThirtyEight group and a fellow kaggler, Satoshi Datamoto, who uploaded the dataset on kaggle. Thank you for inspiration.

Tableau Visualization link :- https://public.tableau.com/app/profile/mayuresh.koli/viz/USALawEnforcementLineofDutyDeaths/main_dashboard

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

This study was undertaken to provide current information on work and family issues from the police officer's perspective, and to explore the existence and prevalence of work and family training and intervention programs offered nationally by law enforcement agencies. Three different surveys were employed to collect data for this study. First, a pilot study was conducted in which a questionnaire, designed to elicit information on work and family issues in law enforcement, was distributed to 1,800 law enforcement officers representing 21 municipal, suburban, and rural police agencies in western New York State (Part 1). Demographic information in this Work and Family Issues in Law Enforcement (WFILE) questionnaire included the age, gender, ethnicity, marital status, highest level of education, and number of years in law enforcement of each respondent. Respondents also provided information on which agency they were from, their job title, and the number of children and step-children they had. The remaining items on the WFILE questionnaire fell into one of the following categories: (1) work and family orientation, (2) work and family issues, (3) job's influence on spouse/significant other, (4) support by spouse/significant other, (5) influence of parental role on the job, (6) job's influence on relationship with children, (7) job's influence on relationships and friendships, (8) knowledge of programs to assist with work and family issues, (9) willingness to use programs to assist with work and family issues, (10) department's ability to assist officers with work and family issues, and (11) relationship with officer's partner. Second, a Police Officer Questionnaire (POQ) was developed based on the results obtained from the pilot study. The POQ was sent to over 4,400 officers in police agencies in three geographical locations: the Northeast (New York City, New York, and surrounding areas), the Midwest (Minneapolis, Minnesota, and surrounding areas), and the Southwest (Dallas, Texas, and surrounding areas) (Part 2). Respondents were asked questions measuring their health, exercise, alcohol and tobacco use, overall job stress, and the number of health-related stress symptoms experienced within the last month. Other questions from the POQ addressed issues of concern to the Police Research and Education Project -- a sister organization of the National Association of Police Organizations -- and its membership. These questions dealt with collective bargaining, the Law Enforcement Officer's Bill of Rights, residency requirements, and high-speed pursuit policies and procedures. Demographic variables included gender, age, ethnicity, marital status, highest level of education, and number of years employed in law enforcement. Third, to identify the extent and nature of services that law enforcement agencies provided for officers and their family members, an Agency Questionnaire (AQ) was developed (Part 3). The AQ survey was developed based on information collected from previous research efforts, the Violent Crime Control and Law Enforcement Act of 1994 (Part W-Family Support, subsection 2303 [b]), and from information gained from the POQ. Data collected from the AQ consisted of whether the agency had a mission statement, provided any type of mental health service, and had a formalized psychological services unit. Respondents also provided information on the number of sworn officers in their agency and the gender of the officers. The remaining questions requested information on service providers, types of services provided, agencies' obstacles to use of services, agencies' enhancement of services, and the organizational impact of the services.

This data was obtained from https://mappingpoliceviolence.us/.

Mapping Police Violence is a 501(c)(3) organization that publishes the most comprehensive and up-to-date data on police violence in America to support transformative change.

This is a database set on openly sharing information on police violence in America.

Some information on this data according to their website: Our data has been meticulously sourced from official police use of force data collection programs in states like California, Texas and Virginia, combined with nationwide data from The Gun Violence Archive and the Fatal Encounters database, two impartial crowdsourced databases. We've also done extensive original research to further improve the quality and completeness of the data; searching social media, obituaries, criminal records databases, police reports and other sources to identify the race of 90 percent of all victims in the database.

We believe the data represented on this site is the most comprehensive accounting of people killed by police since 2013. Note that the Mapping Police Violence database is more comprehensive than the Washington Post police shootings database: while WaPo only tracks cases where people are fatally shot by on-duty police officers, our database includes additional incidents such as cases where police kill someone through use of a chokehold, baton, taser or other means as well as cases such as killings by off-duty police. A recent report from the Bureau of Justice Statistics estimated approximately 1,200 people were killed by police between June, 2015 and May, 2016. Our database identified 1,100 people killed by police over this time period. While there are undoubtedly police killings that are not included in our database (namely, those that go unreported by the media), these estimates suggest that our database captures 92% of the total number of police killings that have occurred since 2013. We hope these data will be used to provide greater transparency and accountability for police departments as part of the ongoing work to end police violence in America.

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.

Agency: US Department of Homeland Security. Frequency of updates: irregular. Description: 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. 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. 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 04/05/2006 and the newest record dates from 10/16/2009 Purpose Homeland Security 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. Projection: WGS 1984.

Chicago Police district station locations and contact information.

The local law enforcement locations feature class/ shapefile contains point location and tabular information pertaining to a wide range of law enforcement entities in the United States. 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 Census of State and Local Law Enforcement Agencies (CSLLEA). Unlike the previous version of this dataset, published in 2009, federal level law enforcement agencies are excluded from this effort. Data fusion techniques are utilized to synchronize overlapping yet disparate source data. The primary sources for this effort are the DOJ-BJS CSLLEA from 2008 and the previously mentioned 2009 feature class from Homeland Security Infrastructure Foundation-Level Data (HIFLD). This feature class contains data for agencies across all 50 U.S. states, Washington D.C. and Puerto Rico.

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

Between 2008 and 2009, the research team gathered survey data from 458 members of the community (Part 1), 312 police officers (Part 2), and 804 individuals who had voluntary contact (Part 3), and 761 individuals who had involuntary contact (Part 4) with police departments in Dallas, Texas, Knoxville, Tennessee, and Kettering, Ohio, and the Broward County, Florida Sheriff's Office. The surveys were designed to look at nine dimensions of police performance: delivering quality services; fear, safety, and order; ethics and values; legitimacy and customer satisfaction; organizational competence and commitment to high standards; reducing crime and victimization; resource use; responding to offenders; and use of authority. The community surveys included questions about police effectiveness, police professionalism, neighborhood problems, and victimization. The officer surveys had three parts: job satisfaction items, procedural knowledge items, and questions about the culture of integrity. The voluntary police contact and involuntary police contact surveys included questions on satisfaction with the way the police officer or deputy sheriff handled the encounter.

This hosted feature layer contains five HPD NIBRS crime yearly cases from 2020 to 2024. This GIS dataset is based on NIBRS data published by Houston Police Department (HPD). The original source data can be found on HPD's Monthly Crime Data By Street And Police Beat webpage "https://www.houstontx.gov/police/cs/Monthly_Crime_Data_by_Street_and_Police_Beat.htm"This GIS dataset was processed and published by Houston Information Technology Services (HITS). National Incident-Based Reporting System (NIBRS) is an incident-based reporting system used by law enforcement agencies in the United States for collecting and reporting data on crimes. Local, state and federal agencies generate NIBRS data from their records management systems. Data is collected on every incident and arrest in the Group A offense category. These Group A offenses include 52 NIBRS classes in three main categories (Person, Property, and Society.) Specific facts about these offenses are gathered and reported to NIBRS. In addition to the Group A offenses, 10 Group B offenses are reported with only the arrest information. Disclaimer: This GIS dataset is prepared and made available for general reference purposes only and should not be used, or relied upon for specific applications, without independent verification. The City of Houston neither represents, nor warrants COHGIS data accuracy, or completeness, nor will the City of Houston accept liability of any kind in conjunction with its use. COHGIS information is in the public domain and may be copied without permission; citation of the source is appreciated.

We will build you a custom US crime dataset based on your needs. Data points may include date, time, location, crime type, crime description, victim demographics, offender demographics, arrest records, charges filed, court outcomes, police department response time, incident outcome, weapon used, property stolen or damaged, crime location type, and other related data.

Use our US crime datasets for a range of applications to enhance public safety and policy effectiveness. Analyzing these datasets can help organizations understand crime patterns and trends across different regions of the United States, enabling them to tailor their strategies and interventions accordingly. Depending on your needs, you may access the entire dataset or a customized subset.

Popular use cases include: improving public safety measures, designing targeted crime prevention programs, resource allocation for law enforcement, and more.

To ensure an accurate sampling frame for its Law Enforcement Management and Administrative Statistics (LEMAS) survey, the Bureau of Justice Statistics periodically sponsors a census of the nation's state and local law enforcement agencies. This census, known as the Directory Survey, gathers data on 49 primary state law enforcement agencies and all sheriffs' departments, local police departments, and special police agencies (state or local) that are publicly funded and employ at least one sworn officer with general arrest powers. The 1996 Directory Survey collected data on the number of sworn and nonsworn personnel employed by each agency, including both full-time and part-time employees. Within the full-time sworn category, data were collected from all agencies on the number who were uniformed officers with regularly assigned duties that included responding to calls for service. For agencies with at least 10 full-time sworn officers, the number whose primary duties were related to investigations, court operations, or jail operations was also obtained. This data collection, compiled in June 1996, represents the third such census, with the first occurring in 1986 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1986: [UNITED STATES] [ICPSR 8696]) and the second in 1992 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1992: [UNITED STATES] [ICPSR 2266]). Variables include personnel totals, type of government, type of agency, and whether the agency had the legal authority to hold a person beyond arraignment for 48 or more hours.

The purpose of the study was to investigate how and why injuries occur to police and citizens during use of force events. The research team conducted a national survey (Part 1) of a stratified random sample of United States law enforcement agencies regarding the deployment of, policies for, and training with less lethal technologies. Finalized surveys were mailed in July 2006 to 950 law enforcement agencies, and a total of 518 law enforcement agencies provided information on less lethal force generally and on their deployment and policies regarding conducted energy devices (CEDs) in particular. A total of 292 variables are included in the National Use of Force Survey Data (Part 1) including items about weapons deployment, force policies, training, force reporting/review, force incidents and outcomes, and conducted energy devices (CEDs). Researchers also collected agency-supplied use of force data from law enforcement agencies in Richland County, South Carolina; Miami-Dade, Florida; and Seattle, Washington; to identify individual and situational predictors of injuries to officers and citizens during use of force events. The Richland County, South Carolina Data (Part 2) include 441 use-of-force reports from January 2005 through July 2006. Part 2 contains 17 variables including whether the officer or suspect was injured, 8 measures of officer force, 3 measures of suspect resistance, the number of witnesses and officers present at each incident, and the number of suspects that resisted or assaulted officers for each incident. The Miami-Dade County, Florida Data (Part 3) consist of 762 use-of-force incidents that occurred between January 2002 and May 2006. Part 3 contains 15 variables, including 4 measures of officer force, the most serious resistance on the part of the suspect, whether the officer or suspect was injured, whether the suspect was impaired by drugs or alcohol, the officer's length of service in years, and several demographic variables pertaining to the suspect and officer. The Seattle, Washington Data (Part 4) consist of 676 use-of-force incidents that occurred between December 1, 2005, as 15 variables, including 3 measures of officer force, whether the suspect or officer was injured, whether the suspect was impaired by drugs or alcohol, whether the suspect used, or threatened to use, physical force against the officer(s), and several demographic variables relating to the suspect and officer(s). The researchers obtained use of force survey data from several large departments representing different types of law enforcement agencies (municipal, county, sheriff's department) in different states. The research team combined use of force data from multiple agencies into a single dataset. This Multiagency Use of Force Data (Part 5) includes 24,928 use-of-force incidents obtained from 12 law enforcement agencies from 1998 through 2007. Part 5 consists a total of 21 variables, including the year the incident took place, demographic variables relating to the suspect, the type of force used by the officer, whether the suspect or officer was injured, and 5 measures of the department's policy regarding the use of CEDs and pepper spray. Lastly, longitudinal data were also collected for the Orlando, Florida and Austin, Texas police departments. The Orlando, Florida Longitudinal Data (Part 6) comprise 4,222 use-of-force incidents aggregated to 108 months -- a 9 year period from 1998 through 2006. Finally, the Austin, Texas Longitudinal Data (Part 7) include 6,596 force incidents aggregated over 60 months- a 5 year period from 2002 through 2006. Part 6 and Part 7 are comprised of seven variables documenting whether a Taser was implemented, the number of suspects and officers injured in a month, the number of force incidents per month, and the number of CEDs uses per month.

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes stop data from AZ, CO, CT, IA, MA, MD, MI and MO. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?