Context

In early 2016, The Washington Post wrote that the Justice Department is "resuming a controversial practice that allows local police departments to funnel a large portion of assets seized from citizens into their own coffers under federal law.

The "Equitable Sharing Program" gives police the option of prosecuting some asset forfeiture cases under federal instead of state law, particularly in instances where local law enforcement officers have a relationship with federal authorities as part of a joint task force. Federal forfeiture policies are more permissive than many state policies, allowing police to keep up to 80 percent of assets they seize." (link to the full article can be found here).

This is the raw data from the Department of Justice’s Equitable Sharing Agreement and Certification forms that was released by the U.S. Department of Justice Asset Forfeiture and Money Laundering Section.

Content

• spending_master.csv is the main spending dataset that contains 58 variables.

• notes.csv lists the descriptions for all variables.

Acknowledgements

The original dataset can be found here. The data was originally obtained from a Freedom of Information Act request fulfilled in December 2014.

Inspiration

• Which agency/sector/item received the most amount of funds from the Justice Department?

• How many agencies received non-cash assets from the federal government through Equitable Sharing?

• Are there any trends in the total equitable sharing fund across agencies?

This study was the first attempt to investigate gang migration systematically and on a national level. The primary objectives of the study were (1) to identify the scope of gang migration nationally, (2) to describe the nature of gang migration, (3) to assess the impact of gang migration on destination cities, and (4) to describe the current law enforcement responses to the migration of gangs and identify those that appeared to be most effective for various types of migration. Two phases of data collection were used. The major objective of the initial phase was to identify cities that had experienced gang migration (Part 1). This was accomplished by distributing a brief mail questionnaire in 1992 to law enforcement agencies in cities identified as potential gang or gang migration sites. The second major phase of data collection involved in-depth telephone interviews with law enforcement officers in cities that had experienced gang migration in order to develop descriptions of the nature of migration and police responses to it (Part 2). For Part 1, information was collected on the year migration started, number of migrants in the past year, factors that deter gang migration, number of gang members, names of gangs, ethnic distribution of gang members and their drug market involvement, number of gang homicides, number of 1991 gang "drive-bys", and if gangs or narcotics were specified in the respondent's assignment. For Part 2, information was collected on the demographics of gang members, the ethnic percentage of drug gang members and their involvement in distributing specific drugs, and the influence of gang migrants on local gang and crime situations in terms of types and methods of crime, drug distribution activities, technology/equipment used, and targets of crime. Information on patterns of gang migration, including motivations to migrate, drug gang migration, and volume of migration, was also collected. Local responses to gang migration covered information sources, department policies relative to migration, gang specialization in department, approaches taken by the department, and information exchanges and coordination among local, state, and federal agencies.

"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...

Content

Police Residence

This folder contains data behind the story Most Police Don’t Live In The Cities They Serve.

Includes the cities with the 75 largest police forces, with the exception of Honolulu for which data is not available. All calculations are based on data from the U.S. Census.

The Census Bureau numbers are potentially going to differ from other counts for three reasons:

1. The census category for police officers also includes sheriffs, transit police and others who might not be under the same jurisdiction as a city’s police department proper. The census category won’t include private security officers.
2. The census data is estimated from 2006 to 2010; police forces may have changed in size since then.
3. There is always a margin of error in census numbers; they are estimates, not complete counts.

How to read police-locals.csv

Note: When a cell contains ** it means that there are fewer than 100 police officers of that race serving that city.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Entity Resolution for Law Enforcement Market Outlook

According to our latest research, the global market size for Entity Resolution for Law Enforcement reached USD 1.42 billion in 2024. The market is experiencing robust expansion, supported by a CAGR of 14.8% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 4.32 billion. This impressive growth is primarily driven by the increasing need for advanced data analytics and identity management solutions in law enforcement to combat sophisticated criminal activities and enhance operational efficiencies.

The growth of the Entity Resolution for Law Enforcement market is underpinned by the rapid digitalization of law enforcement agencies globally. As agencies transition from traditional paper-based systems to digital platforms, the volume, variety, and velocity of data generated have grown exponentially. This transformation necessitates robust entity resolution solutions capable of accurately identifying, linking, and deduplicating entities across disparate data sources. The proliferation of smart devices, surveillance systems, and interconnected databases has further intensified the demand for advanced software that can process and analyze massive datasets in real time. The market is also benefiting from government initiatives aimed at modernizing public safety infrastructure, which often include investments in advanced data management and analytics platforms.

Another significant driver for the Entity Resolution for Law Enforcement market is the escalating complexity and sophistication of criminal activities. Criminals are increasingly leveraging technology to obscure their identities, create false records, and exploit gaps in law enforcement data systems. This has made traditional investigative methods less effective, pushing agencies to adopt entity resolution solutions that use artificial intelligence, machine learning, and natural language processing to uncover hidden connections and relationships. The integration of these advanced technologies enables law enforcement to detect fraud, analyze intelligence, and solve cases more efficiently. Furthermore, the growing emphasis on data-driven policing and predictive analytics is accelerating the adoption of entity resolution platforms to support proactive crime prevention and resource allocation.

Additionally, the rising concerns around national security, terrorism, and cross-border crimes have compelled federal and intelligence agencies to invest heavily in entity resolution technologies. These solutions are critical for consolidating fragmented data from multiple jurisdictions and sources, enabling agencies to build comprehensive profiles of suspects, organizations, and criminal networks. The ability to accurately resolve entities across complex datasets not only enhances investigative outcomes but also supports intelligence sharing and collaboration between local, national, and international agencies. As data privacy and regulatory compliance become more stringent, entity resolution platforms are evolving to incorporate robust security features and audit trails, further boosting their adoption in the law enforcement sector.

From a regional perspective, North America continues to dominate the Entity Resolution for Law Enforcement market, driven by substantial investments in public safety technologies, a high incidence of cyber and financial crimes, and the presence of leading solution providers. Europe and Asia Pacific are also witnessing significant growth, fueled by increasing government focus on digital transformation and public safety modernization. Emerging economies in Latin America and the Middle East & Africa are gradually adopting entity resolution solutions as part of broader efforts to enhance law enforcement capabilities and address rising crime rates. The regional dynamics are shaped by varying levels of technological maturity, regulatory frameworks, and law enforcement priorities, contributing to a diverse and evolving global market landscape.

Component Analysis

The Component segment of the Entity Resolution for Law Enforcement market is bifurcated into Software and Services. Software solutions represent the backbone of entity resolution, providing the algorithms, analytics engines, and user interfaces necessary for data integration, matching, and deduplication. These platforms are designed to handle

NIBRS Reporting Compliance Platforms Market Outlook

According to our latest research, the global NIBRS Reporting Compliance Platforms market size reached USD 1.42 billion in 2024, reflecting the increasing demand for advanced solutions that support law enforcement agencies in meeting the National Incident-Based Reporting System (NIBRS) standards. The market is poised for robust expansion, with a projected CAGR of 11.7% from 2025 to 2033. By the end of 2033, the market value is forecasted to reach approximately USD 3.89 billion. This growth is primarily driven by the rising emphasis on data-driven policing, regulatory mandates for transparent crime reporting, and the need for modernized public safety infrastructure worldwide.

The primary growth factor fueling the NIBRS Reporting Compliance Platforms market is the increasing enforcement of federal and state-level mandates that require law enforcement agencies to transition from the legacy Uniform Crime Reporting (UCR) system to the more comprehensive NIBRS framework. Governments and regulatory bodies across North America and Europe are particularly focused on improving the accuracy, granularity, and timeliness of crime data. This regulatory push compels agencies to adopt advanced compliance platforms that automate reporting, streamline data collection, and ensure adherence to NIBRS standards. As a result, software vendors and service providers are witnessing a surge in demand for both new deployments and upgrades to existing systems, fostering a competitive and innovation-driven market environment.

Another significant driver of market growth is the rapid digitalization of law enforcement operations and the integration of next-generation technologies such as cloud computing, artificial intelligence, and advanced analytics. Modern NIBRS Reporting Compliance Platforms leverage these technologies to provide real-time insights, support predictive policing, and enhance cross-agency data sharing. The scalability and flexibility offered by cloud-based solutions are particularly attractive to agencies with limited IT infrastructure or those seeking to optimize operational costs. This technological evolution not only improves reporting efficiency but also empowers agencies to derive actionable intelligence from vast datasets, ultimately contributing to more effective crime prevention and public safety strategies.

The market is further propelled by the growing need for transparency and accountability in public safety. High-profile incidents and increasing public scrutiny have placed law enforcement agencies under pressure to deliver accurate, timely, and transparent crime statistics. NIBRS Reporting Compliance Platforms enable agencies to meet these expectations by facilitating detailed incident-level reporting and supporting compliance audits. Additionally, the platforms’ ability to integrate with other public safety systems, such as computer-aided dispatch (CAD) and records management systems (RMS), enhances their value proposition and drives adoption across diverse end-user segments, including government agencies and public safety organizations.

From a regional perspective, North America continues to dominate the NIBRS Reporting Compliance Platforms market, owing to the early adoption of NIBRS standards and significant investments in law enforcement modernization. The United States, in particular, has implemented nationwide mandates for NIBRS compliance, creating a fertile environment for platform providers. Europe is also witnessing steady growth, driven by similar regulatory initiatives and the increasing focus on digital transformation within public safety agencies. Meanwhile, the Asia Pacific region is emerging as a promising market, fueled by rising investments in smart city projects and the modernization of law enforcement infrastructure. As these trends converge, the global market is set to experience sustained growth throughout the forecast period.

Component Analysis

The Component segment of the NIBRS Reporting

Violent Crime in CA

Regional, County, City/Town Rates 2006-2010

By Health [source]

About this dataset

This dataset contains information on the rate of violent crime across California - its regions, counties, cities and towns. The data was collected as part of a larger effort by the Office of Health Equity to better understand public health indicators and ensure equitable outcomes for all.

The numbers reflect more than just a problem in California communities - it reflects a problem with unequal access to resources and opportunity across race, ethnicities and geographies. African Americans in California are 11 times more likely to die from assault or homicide compared to white Californians. Similarly, certain regions report higher crime rates than others at the county level- indicating underlying issues with poverty or institutionalized inequality.

Law enforcement agencies teamed up with the Federal Bureau of Investigations’ Uniform Crime Reports to collect this data table which includes details such as reported number of violent crimes (numerator), population size (denominator), rate per 1,000 population (ratex1000) confidence intervals (LL_95CI & UL_95CI ) standard errors & relative standard errors (se & rse) as well as ratios between city/town rates vs state rates (RR_city2state). Additionally, each record is classified according to region name/code and race/ethnicity code/name , giving researchers further insight into these troubling statistics at both macro and micro levels.

Armed with this information we can explore new ways identify inequitable areas and begin looking for potential solutions that combat health disparities within our communities like never before!

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How to use the dataset

The data is presented with twenty columns providing various segments within each row including:

• Crime definition
• Race/ethnicity code
• Region code
• Geographic area identifier
• Numerator and Denominator values of population
• Standard Error and 95% Confidence Intervals
• Relatvie Standard Error (RSE) value

• Ratios related to city/towns rate to state rate

  The information provided can be used for a variety of applications such as creating visualizations or developing predictive models. It is important to note that rates are expressed per 1,000 population for their respective geographic area during each period noted by the report year field within the dataset. Additionally CA_decile column may be useful in comparing counties due numerical grading system identifying a region’s percentile ranking when compared to other counties within the current year’s entire dataset as well as ratios present under RR_city2state which presents ratio comparison between city/town rate and state rate outside given geographic area have made this an extremely valuable dataset for further analysis

Research Ideas

• Developing a crime prediction and prevention program that uses machine learning models to identify criminal hotspots and direct resources to those areas
• Exploring the connection between race/ethnicity and rates of violence in California
• Creating visualizations and interactive maps to display types of violent crime across different counties within California

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Violent_Crime_Rate_California_2006-2010-DD.csv

File: rows.csv | Column name | Description ...

Description

This is a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. The dataset currently contains approximately 7.7 million accident records. For more information about this dataset, please visit here.

Acknowledgements

If you use this dataset, please kindly cite the following papers:

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. "Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights." In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.

Content

This dataset was collected in real-time using multiple Traffic APIs. It contains accident data collected from February 2016 to March 2023 for the Contiguous United States. For more details about this dataset, please visit [here].

Inspiration

The US-Accidents dataset can be used for numerous applications, such as real-time car accident prediction, studying car accident hotspot locations, casualty analysis, extracting cause and effect rules to predict car accidents, and studying the impact of precipitation or other environmental stimuli on accident occurrence. The most recent release of the dataset can also be useful for studying the impact of COVID-19 on traffic behavior and accidents.

Sampled Data (New!)

For those requiring a smaller, more manageable dataset, a sampled version is available which includes 500,000 accidents. This sample is extracted from the original dataset for easier handling and analysis.

Other Details

Please note that the dataset may be missing data for certain days, which could be due to network connectivity issues during data collection. Regrettably, the dataset will no longer be updated, and this version should be considered the latest.

Usage Policy and Legal Disclaimer

This dataset is being distributed solely for research purposes under the Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By downloading the dataset, you agree to use it only for non-commercial, research, or academic applications. If you use this dataset, it is necessary to cite the papers mentioned above.

Inquiries or need help?

For any inquiries or assistance, please contact Sobhan Moosavi at sobhan.mehr84@gmail.com

Source:

Creator: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA -- culled from 1990 US Census, 1995 US FBI Uniform Crime Report, 1990 US Law Enforcement Management and Administrative Statistics Survey, available from ICPSR at U of Michigan. -- Donor: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA -- Date: July 2009

Data Set Information:

Many variables are included so that algorithms that select or learn weights for attributes could be tested. However, clearly unrelated attributes were not included; attributes were picked if there was any plausible connection to crime (N=122), plus the attribute to be predicted (Per Capita Violent Crimes). The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units.

The per capita violent crimes variable was calculated using population and the sum of crime variables considered violent crimes in the United States: murder, rape, robbery, and assault. There was apparently some controversy in some states concerning the counting of rapes. These resulted in missing values for rape, which resulted in incorrect values for per capita violent crime. These cities are not included in the dataset. Many of these omitted communities were from the midwestern USA.

Data is described below based on original values. All numeric data was normalized into the decimal range 0.00-1.00 using an Unsupervised, equal-interval binning method. Attributes retain their distribution and skew (hence for example the population attribute has a mean value of 0.06 because most communities are small). E.g. An attribute described as 'mean people per household' is actually the normalized (0-1) version of that value.

The normalization preserves rough ratios of values WITHIN an attribute (e.g. double the value for double the population within the available precision - except for extreme values (all values more than 3 SD above the mean are normalized to 1.00; all values more than 3 SD below the mean are normalized to 0.00)).

However, the normalization does not preserve relationships between values BETWEEN attributes (e.g. it would not be meaningful to compare the value for whitePerCap with the value for blackPerCap for a community)

A limitation was that the LEMAS survey was of the police departments with at least 100 officers, plus a random sample of smaller departments. For our purposes, communities not found in both census and crime datasets were omitted. Many communities are missing LEMAS data.

Attribute Information:

'(125 predictive, 4 non-predictive, 18 potential goal) ', ' communityname: Community name - not predictive - for information only (string) ', ' state: US state (by 2 letter postal abbreviation)(nominal) ', ' countyCode: numeric code for county - not predictive, and many missing values (numeric) ', ' communityCode: numeric code for community - not predictive and many missing values (numeric) ', ' fold: fold number for non-random 10 fold cross validation, potentially useful for debugging, paired tests - not predictive (numeric - integer) ', ' population: population for community: (numeric - expected to be integer) ', ' householdsize: mean people per household (numeric - decimal) ', ' racepctblack: percentage of population that is african american (numeric - decimal) ', ' racePctWhite: percentage of population that is caucasian (numeric - decimal) ', ' racePctAsian: percentage of population that is of asian heritage (numeric - decimal) ', ' racePctHisp: percentage of population that is of hispanic heritage (numeric - decimal) ', ' agePct12t21: percentage of population that is 12-21 in age (numeric - decimal) ', ' agePct12t29: percentage of population that is 12-29 in age (numeric - decimal) ', ' agePct16t24: percentage of population that is 16-24 in age (numeric - decimal) ', ' agePct65up: percentage of population that is 65 and over in age (numeric - decimal) ', ' numbUrban: number of people living in areas classified as urban (numeric - expected to be integer) ', ' pctUrban: percentage of people living in areas classified as urban (numeric - decimal) ', ' medIncome: median household income (numeric - may be integer) ', ' pctWWage: percentage of households with wage or salary income in 1989 (numeric - decimal) ', ' pctWFarmSelf: percentage of households with farm or self employment income in 1989 (numeric - decimal) ', ' pctWInvInc: percentage of households with investment / rent income in 1989 (numeric - decimal) ', ' pctWSocSec: percentage of households with social security income in 1989 (numeric - decimal) ', ' pctWPubAsst: pe...