The average for 2017 based on 97 countries was 7.4 homicides per 100,000 people. The highest value was in El Salvador: 61.8 homicides per 100,000 people and the lowest value was in Japan: 0.2 homicides per 100,000 people. The indicator is available from 1990 to 2017. Below is a chart for all countries where data are available.

Turks and Caicos Islands saw a murder rate of ***** per 100,000 inhabitants, making it the most dangerous country for this kind of crime worldwide as of 2024. Interestingly, El Salvador, which long had the highest global homicide rates, has dropped out of the top 29 after a high number of gang members have been incarcerated. Meanwhile, Colima in Mexico was the most dangerous city for murders. Violent conflicts worldwide Notably, these figures do not include deaths that resulted from war or a violent conflict. While there is a persistent number of conflicts worldwide, resulting casualties are not considered murders. Partially due to this reason, homicide rates in Latin America are higher than those in Afghanistan or Syria. A different definition of murder in these circumstances could change the rate significantly in some countries. Causes of death Also, noteworthy is that murders are usually not random events. In the United States, the circumstances of murders are most commonly arguments, followed by narcotics incidents and robberies. Additionally, murders are not a leading cause of death. Heart diseases, strokes and cancer pose a greater threat to life than violent crime.

Content

In this dataset you'll find statistics on the most common forms of crime committed from all countries around the world.

Acknowledgements

This data comes from https://data.world/unodc/b4aa5785-7a33-4c07-af15-0f15d95a121f.

In 2025, Pietermaritzburg in South Africa ranked as the world's most dangerous city with a crime rate of 82 per 100,000 inhabitants. Five of the 10 cities with the highest crime rates worldwide are found in South Africa. The list does not include countries where war and conflict exist. South Africa dominates crime statistics When looking at crime rates, among the 10 most dangerous cities in the world, half of them are found in South Africa. The country is struggling with extremely high levels of inequality, and is struggling with high levels of crime and power outages, harming the country's economy and driving more people into unemployment and poverty. Crime in Latin America On the other hand, when looking at murder rates, Latin America dominates the list of the world's most dangerous countries. Violence in Latin America is caused in great part by drug trafficking, weapons trafficking, and gang wars.

The average for 2016 based on 74 countries was 783 thefts per 100,000 people. The highest value was in Denmark: 3949 thefts per 100,000 people and the lowest value was in Senegal: 1 thefts per 100,000 people. The indicator is available from 2003 to 2016. Below is a chart for all countries where data are available.

Investigator(s): United Nations Office at Vienna, R.W. Burnham, Helen Burnham, Bruce DiCristina, and Graeme Newman The United Nations Surveys of Crime Trends and Operations of Criminal Justice Systems (formerly known as the United Nations World Crime Surveys) series was begun in 1978 and is comprised of five quinquennial surveys covering the years 1970-1975, 1975-1980, 1980-1986, 1986-1990, and 1990-1994. The project was supported by the United States Bureau of Justice Statistics, and conducted under the auspices of the United Nations Criminal Justice and Crime Prevention Branch, United Nations Office in Vienna. Data gathered on crime prevention and criminal justice among member nations provide information for policy development and program planning. The main objectives of the survey include: to conduct a more focused inquiry into the incidence of crime worldwide, to improve knowledge about the incidence of reported crime in the global development perspective and also international understanding of effective ways to counteract crime, to improve the dissemination globally of the information collected, to facilitate an overview of trends and interrelationships among various parts of the criminal justice system so as to promote informed decision-making in its administration, nationally and cross-nationally, and to serve as an instrument for strengthening cooperation among member states by putting the review and analysis of national crime-related data in a broader context. The surveys also provide a valuable source of charting trends in crime and criminal justice over two decades.

The average for 2017 based on 79 countries was 105 robberies per 100,000 people. The highest value was in Costa Rica: 1587 robberies per 100,000 people and the lowest value was in Oman: 1 robberies per 100,000 people. The indicator is available from 2003 to 2017. Below is a chart for all countries where data are available.

The United Nations began its World Crime Surveys in 1978. The first survey collected statistics on a small range of offenses and on the criminal justice process for the years 1970-1975. The second survey collected data on a wide range of offenses, offenders, and criminal justice process data for the years 1975-1980. Several factors make these two collections difficult to use in combination. Some 25 percent of those countries responding to the first survey did not respond to the second and, similarly, some 30 percent of those responding to the second survey did not respond to the first. In addition, many questions asked in the second survey were not asked in the first survey. This data collection represents the efforts of the investigators to combine, revise, and recheck the data of the first two surveys. The data are divided into two parts. Part 1 comprises all data on offenses and on some criminal justice personnel. Crime data are entered for 1970 through 1980. In most cases 1975 is entered twice, since both surveys collected data for this year. Part 2 includes data on offenders, prosecutions, convictions, and prisons. Data are entered for 1970 through 1980, for every even year.

Analysis of ‘Hate Crime Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/2014-hate-crime-statisticse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

The Uniform Crime Reporting Program collects data about both single-bias and multiple-bias hate crimes. For each offense type reported, law enforcement must indicate at least one bias motivation. A single-bias incident is defined as an incident in which one or more offense types are motivated by the same bias. As of 2013, a multiple-bias incident is defined as an incident in which one or more offense types are motivated by two or more biases. Overview
In 2014, 15,494 law enforcement agencies participated in the Hate Crime Statistics Program. Of these agencies, 1,666 reported 5,479 hate crime incidents involving 6,418 offenses.
There were 5,462 single-bias incidents that involved 6,385 offenses, 6,681 victims, and 5,176 known offenders.
The 17 multiple-bias incidents reported in 2014 involved 33 offenses, 46 victims, and 16 offenders. (See Tables 1 and 12.) Source: FBI Hate Crime Statistics and more about the Hate Crime Statistics

Source: https://ucr.fbi.gov/about-us/cjis/ucr/hate-crime/2014/resource-pages/download-files

This dataset was created by Uniform Crime Reports and contains around 0 samples along with Unnamed: 13, Unnamed: 3, technical information and other features such as: - Unnamed: 12 - Unnamed: 5 - and more.

How to use this dataset

• Analyze Unnamed: 14 in relation to Unnamed: 9
• Study the influence of Unnamed: 15 on Unnamed: 4
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Uniform Crime Reports

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

The Fifth United Nations Survey, covering the years 1990-1994, was designed to collect data on the incidence of reported crime and the operation of criminal justice systems with a view to improving the dissemination of that information globally. To that end, the survey facilitates an overview of trends and interrelationships among various parts of the criminal justice system to promote informed decision-making in its administration, nationally and crossnationally. Variables describe combined police and prosecution expenditure by year and by country, number of police personnel by gender, total number of homicides by country and by city, number of assaults, rapes, robberies, thefts, burglaries, frauds, and embezzlements, amount of drug crime, number of people formally charged with crime, age of suspects, number and gender of prosecutors, number of individuals prosecuted and the types of crimes prosecuted, gender and age of individuals prosecuted, types of courts, number of individuals convicted and acquitted, numbers sentenced to capital punishment and various other punishments, number of convictions on various charges, number of individuals sentenced and in detention, number of prisoners, sentence lengths, and prison demographics.

In 2024, the highest homicide rate among 22 Latin American and Caribbean countries surveyed was in Haiti, with around 62 murders committed per 100,000 inhabitants. Trinidad and Tobago came in second, with a homicide rate of 46, while Honduras ranked seventh, with 25. In the same year, the lowest rate was recorded in El Salvador, with a homicide rate of 1.9 per 100,000 inhabitants. A violence-ridden region Violence and crime are some of the most pressing problems affecting Latin American society nowadays. More than 40 of the 50 most dangerous cities in the world are located in this region, as well as one of the twenty countries with the least peace in the world according to the Global Peace Index. Despite governments’ large spending on security and high imprisonment rates, drug and weapon trafficking, organized crime, and gangs have turned violence into an epidemic that affects the whole region and a solution to this issue appears to be hardly attainable. The cost of violence in Mexico Mexico stands out as an example of the great cost that violence inflicts upon a country, since beyond claiming human lives, it also affects everyday life and has a negative impact on the economy. Mexicans have a high perceived level of insecurity, as they do not only fear becoming victims of homicide, but also of other common crimes, such as assault or rape. Such fear prevents people from performing everyday activities, for instance, going out at night, taking a taxi or going to the movies or the theater. Furthermore, the economic toll of violence in Mexico is more than considerable. For example, the cost of homicide and violent crime amounted to 2099.8 and 1778.1 billion Mexican pesos in 2023, respectively.

Context

Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances.

The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values).

Content

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 crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes)

predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18

Acknowledgements

http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized

U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files),

U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995)

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing.

However, I would like to try and answer the following questions answered.

1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities

2. How has unemployment changed crime rate(violent and non-violent) in the communities?

3. Were people from a particular age group more vulnerable to crime?

4. Does ethnicity play a role in crime rate?

5. Has education played a role in bringing down the crime rate?

In 2023, there were around *** million fraud crimes recorded in China. This made fraud the most common crime committed. The number of fraud crimes showed constant fluctuations in recent years, while theft crimes decreased considerably. Crime situation in China According to governmental statistics, the total number of crimes committed in China has decreased over the past years, amounting to **** million cases in 2022, the lowest number in the last ten years. However, the number of arrests of criminal suspects in China reached a high in 2019 with over **** million arrests, receding only recently due to the coronavirus pandemic. ************************* were the top three types of crimes in China. The country has a lower murder rate compared to many other countries in the world. City safety in China Generally speaking, the crime rate is associated with population density. In regions with higher population densities, there are also more theft and robbery crimes committed. Even though some Chinese cities have the highest population densities in the world, the crime rate of these regions are still low when compared to global rates. Cities in China are also widely covered with closed-circuit television cameras, which have contributed positively to the reduction of crimes as well as to the crime detection rate.

Crime Analytics Tools Market Outlook

The global crime analytics tools market size was valued at approximately USD 5.3 billion in 2023 and is projected to reach around USD 12.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.2% during the forecast period. The key growth factor driving this market is the increasing need for advanced analytical tools to combat rising crime rates and enhance public safety. With the rapid advancements in technology and the growing importance of data in decision-making processes, the crime analytics tools market is poised for substantial growth.

One of the primary growth factors for the crime analytics tools market is the rising incidence of criminal activities globally, which has prompted law enforcement agencies to invest in advanced analytical solutions. These tools help in identifying, predicting, and preventing crimes, thus enhancing the overall effectiveness of law enforcement operations. Additionally, the integration of machine learning and artificial intelligence (AI) in crime analytics tools is revolutionizing the way crimes are analyzed and mitigated, providing a significant boost to market growth.

Another critical growth factor is the increasing government initiatives and funding aimed at strengthening national security and public safety. Governments worldwide are recognizing the importance of advanced crime analytics tools in enhancing public safety and are consequently increasing their budget allocations for the adoption of these tools. This surge in government investments is expected to drive the market's growth significantly during the forecast period.

The proliferation of smart city initiatives is also contributing to the market's growth. As cities around the world aim to become smarter and safer, the deployment of advanced crime analytics tools is becoming essential. These tools enable city authorities to monitor and manage urban safety more effectively, thereby reducing crime rates and improving the quality of life for residents. This trend is expected to fuel the demand for crime analytics tools in the coming years.

Regionally, North America dominates the crime analytics tools market, owing to the high adoption rate of advanced technologies and significant government investments in public safety. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by increasing urbanization, rising crime rates, and growing government initiatives for public safety. Europe, Latin America, and the Middle East & Africa are also expected to contribute significantly to the market's growth, with steady adoption of crime analytics tools across these regions.

Component Analysis

The crime analytics tools market is segmented based on components into software, hardware, and services. The software segment holds the largest market share and is expected to maintain its dominance throughout the forecast period. Crime analytics software includes advanced solutions that leverage data analytics, machine learning, and artificial intelligence to analyze crime patterns, predict future crimes, and provide actionable insights to law enforcement agencies. The increasing adoption of these sophisticated software solutions by government agencies and private security firms is driving the growth of this segment.

The hardware segment, although smaller compared to software, plays a crucial role in the overall crime analytics ecosystem. Hardware components include surveillance cameras, sensors, and other data collection devices that are essential for gathering real-time data. The integration of these hardware components with advanced software solutions enhances the overall efficiency of crime analytics tools. The continuous advancements in hardware technology, such as the development of high-resolution cameras and IoT-enabled devices, are expected to drive the growth of this segment.

The services segment is also witnessing significant growth, driven by the increasing need for implementation, training, and maintenance services associated with crime analytics tools. As these tools become more sophisticated, the demand for specialized services to ensure their optimal performance is rising. These services include consulting, custom development, and ongoing support, which are crucial for the successful deployment and operation of crime analytics solutions. The growing emphasis on end-to-end solutions is further propelling the demand for services in this market.

Overall, the component analysis

The United Nations International Crime Prevention and Criminal Justice Branch began the Surveys of Crime Trends and Operations of Criminal Justice Systems (formerly known as the World Crime Surveys) in 1978. The goal of the data collection effort was to conduct a more focused inquiry into the incidence of crime worldwide. To date, there have been five quinquennial surveys, covering the years 1970-1975, 1975-1980, 1980-1986, 1986-1990, and 1990-1994, respectively. Starting with the 1980 data, the waves overlap by one year to allow for reliability and validity checks of the data. For this data collection, the original United Nations data were restructured into a standard contemporary file structure, with each file consisting of all data for one year. Naming conventions were standardized, and each country and each variable was given a unique identifying number. Crime variables include counts of recorded crime for homicide, assault, rape, robbery, theft, burglary, fraud, embezzlement, drug trafficking, drug possession, bribery, and corruption. There are also counts of suspects, persons prosecuted, persons convicted, and prison admissions by crime, gender, and adult or juvenile status. Other variables include the population of the country and largest city, budgets and salaries for police, courts, and prisons, and types of sanctions, including imprisonment, corporal punishment, deprivation of liberty, control of freedom, warning, fine, and community sentence. The countries participating in the survey and the variables available vary by year.

Chicago Crime Incident Data

65,000+ Records of Arrests, Locations, and Descriptions

By City of Chicago [source]

About this dataset

This dataset is a compilation of reported crimes that have taken place in the City of Chicago over the past year, and provides an invaluable insight into the criminal activity occurring within our city. Featuring more than 65,000 records of data, it contains information on the date of each incident, its location (down to the block level), type of crime committed (determined by FBI Crime Classification Codes) and whether or not an arrest has been made in connection with each crime. As this dataset reveals detailed information on crime incidents which may lead to personal identification, addresses are masked beyond block level and specific locations are not disclosed.

For additional questions regarding this dataset, please do not hesitate to reach out to The Research & Development Division at 312.745.6071 or RandDchicagopolice.com who will be more than happy to help answer any inquiries you may have about our data findings! All visualized maps should be considered approximate however—it is prohibited for any attempts to derive specific addresses from them as accuracy cannot be guaranteed with regards to mechanical or human error when collecting this data over time. So come join us as we explore a year's worth of criminal activities throughout Chicago!

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How to use the dataset

This guide will provide an overview on how to use this dataset to analyze patterns or draw conclusions about crime incidents in and around Chicago.

Secondly, become familiar with columns names which appear at top most row of your opened file which helps you understand what kind of data is stored at each column such as - CASE# - Unique identifier for the crime incident., DATE OF OCCURRENCE - Date when crime incident occurred , BLOCK - Block where event took place , LOCATION DESCRIPTION- Description of location where incident happened . Through these columns name you can easily recognize what kind of data exists within that record/row. That’s why it’s important to get familiar with them first before diving into raw datasets because they’ll help make exploring and understanding large sets easier later on when we go further into illustrating charts & graphs using programs such as Tableau & Power BI or even spreadsheets (Excel). After understanding column names its time to explore further by digging deeper into each record/row and apply filters if required e.g below $100 value will show only those rows having value less than 100 thus it will filter entire dataset according to your requirement. Lastly analyse collected datasets either Visually through plotting graphs with help tableau software OR By using Mathematical mathematical equations based on research questions such as finding out average values after applying sum/avg functions from respective cells etc

Research Ideas

• Creating a visualization mapping tool to help visualize the types of crimes and their locations over time within Chicago.
• An analysis tool for city officials or police departments so they can understand correlations between crime type, geography, and other factors like weather changes or economic downturns in order to develop long-term plans for crime prevention.
• Developing an AI model that would be able to predict what areas may be more vulnerable for certain types of crimes or even predict crimes ahead of time based on the data from this dataset

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: crimes-one-year-prior-to-present-1.csv | Column name | Description | |:-------------------------|:------------------------------------------------------------------------------| | CASE# | Unique identifier for each crime incident (String) | | BLOCK | Block where the crime incident occurred (String) | | LOCATION DESCRIPTION | Description of where an incident took place (String) | | ARREST | Indicates if an arrest was made in connection with a crime incident (Boolean) | | DOMESTIC | Indicates if a reported incident is domestic related (Boolean) | | BEAT ...

The average for 2017 based on 65 countries was 1.8 kidnappings per 100,000 people. The highest value was in Belgium: 10.3 kidnappings per 100,000 people and the lowest value was in Bermuda: 0 kidnappings per 100,000 people. The indicator is available from 2003 to 2017. Below is a chart for all countries where data are available.

Crime Trends in India from 2001 to 2020

A Statistical Overview of Criminal Victimization and Offender Action

By Rajanand Ilangovan [source]

About this dataset

This Dataset provides an up-to-date analysis of crime trends in India from 2001 to the present. It contains complete information about different types of crimes such as rape, murder, and theft that were committed across India. By analyzing this dataset we can determine the areas where crimes were most prevalent, what type of offenders were usually involved in the crime and which year had the highest number of registered cases. Additionally, we can also analyse which group experienced most complaints and what kind of punishments or consequences they faced like departmental enquiries, magisterial enquiries or police personnel trials completed. This data set is perfect for further research into crime trends in India and will help us better understand why certain types of crimes take place more frequently than others

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How to use the dataset

• Area Name (state or UT) where the crime was reported. • Year in which the crime was reported. • Subgroup (type of crime). • Number of cases registered, number of cases reported for departmental action etc., related to a particular type of crime and state/UT.
• Number of complaints/cases declared false/unsubstantiated, number of police personnel convictions etc., related to a particular type of crime and state/UT.
• Number of cases in which offenders were others known persons to the victims, neighbours or relatives to the victims etc., related to a particular type of crime and state/UT.

By studying this dataset one might explore different angles by analysing factors like:

• What are the top states with high rate criminal activities? Which areas are relatively safer?
• Are any states witnessing higher incidences than national average levels? Alternatively, are there any regions which have recorded lower rates than national average levels?
• What is trend between sub crimes across India both regional & time wise? How has it changed over time ? (2001-20) ;
Movement among crimes on monthly basis during period 2001 - 2020 Comparison among ages , genders & professions involved with Crime Rates && Timeline comparison between Types Of Crime , Crimes Involving Police Personnel Contractors in Crimes as timeline . Immigration Report . Is absolute difference btw urban & rural up from previous years ? Open conversations about what government efforts need more focus & why . Fundamentals impacting reducing / increasing rate behind closed doors . Any impactful key insights about SelfDefence Degree given out that year highlighting decreasing / increasing amount if increase thenwhat extra activity got curated btw that law was enacted vs before enactment if possible Outliers Analysis on same murders done by pediphiles or sexual assault against women under minorities if exists

Research Ideas

• Analyzing crime trends over time by analyzing the Year, Sub_group and Area_Name columns to understand different types of crimes and patterns of criminal activity in India.

• Evaluating the effectiveness of police response to different types of crimes, such as comparing the CPA_-_Cases_Registered, CPA_-_Cases_Reported_for_Dept._Action and CPB_-_Police_PersonnelAcquitted data fields across different time periods, sub-groups and areas to assess how well law enforcement is responding to crimes reported.

• Tracking changes in punishment awarded for different crimes by analyzing the CPC_-_Police_-Personnel_-Major-Punishment_-awarded data field for changes over ti...

Police Analytics Software Market Outlook

The global police analytics software market size was valued at approximately USD 5.1 billion in 2023 and is expected to reach around USD 12.9 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.8% during the forecast period. This robust growth is driven by several factors, including the increasing need for law enforcement agencies to improve crime detection, prevention, and resolution rates using advanced technological tools. As crime becomes more sophisticated, the deployment of analytical and predictive tools is instrumental in aiding police departments to manage and utilize vast amounts of data effectively.

One of the primary growth factors for the police analytics software market is the heightened emphasis on data-driven decision-making within law enforcement agencies. With the proliferation of digital data from various sources such as surveillance cameras, social media, and public databases, agencies are recognizing the potential of analytics software to process and extract actionable insights from this data. This technology enables better resource allocation, enhances situational awareness, and ultimately aids in reducing crime rates. Moreover, governments around the world are increasingly investing in smart city initiatives, which incorporate sophisticated policing technologies, thereby fueling market expansion.

Another significant growth driver is the growing concern for public safety, coupled with the surge in criminal activities across urban and rural landscapes. Law enforcement agencies face continuous pressure to enhance their operational efficiency and responsiveness. Police analytics software equips these agencies with tools to conduct thorough crime analysis and predictive policing, which helps in identifying crime hotspots and potential threats ahead of time. These capabilities are not only improving operational efficiency but also fostering a proactive approach to crime prevention, which is highly valued by both agencies and the communities they serve.

The advancement of artificial intelligence and machine learning technologies is also propelling the growth of the police analytics software market. These technologies allow for more sophisticated data analysis, including pattern recognition and predictive modeling, which can foresee criminal activities before they occur. By employing AI-driven analytics, law enforcement agencies can significantly enhance their crime-fighting capabilities, improving accuracy in suspect identification and crime linkage analysis. This technological advancement is creating new opportunities for innovation within the market and compelling more agencies to adopt these solutions for a competitive edge.

Regionally, North America is currently leading the market, attributable to its technologically advanced law enforcement infrastructure and significant investments in public safety technologies. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. This growth is fueled by rapid urbanization, rising crime rates, and increasing government investments in smart policing technologies across developing nations such as India and China. Meanwhile, Europe is also expected to experience substantial market growth, driven by stringent regulations related to public safety and the adoption of advanced crime-fighting solutions across various countries in the region.

Component Analysis

The police analytics software market by component is segmented into software and services, both of which play a pivotal role in enhancing the capabilities of law enforcement agencies. The software segment is at the forefront of this market, comprising various solutions designed to aid in crime analysis, predictive policing, and incident management. These software tools are essential in processing vast datasets generated from various sources, allowing agencies to derive actionable insights that aid in decision-making processes. The continuous evolution of software capabilities, driven by artificial intelligence and machine learning advancements, has further enhanced their analytical power, making them indispensable tools for modern policing.

Within the software segment, there are applications tailored for specific functions such as predictive policing and real-time crime mapping. Predictive policing software has gained significant traction as it enables law enforcement to anticipate potential crime hotspots and deploy resources effectively. Real-time crime mapping, on the other hand, provides law enforcement with a dynamic view of crime patterns

The overall ACE project is motivated by the need to better understand the behaviour of non-state armed groups in perpetrating atrocity crimes such as crimes against humanity, ethnic cleansing and war crimes. The data collection is based on six countries Central African Republic, the Democratic Republic of Congo, Iraq, Nigeria, Syria, and Somalia with a focus on non-state actor perpetrated atrocity events. The aim is to have a fine-grained event data collection of different types of atrocity crimes in these countries. These event types are derived from the Rome Statute. More specifically, the unit of observation in ACE is the event where a named state or non-state actor is involved on a given day in a specific location. Each individual event is covered with the best precision regarding the type of event, location, perpetrator and victims.Since 2010, there has been a 'dramatic resurgence' of violent conflict around the world (United Nations, 2018, p. v). As part of this trend, mass atrocity crimes, defined as genocide, war crimes, crimes against humanity, and ethnic cleansing (GWCE), have become 'the new normal' (Human Rights Watch 2018). At this time of writing, the Global Centre for the Responsibility to Protect (GCR2P) identifies seven countries that are 'currently' experiencing GWCE, three at 'imminent risk', seven of 'serious concern', and thirteen being 'monitored' because they have experienced GWCE in the recent past (Global Centre for the Responsibility to Protect 2019). These crises have seen millions of people killed, tens of thousands raped, and underpin an unprecedented refugee crisis. Although mass violence is not a new phenomenon, non-state armed groups such as Al Qaeda, Islamic State, Boko Haram, Lord's Resistance Army, and Al-Shabaab are increasingly playing a critical role in the perpetration of atrocity crimes leading to key policymakers calling for urgent research on this specific threat (see case for support). Responding to this new reality, the project answers the following primary research question: under what conditions do non-state armed groups perpetrate GWCE? The funding will enable us to develop the first dataset in the world that collects systematic evidence on non-state armed groups perpetrating GWCE, which we call 'Atrocity Crime Events' (ACE) dataset. To do this, we will analyse six countries and three themes. The former refers to the Central African Republic, the Democratic Republic of Congo, Iraq, Nigeria, Syria and Somalia. The latter focuses on i) interactions, for example, between the non-state armed group[s] themselves, other actors such as the government, and external actors such as UN peacekeepers, ii) local factors, for instance, geography, economics, population density, as well as natural resources, and iii) group characteristics, such as age, ideology, and external support. The scientific impact develops in three stages. First, the unique dataset 'ACE' will provide the necessary information to run statistical analysis to explain why, when, and where mass atrocities happen in our six chosen countries. Second, we will develop hypothesis based on our three themes that can be tested through future academic inquiry. Third, the project seeks to drive forward quantitative research into the causes of non-state armed groups perpetrating mass violence. This advance in knowledge will allow us to provide policy recommendations in order to improve international, regional, and national strategies toward mass atrocity prevention with a specific focus on policymakers in the United Nations (UN), the European Union (EU), the United Kingdom (UK), and Africa (the four case study governments and organisations such as the African Union). We will work with three project partners, GCR2P (New York and Geneva), Aegis Trust (Kigali), and Protection Approaches (London), as well as an advisory board consisting of Alex Bellamy, Gyorgy Tatar, Ivan Simonovic, Karen E. Smith, and Kristian Skrede Gleditsch. As part of our impact strategy, we will hold end of project workshops in London, New York, and Kigali. Outputs will include i) publicly available dataset and codebook, ii) six articles in high ranking journals, iii) an Analysis Framework for the United Nations Office on Genocide Prevention and the RtoP, iv) co-created policy reports with each project partner focusing on the UN, the UK, the EU, and African mass atrocity prevention strategies, v) blogposts, vi) infographics, and vii) presentations at conferences and policy-orientated meetings. The data collection methodology is based on coding news reports extracted from LexisNexis. The extraction of news reports from LexisNexis has been narrowed down by using specific search terms for each event type, including the countries in this project. The focus is primarily on English language sources and where necessary, the geography filter is used to narrow down results based on the location of the event. Once a set of news reports have been identified from Lexis Nexis, the coders skim through the reports based on headlines/short descriptions and select to read through the ones that seem to constitute an event (as opposed to, for example, reports about UN meetings to discuss atrocities etc.). The coders then write a short description of the event on the dataset and code the rest of the variables in the dataset with best precision possible. The coding of the events is based on ACE codebook and is conducted by human coders, each specialising in one of the countries in question.