World crime rate per 100K population for 2020 was 5.61, a 0.74% increase from 2019.

<ul style='margin-top:20px;'>

<li>World crime rate per 100K population for 2019 was <strong>5.56</strong>, a <strong>3.65% decline</strong> from 2018.</li>
<li>World crime rate per 100K population for 2018 was <strong>5.77</strong>, a <strong>2.24% decline</strong> from 2017.</li>
<li>World crime rate per 100K population for 2017 was <strong>5.91</strong>, a <strong>0.69% decline</strong> from 2016.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

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.

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.

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.

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 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.

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.

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.

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.

India crime rate per 100K population for 2021 was 2.94, a 0.86% increase from 2020.

<ul style='margin-top:20px;'>

<li>India crime rate per 100K population for 2020 was <strong>2.91</strong>, a <strong>0.53% decline</strong> from 2019.</li>
<li>India crime rate per 100K population for 2019 was <strong>2.93</strong>, a <strong>2.24% decline</strong> from 2018.</li>
<li>India crime rate per 100K population for 2018 was <strong>2.99</strong>, a <strong>1.16% decline</strong> from 2017.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

In 2023, the number of crimes committed in Singapore for every 100,000 individuals was 1,188. This was a ten-year high, and mostly due to the increase in scams and cybercrimes cases. Low crime rates in Singapore Singapore has a reputation for being one of the safest cities in the world. Violent crime in Singapore is rare – as of 2021, such crimes accounted for nine per 100 thousand population. One reason for this could be the harsh penalties for offenders, as well as a strict ban on weapons for those not in law enforcement. Singapore still carries out capital punishment for crimes such as murder and the illegal possession of firearms carry the death penalty. Increase in commercial crime The most common type of crime committed in Singapore were commercial crimes, especially scams. As Singaporeans carry out more aspects of everyday life online, so too are criminals looking to take advantage of unsuspecting victims. In 2021, scams involving e-commerce transactions were the most common of such crimes. These typically involve the fraudulent sale of products on C2C commercial sites, which are harder to track. Such scams, however, usually involve smaller amounts of money, unlike investment scams. These involve targeting individuals and tricking them into wiring large sums of money for supposed financial investments. In 2021, individuals in Singapore who fell victim to such scams were cheated out of around 191 million Singapore dollars.

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 ...

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

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.

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.

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?

The UCF-Crime dataset is a large-scale dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety.

This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities.

Sweden crime rate per 100K population for 2021 was 1.08, a 9.72% decline from 2020.

<ul style='margin-top:20px;'>

<li>Sweden crime rate per 100K population for 2020 was <strong>1.20</strong>, a <strong>10.63% increase</strong> from 2019.</li>
<li>Sweden crime rate per 100K population for 2019 was <strong>1.08</strong>, a <strong>1.71% increase</strong> from 2018.</li>
<li>Sweden crime rate per 100K population for 2018 was <strong>1.06</strong>, a <strong>5.4% decline</strong> from 2017.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

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.

The Study’s Subject: The German Statistical Office of the German Empire compiled a comparative representation of different countrie’s crime statistics. In this context the statistical office was faced with the problem of diverging methodologies and classifications of the countrie’s crime statistics data collections. After World War 1 the “International Statistic Institute (ISI)” and the “International Penal Law and Prison Commission” (IPPC) ) resumed their research activities in the fields of criminal statistics in international comparison. In this context the Statistical Office of the German Empire carried out an investigation of 33 european and non-european countries with the aim to work out a comparative compilation of various criminalstatistical classifications. Is was established that at the time of preparation a comparison of different classifications a comparable international data compilation could not be gathered due to significant differences between the classifications. Finally from the 33 countries it could be compiled time series on criminal statisics only for a small selection of countries. The reason for this situation was the lack of data material for many countries. Therefore, the development of crime could be presented in form of time series for the following countries: - Austria- England and Wales- France- German Empire- Sweden- Canada- Japan In terms of the crime statistical objective data on lawsuit processes (for example the number of criminal proceedings) has not been incorporated. Furthermore, no data on the military criminal justice are included in the data compilation. The following information, which was available in the statistics, has been taken from the statistics for the data compilation: Information on the persons, who has been accused or convicted: Number of persons totally, by gender, teenagers or adults.Information on the offences the persons were accused for: accused or convicted by groups of offences or single selected offences.The sentences imposed as results of lawsuit processes are not included in this data compilation. Data tables in HISTAT (Thema: Kriminaltiät): A. Österreich (Austria) A.1 Rechtskräftig Verurteilte nach Geschlecht (Legally convicted by sex)A.2 Rechtskräftig Verurteilte wegen Verbrechen nach ausgewählten Deliktarten (Legally convicted of crimes by selected types of offences)A.3 Rechtskräftig Verurteilte wegen Verbrechen und Übertretungen zusammen nach ausgewählten Deliktarten (Legally convicted of crimes and violations by selected types of offences)A.4 Verurteilte auf 100.000 Strafmündige nach ausgewählten Deliktarten (Kriminalitätsziffern) (Convicted per 100.000 of population of the age of criminal responsibility by selected offences (crime rate)) B. England und Wales (England and Wales)B.1 Angeklagte wegen schwerer Vergehen vor Schwurgerichten und Vierteljahressitzungen nach Deliktarten (Accuesed of heavy offences at the jury court (Assizes) and at the „Quarter Sessions“ by types of offences)B.2 Angeklagte wegen schwerer und leichter Vergehen vor allen Gerichten insgesamt und vor den Gerichten für summarische Rechtsprechung (Accused of heavy offences and of petty offences at all types of courts and at courts of summary jurisdiction)B.3 Angeklagte und Verurteilte nach Geschlecht C. Frankreich (France) C.1 Verhandlungen vor Schwurgerichten (Hearings at the jury courts)C.1.1 Angeklagte vor Schwurgerichte nach Geschlecht (Accused at jury courts by gender)C.1.2 Anzahl der Verurteilten durch Schwurgerichte (Number of convicted by the jury court)C.1.3 Erhobene Anklagen nach Deliktart vor Schwurgerichten (Prosecutions by types of offences at the jury court) C.2 Verhandlungen vor Strafgerichten (Hearings at the tribunal court)C.2.1 Angeklagte vor und Verurteilte der Strafgerichte insgesamt (Accused and convicted of tribunal courts, totaly)C.2.2 Anklagen vor Strafgerichte nach Deliktarten (Prosecutions at the tribunal court by types of offences) D. Deutsches Reich (German Empire) D.1 Abgeurteilte Personen und verurteilte Personen nach Geschlecht, Jugendliche und Vorbestrafte (1882-1927) (Persons judged and convicted persons by sex)D.2 Verurteilte Personen nach Deliktgruppen (1882-1927) (Convicted Persons by types of offences)D.3 Kriminalitätsziffern der verurteilten Personen - auf 100.000 der strafm. Bevölkerung (1882-1927) (Crime rate of convicted Persons – per 100.000 of population of the age of criminal responsibility)D.4 Kriminalitätsziffern der verurteilten Personen nach Deliktgruppe - auf 100.000 der strafm. Bevölkerung (1882-1927) (Crime Rate of convicted Persons by type of offence – per 100.000 of population of the age of criminal responsibility)D.5 Die Strafmündige Bevölkerung des Deutschen Reiches (1882-1928) (Population of the German Empire of the a...