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.

In 2023, around 3,640.56 violent crimes per 100,000 residents were reported in Oakland, California. This made Oakland the most dangerous city in the United States in that year. Four categories of violent crimes were used: murder and non-negligent manslaughter; forcible rape; robbery; and aggravated assault. Only cities with a population of at least 200,000 were considered.

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.

Comprehensive crime statistics for Los Angeles County's safest neighborhoods including violent crime rates, property crime rates, and annual victimization chances by neighborhood for 2024-2025.

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.

In 2024, Pietermaritzburg (South Africa) ranked first in the crime index among African cities, with a rating of roughly ** index points. The six most dangerous areas on the continent were South African cities. The index estimates the overall level of crime in a specific territory. According to the score, crime levels are classified as very high (over 80), high (60-80), moderate (40-60), low (20-40), and very low (below 20). South Africa’s crime situation According to the crime index ranking, ************ was the most dangerous country in Africa in 2023, followed by ***************** and ******. Murder and organized crime are particularly widespread in South Africa. In 2023, the country had one of the highest murder rates globally, registering around ** homicides per 100,000 inhabitants. Moreover, South Africa’s crime scene is also characterized by the presence of organized criminal activities, for which the country ranked third in Africa. Reflecting these high levels of crime, a survey conducted in 2023 showed that around ** percent of South Africans were worried about crime and violence in the country. Crime risks in Africa The African continent hosts some of the most dangerous places worldwide. In 2023, *********** and the ******************************** were the least peaceful countries in Africa, according to the Global Peace Index. Worldwide, they ranked fourth and fifth, respectively, behind Afghanistan, Yemen, and Syria. Terrorism is a leading type of crime perpetrated in Africa. Home to Boko Aram, Nigeria is among the countries with the highest number of terrorism-related deaths globally. Furthermore, Burkina Faso had the highest number of fatalities in the world. Human trafficking is also widespread, predominantly in West Africa. The most common forms of exploitation of victims of trafficking in persons are forced labor and sexual exploitation.

This table contains data on the rate of violent crime (crimes per 1,000 population) for California, its regions, counties, cities and towns. Crime and population data are from the Federal Bureau of Investigations, Uniform Crime Reports. Rates above the city/town level include data from city, university and college, county, state, tribal, and federal law enforcement agencies. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Ten percent of all deaths in young California adults aged 15-44 years are related to assault and homicide. In 2010, California law enforcement agencies reported 1,809 murders, 8,331 rapes, and over 95,000 aggravated assaults. African Americans in California are 11 times more likely to die of assault and homicide than Whites. More information about the data table and a data dictionary can be found in the About/Attachments section.

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.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Data Fulfillment and Analysis Division of the Chicago Police Department at DFA@ChicagoPolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data are updated daily. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

This study of violent incidents among middle- and high-school students focused not only on the types and frequency of these incidents, but also on their dynamics -- the locations, the opening moves, the relationship between the disputants, the goals and justifications of the aggressor, the role of third parties, and other factors. For this study, violence was defined as an act carried out with the intention, or perceived intention, of physically injuring another person, and the "opening move" was defined as the action of a respondent, antagonist, or third party that was viewed as beginning the violent incident. Data were obtained from interviews with 70 boys and 40 girls who attended public schools with populations that had high rates of violence. About half of the students came from a middle school in an economically disadvantaged African-American section of a large southern city. The neighborhood the school served, which included a public housing project, had some of the country's highest rates of reported violent crime. The other half of the sample were volunteers from an alternative high school attended by students who had committed serious violations of school rules, largely involving illegal drugs, possession of handguns, or fighting. Many students in this high school, which is located in a large city in the southern part of the Midwest, came from high-crime areas, including public housing communities. The interviews were open-ended, with the students encouraged to speak at length about any violent incidents in school, at home, or in the neighborhood in which they had been involved. The 110 interviews yielded 250 incidents and are presented as text files, Parts 3 and 4. The interview transcriptions were then reduced to a quantitative database with the incident as the unit of analysis (Part 1). Incidents were diagrammed, and events in each sequence were coded and grouped to show the typical patterns and sub-patterns in the interactions. Explanations the students offered for the violent-incident behavior were grouped into two categories: (1) "justifications," in which the young people accepted responsibility for their violent actions but denied that the actions were wrong, and (2) "excuses," in which the young people admitted the act was wrong but denied responsibility. Every case in the incident database had at least one physical indicator of force or violence. The respondent-level file (Part 2) was created from the incident-level file using the AGGREGATE procedure in SPSS. Variables in Part 1 include the sex, grade, and age of the respondent, the sex and estimated age of the antagonist, the relationship between respondent and antagonist, the nature and location of the opening move, the respondent's response to the opening move, persons present during the incident, the respondent's emotions during the incident, the person who ended the fight, punishments imposed due to the incident, whether the respondent was arrested, and the duration of the incident. Additional items cover the number of times during the incident that something was thrown, the respondent was pushed, slapped, or spanked, was kicked, bit, or hit with a fist or with something else, was beaten up, cut, or bruised, was threatened with a knife or gun, or a knife or gun was used on the respondent. Variables in Part 2 include the respondent's age, gender, race, and grade at the time of the interview, the number of incidents per respondent, if the respondent was an armed robber or a victim of an armed robbery, and whether the respondent had something thrown at him/her, was pushed, slapped, or spanked, was kicked, bit, or hit with a fist or with something else, was beaten up, was threatened with a knife or gun, or had a knife or gun used on him/her.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Pennington County, SD (DISCONTINUED) (FBITC046103) from 2004 to 2021 about Pennington County, SD; Rapid City; crime; violent crime; property crime; SD; and USA.

Context

Brazil has a very powerful Freedom of Information law which allows any citizen to request any data from the government which is not restricted, and where these restrictions are well defined exceptions. But still, having the right to request the information does not mean it is easy to get it. Bureaucracy and ignorance of the law gets in the way many times. In order to encourage the government to put their databases in order and to inspire people to have the courage to ask the government for information, we made a massive request of information, for the complete dataset of crime data available for the last 10 years, in the biggest city of South America.

Content

This dataset contains structured data about all crime occurrences that have been acted upon by the PM, the main police force in Sao Paulo. The dataset is not consistent in its completeness, as some of the towns comprising the Greater Sao Paulo were slow in collecting full data. It also does not contain the actual historic of each crime report, as that would violate privacy.

Acknowledgements

We would like to acknowledge the prompt assistance from the SSP (Secretaria de Seguranca Publica), for providing the data with minimal resistance.

Inspiration

Primarily we would like to see a visualisation of this data, so that the people can have an idea of how crime has evolved in their city, which crimes are more prevalent in which areas, etc. In addition, any model which can predict at what times and where the police is most needed would be helpful, as this can then be sent to the SSP to help them in planning.

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

Attributes/demographics of FBI Uniform Crime Reporting Part I violent crime victims and offenders, updated monthly, aggregated to the CMPD jurisdiction, Neighborhood Profile Area (NPA), and Violent Crime Hotspot (focus areas for the City's violence reduction initiative). Monthly counts cover the time frame Jan-2015 to present. Crime categories comprising violent crime include homicide, rape, robbery, and aggravated assault. Attributes of violent crime victims include counts of domestic violence (DV and Non-DV), age group, gender, and race/ethnicity. Attributes of violent crime offenders include counts of age group, gender, and race/ethnicity.

Table containing authoritative violent crime values for Sioux Falls, South Dakota.

All BPD data on Open Baltimore is preliminary data and subject to change. The information presented through Open Baltimore represents Part I victim based crime data. The data do not represent statistics submitted to the FBI's Uniform Crime Report (UCR); therefore any comparisons are strictly prohibited. For further clarification of UCR data, please visit http://www.fbi.gov/about-us/cjis/ucr/ucr. Please note that this data is preliminary and subject to change. Prior month data is likely to show changes when it is refreshed on a monthly basis. All data is geocoded to the approximate latitude/longitude location of the incident and excludes those records for which an address could not be geocoded. Any attempt to match the approximate location of the incident to an exact address is strictly prohibited.

Violent and property crime rates per 100,000 population for San Mateo County and the State of California. The total crimes used to calculate the rates for San Mateo County include data from: Sheriff's Department Unincorporated, Atherton, Belmont, Brisbane, Broadmoor, Burlingame, Colma, Daly City, East Palo Alto, Foster City, Half Moon Bay, Hillsborough, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Bay Area DPR, BART, Union Pacific Railroad, and CA Highway Patrol.

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.

Alaska crime data from 2000 to present from the FBI Uniform Crime Reporting (UCR) program. Information includes data on both violent and property crime.The UCR Program's primary objective is to generate reliable information for use in law enforcement administration, operation, and management; over the years, however, the data have become one of the country’s leading social indicators. The program has been the starting place for law enforcement executives, students of criminal justice, researchers, members of the media, and the public at large seeking information on crime in the nation. The program was conceived in 1929 by the International Association of Chiefs of Police to meet the need for reliable uniform crime statistics for the nation. In 1930, the FBI was tasked with collecting, publishing, and archiving those statistics.Source: US Federal Bureau of Investigation (FBI)This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: FBI UCR ProgramOffenses Known to Law Enforcement, by State by City, 2017 The FBI collects these data through the Uniform Crime Reporting (UCR) Program. Important note about rape data In 2013, the FBI’s UCR Program initiated the collection of rape data under a revised definition within the Summary Based Reporting System. The term “forcible” was removed from the offense name, and the definition was changed to “penetration, no matter how slight, of the vagina or anus with any body part or object, or oral penetration by a sex organ of another person, without the consent of the victim.” In 2016, the FBI Director approved the recommendation to discontinue the reporting of rape data using the UCR legacy definition beginning in 2017. General comment This table provides the volume of violent crime (murder and nonnegligent manslaughter, rape, robbery, and aggravated assault) and property crime (burglary, larceny-theft, and motor vehicle theft) as reported by city and town law enforcement agencies (listed alphabetically by state) that contributed data to the UCR Program. (Note: Arson is not included in the property crime total in this table; however, if complete arson data were provided, it will appear in the arson column.) Caution against ranking Readers should take into consideration relevant factors in addition to an area’s crime statistics when making any valid comparisons of crime among different locales. UCR Statistics: Their Proper Use provides more details. Methodology The data used in creating this table were from all city and town law enforcement agencies submitting 12 months of complete offense data for 2017. Rape figures, and violent crime, which rape is a part, will not be published in this table for agencies submitting rape using the UCR legacy rape definition. The rape figures, and violent crime, which rape is a part, published in this table are from only those agencies using the UCR revised rape definition as well as converted data from agencies that reported data for rape, sodomy, and sexual assault with an object via NIBRS. The FBI does not publish arson data unless it receives data from either the agency or the state for all 12 months of the calendar year. When the FBI determines that an agency’s data collection methodology does not comply with national UCR guidelines, the figure(s) for that agency’s offense(s) will not be included in the table, and the discrepancy will be explained in a footnote. Population estimation For the 2017 population estimates used in this table, the FBI computed individual rates of growth from one year to the next for every city/town and county using 2010 decennial population counts and 2011 through 2016 population estimates from the U.S. Census Bureau. Each agency’s rates of growth were averaged; that average was then applied and added to its 2016 Census population estimate to derive the agency’s 2017 population estimate.

DescriptionCrime incidents starting with those reported in 2016. The data provided is the latest available information and is updated regularly as statistics change. For access to comprehensive reports, kindly submit a public record request here.Note: Crimes that occurred before 2016 are included if the date reported was in 2016 or later.Disclaimer: The City strives to provide the highest-quality information on this platform. The content on this website is provided as a public service, on an ‘as is’ basis. The City makes no warranty, representation, or guarantee of any type as to the content, accuracy, timeliness, completeness, or fitness for any particular purpose or use of any public data provided on this portal; nor shall any such warranty be implied, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose. The City assumes no liability by making data available to the public or other departments.This dataset is featured in the following app(s):Cleveland Division of Police Crime DashboardCrime Incidents MapData GlossarySee the Attributes section below for details about each column in this dataset.Update FrequencyDaily around 8 AM ESTContactsCity of Cleveland, Division of Police