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 2020, the capital city Caracas ranked third in the ranking of most violent Venezuelan metropolises, with a murder rate of nearly ** per 100,000 inhabitants. The list was headed by Cumaná with a homicide rate of *****.

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.

The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_60 _100.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_100 _250.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_250 _plus.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'total_crime': total crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'total_violent _crime': total violent crime - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft - 'tot_prop _crime': total property crime - 'arson': arson

Photo by David von Diemar on Unsplash

In 2020, Memphis, TN-MS-AR reported 1,358.8 violent crimes per 100,000 inhabitants, the most out of any metro area in the United States. Monroe, LA followed closely behind, with a violent crime rate of 1,308.5 crimes per 100,000 inhabitants.

Crime severity index (violent, non-violent, youth) and weighted clearance rates (violent, non-violent), Canada, provinces, territories and Census Metropolitan Areas, 1998 to 2024.

In 2022, the New Orleans-Metairie, LA metro area recorded the highest homicide rate of U.S. cities with a population over 250,000, at **** homicides per 100,000 residents, followed by the Memphis, TN-MS-AR metro area. However, homicide data was not recorded in all U.S. metro areas, meaning that there may be some cities with a higher homicide rate. St. Louis St. Louis, which had a murder and nonnegligent manslaughter rate of **** in 2022, is the second-largest city by population in Missouri. It is home to many famous treasures, such as the St. Louis Cardinals baseball team, Washington University in St. Louis, the Saint Louis Zoo, and the renowned Gateway Arch. It is also home to many corporations, such as Monsanto, Arch Coal, and Emerson Electric. The economy of St. Louis is centered around business and healthcare, and boasts ten Fortune 500 companies. Crime in St. Louis Despite all of this, St. Louis suffers from high levels of crime and violence. As of 2023, it was listed as the seventh most dangerous city in the world as a result of their extremely high murder rate. Not only does St. Louis have one of the highest homicide rates in the United States, it also reports one of the highest numbers of violent crimes. Despite high crime levels, the GDP of the St. Louis metropolitan area has been increasing since 2001.

Crime report data is provided for Louisville Metro Police Divisions only; crime data does not include smaller class cities.The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Data is broken out by year into separate CSV files. Note the file grouping by year is based on the crime's Date Reported (not the Date Occurred).Older cases found in the 2003 data are indicative of cold case research. Older cases are entered into the Police database system and tracked but dates and times of the original case are maintained.Data may also be viewed off-site in map form for just the last 6 months on Crimemapping.comData Dictionary:INCIDENT_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence roomsDATE_REPORTED - the date the incident was reported to LMPDDATE_OCCURED - the date the incident actually occurredUOR_DESC - Uniform Offense Reporting code for the criminal act committedCRIME_TYPE - the crime type categoryNIBRS_CODE - the code that follows the guidelines of the National Incident Based Reporting System. For more details visit https://ucr.fbi.gov/nibrs/2011/resources/nibrs-offense-codes/viewUCR_HIERARCHY - hierarchy that follows the guidelines of the FBI Uniform Crime Reporting. For more details visit https://ucr.fbi.gov/ATT_COMP - Status indicating whether the incident was an attempted crime or a completed crime.LMPD_DIVISION - the LMPD division in which the incident actually occurredLMPD_BEAT - the LMPD beat in which the incident actually occurredPREMISE_TYPE - the type of location in which the incident occurred (e.g. Restaurant)BLOCK_ADDRESS - the location the incident occurredCITY - the city associated to the incident block locationZIP_CODE - the zip code associated to the incident block locationID - Unique identifier for internal databaseContact:Crime Information CenterCrimeInfoCenterDL@louisvilleky.gov

Number and rate (per 100,000 population) of homicide victims, Canada and Census Metropolitan Areas, 1981 to 2024.

This study focuses on "hot spot" or "place-based" policing, defined as focused police interventions on small areas of concentrated crime, often combining elements of community-oriented policing (COP) and problem-oriented policing (POP). COP focuses on community outcomes, such as police legitimacy and police-community relations, while POP uses a Scanning, Analysis, Response, and Assessment model to find effective solutions to problems and reduce future crime. The purpose of this project was to test whether place-based policing strategies can be implemented to both reduce crime and achieve broader benefits for the community and police agencies. Researchers measured crime, community member's perceptions of safety, perceptions of police-community relations, police legitimacy, and community collective efficacy in targeted areas. Respondents' demographic information was also collected, including residency, race, age, and employment status.

In 2023, the state with the highest crime rate in the United States per 100,000 inhabitants was New Mexico. That year, the crime rate was ******** crimes per 100,000 people. In comparison, New Hampshire had the lowest crime rate at ****** crimes per 100,000 people. Crime rate The crime rate in the United States has generally decreased over time. There are several factors attributed to the decrease in the crime rate across the United States. An increase in the number of police officers and an increase in income are some of the reasons for a decrease in the crime rate. Unfortunately, people of color have been disproportionately affected by crime rates, as they are more likely to be arrested for a crime versus a white person. Crime rates regionally The District of Columbia had the highest rate of reported violent crimes in the United States in 2023 per 100,000 inhabitants. The most common crime clearance type in metropolitan counties in the United States in 2020 was murder and non-negligent manslaughter. The second most dangerous city in the country in 2020 was Detroit. Detroit has faced severe levels of economic and demographic declines in the past years. Not only has the population decreased, the city has filed for bankruptcy. Despite the median household income increasing, the city still struggles financially.

Abstract: The stay at home restrictions to control the spread of COVID-19 led to unparalleled sudden change in daily life, but it is unclear how they affected urban crime globally. We collected data on daily counts of crime in 26 cities across 22 countries in the Americas, Europe, the Middle East and Asia. We conducted interrupted time series analyses to assess the impact of stay at home restrictions on different types of crime in each city. Our findings show that the stay at home policies were associated with a considerable drop in urban crime, but with substantial variation across cities and types of crime. Meta-regression results showed that more stringent restrictions over movement in public space were predictive of larger declines in crime.

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.

Content

This dataset contain official crime statistics from São Paulo state cities (Brazil) and were prepared based on information available on the Civil State Police statistics site: http://www.ssp.sp.gov.br/Estatistica/Pesquisa.aspx

• Period available:

  • Crime Monthly Occurrences and Policy productivity: 2001 - May, 2021 Note: in both datasets, decimal separator is a point (".") .

  • Crime rates (annual rates available): 1999 -2020 (available for some of the cities) Note: in this dataset, decimal separator is a comma (",") .

• Data and labels in brazilian portuguese.

• Information about crime type interpretation (available only in brazilian portuguese) in: http://www.ssp.sp.gov.br/Estatistica/download/manual.pdf

• Datasets prepared with Selenium (webscraping) and Pandas libraries in Python.

Author: Dalciana B. Waller https://github.com/DBWALLER

The graph illustrates the murder rate in the United States from 1985 to 2025. The x-axis represents the years, labeled with two-digit abbreviations from '85 to '25, while the y-axis shows the annual murder rate per 100,000 individuals. Throughout this 41-year period, the murder rate fluctuates between a high of 10.66 in 1991 and a low of 4.7 in 2014. Overall, the data reveals a significant downward trend in the murder rate from the mid-1980s, reaching its lowest point in the mid-2010s, followed by slight increases in the most recent years.

This dataset captures a comprehensive snapshot of criminal activities across multiple Indian cities from 2020 to 2024. It includes detailed information about the type of crime, the date and time of occurrence, the weapon used, victim demographics, and the level of police deployment. With crimes ranging from identity theft to homicide, this dataset offers valuable insights for researchers, policymakers, and law enforcement agencies aiming to understand crime patterns and improve public safety. The data also includes information on whether the case was closed, providing a clear view of crime resolution rates across cities

This table contains figures on the number of registered crimes per month and per year. These are broken down by type of crime and by place. Attempts are also included in the recorded crimes. For some crimes (e.g. murder/homicide), this results in a much higher number than just the number of completed crimes.

Since July 2018, it is no longer possible to record multiple offences, which are related to each other (concurrence), in one registration. An example of this is a street robbery in which a firearm (gun possession) is used. If several offences occur in one registration, only the most serious offence was counted before July 2018. As a result of this adjustment, a number of offences show an increase compared to 2018. This mainly concerns trespassing, special laws including money laundering, arms trafficking including possession of weapons, drug trafficking, violation of public order and other social integrity including insults. The increase was therefore mainly visible in the last 6 months of 2018. This adjustment has only a limited impact on the total number of crimes. For the whole of 2018, this causes an increase of approximately 1.0%. Since 30 April 2020, it is possible to report WhatsApp fraud via the Internet (also known as friend-in-emergency fraud). This was immediately used extensively. In the months of May to December 2020, approximately 20,000 reports of WhatsApp fraud were made.

The number of registered crimes fireworks 2023 is not final. In the first half of 2024, many incidents with retroactive effect will still be classified as a criminal offence and included in the census.

Data available from: 2012

Status of figures: The figures in this table are regularly updated. This may result in minor differences with previous publications. Updating the figures is necessary, for example, in order to be able to retroactively process the reclassification of municipalities or the adjustment of coding.

Changes as of 15 November 2024: Figures for October have been added.

When will there be new figures? The figures for November are added on 16 December.

GapMaps offers an advanced and reliable Crime Risk dataset sourced from Applied Geographic Solutions (AGS), a trusted provider of premium demographic insights with over 20 years of experience. Leveraged by thousands of businesses, AGS use advanced statistical methodologies and a rolling seven-year database of FBI and local agency statistics to provide a highly accurate view of the relative risk of specific crime types for any geographic area empowering organizations to make informed decisions in areas such as insurance, urban planning, and real estate.

The AGS Crime Risk dataset includes: - Standardised indexes for a range of serious crimes against both persons and property such as murder, rape, robbery, assault, burglary, theft, and motor vehicle theft - Aggregate measures of crime risk, including crimes against persons, crimes against property, and overall crime risk, offering a comprehensive overview of an area’s safety. - 5-Year Projections: Added in 2020, these projections enhance the dataset by forecasting future crime risks, providing valuable insights for long-term planning. - High-Resolution Data: Crime risk indexes are available at the block group level, allowing insurers to identify variations in crime risk across specific land uses such as motor vehicle theft from parking structures.

Use cases for Crime Risk data in Insurance include: 1) Help determine the likelihood of policyholders filing claims. This information allows them to price policies appropriately. Higher crime risk areas typically result in higher premiums to account for the increased likelihood of claims. 2) Provide recommendations for loss prevention measures based on crime risk assessments. This could include installing security systems, using better lighting, or employing security personnel. Effective loss prevention can reduce the frequency and severity of claims. 3) Insurance underwriters can establish sufficient premiums to cover potential claims. Misjudging crime risk could lead to financial losses if claims exceed the collected premiums. This stability is essential for the insurer's long-term viability. 4) Offer competitive premiums while maintaining profitability. This can attract more customers and increase market share.

Methodology: The primary source of CrimeRisk is a careful compilation and analysis of the FBI Uniform Crime Report databases. On an annual basis, the FBI collects data from each of about 16,000 separate law enforcement jurisdictions at the city, county, and state levels and compiles these into its annual Uniform Crime Report (UCR). While useful, the UCR provides detailed data only for the largest cities, counties, and metropolitan areas. A complex set of statistical models are used to estimate and forecast risk of each individual crime type by using land use data in conjunction with demographic and business characteristics.

This dataset reflects incidents of crime in the City of Los Angeles dating back to 2020. This data is transcribed from original crime reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

The dataset you provided appears to contain information related to reported crimes, with each row representing a specific crime incident. Below, I'll describe the meaning and potential content of each column in the dataset:

1. DR_NO: This column likely represents a unique identifier or reference number for each reported crime incident. It helps in tracking and referencing individual cases.

2. Date Rptd: This column stores the date when the crime was reported to law enforcement authorities. It marks the date when the incident came to their attention.

3. DATE OCC: This column indicates the date when the crime actually occurred or took place. It represents the day when the incident happened.

4. TIME OCC: This column records the time of day when the crime occurred. It provides a timestamp for the incident.

5. AREA: This column may represent a specific geographical area or jurisdiction within a larger region where the crime took place. It categorizes the incident's location.

6. AREA NAME: This column likely contains the name or label of the larger area or district that encompasses the specific area where the crime occurred.

7. Rpt Dist No: This column might represent a reporting district number or code within the specified area. It provides additional location details.

8. Part 1-2: This column could be related to the type or category of crime reported. "Part 1" crimes typically include serious offenses like homicide, robbery, etc., while "Part 2" crimes may include less serious offenses.

9. Crm Cd: This column may contain a numerical code representing the specific type of crime that was committed. Each code corresponds to a distinct category of criminal activity.

10. Crm Cd Desc: This column likely contains a textual description or label for the crime type identified by the "Crm Cd."

11. Mocodes: This column might store additional information or details related to the modus operandi (MO) of the crime, providing insights into how the crime was committed.

12. Vict Age: This column records the age of the victim involved in the crime.

13. Vict Sex: This column indicates the gender or sex of the victim.

14. Vict Descent: This column might represent the ethnic or racial background of the victim.

15. Premis Cd: This column could contain a numerical code representing the type of premises where the crime occurred, such as a residence, commercial establishment, or public place.

16. Premis Desc: This column likely contains a textual description or label for the type of premises identified by the "Premis Cd."

17. Weapon Used Cd: This column may indicate whether a weapon was used in the commission of the crime and, if so, it could provide a numerical code for the type of weapon.

18. Weapon Desc: This column likely contains a textual description or label for the type of weapon identified by the "Weapon Used Cd."

19. Status: This column could represent the current status or disposition of the reported crime, such as "open," "closed," "under investigation," etc.

20. Status Desc: This column likely contains a textual description or label for the status of the reported crime.

21. Crm Cd 1, Crm Cd 2, Crm Cd 3, Crm Cd 4: These columns might provide additional numerical codes for multiple crime categories associated with a single incident.

22. LOCATION: This column likely describes the specific location or address where the crime occurred, providing detailed location information.

23. Cross Street: This column might include the name of a cross street or intersection near the crime location, offering additional context.

24. LAT: This column stores the latitude coordinate of the crime location, allowing for precise geospatial mapping.

25. LON: This column contains the longitude coordinate of the crime location, complementing the latitude for accurate geolocation.

Overall, this dataset appears to be a comprehensive record of reported crimes, providing valuable information about the nature of each incident, the location, and various details related to the victims, perpetrators, and circumstances surrounding the crimes. It can be a valuable resource for crime analysis, law enforcement, and public safety research.

In 2020, the number of known robberies committed in cities in the United States amounted to 183,590 cases. A further 677,169 burglaries were committed in cities in the United States in that same year.