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.

In 2023, the District of Columbia had the highest reported violent crime rate in the United States, with 1,150.9 violent crimes per 100,000 of the population. Maine had the lowest reported violent crime rate, with 102.5 offenses per 100,000 of the population. Life in the District The District of Columbia has seen a fluctuating population over the past few decades. Its population decreased throughout the 1990s, when its crime rate was at its peak, but has been steadily recovering since then. While unemployment in the District has also been falling, it still has had a high poverty rate in recent years. The gentrification of certain areas within Washington, D.C. over the past few years has made the contrast between rich and poor even greater and is also pushing crime out into the Maryland and Virginia suburbs around the District. Law enforcement in the U.S. Crime in the U.S. is trending downwards compared to years past, despite Americans feeling that crime is a problem in their country. In addition, the number of full-time law enforcement officers in the U.S. has increased recently, who, in keeping with the lower rate of crime, have also made fewer arrests than in years past.

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.

Police recorded crime figures by Police Force Area and Community Safety Partnership areas (which equate in the majority of instances, to local authorities).

This dataset reflects reported incidents of crime that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. 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 Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@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. Any use of the information for commercial purposes is strictly prohibited. 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 is updated daily.

In 2023, the metropolitan city of Milan ranked first in terms of crime rate, as it recorded 7,100 felonies per 100,000 inhabitants. Furthermore, the provinces of Rome and Florence followed with around 6,000 cases reported. In Milan, burglaries in shops and thefts were much more common than in any other Italian provinces. Frequent car thefts The Southern province of Barletta-Andria-Trani, in the region of Apulia, was the place in Italy with the highest rate of stolen cars. Roughly 697 cases per every 100,000 residents were registered in 2019. Catania had the second-largest rate with about 656 reports. Nationwide, the three most frequently stolen car models belonged to Fiat, the leading Italian vehicle manufacturer. Moreover, a Lancia car model ranked fourth. This company was also part of the Fiat Group, which, however, only sells vehicles in Italy. Mafia associations  In the last years, the number of mafia associations in Italy experienced a decline. However, there are still dozens of mafia-type organizations in the country. The Southern region of Campania was the place faced with the largest amount of crime associations. In total, 67 of such crimes were reported in Campania in 2019.

In 2023, property crime was the most common type of crime committed in the United States, with over 6.41 million offenses reported to the FBI. In the same year, there were around 1.22 million cases of violent crime reported to the FBI, of which there were 19,252 cases of homicide, including murder and nonnegligent manslaughter.

Top 5 Crimes Reported in 2017 listed by Aldermanic District.

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.

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.

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models. The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months. A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study. The statistical datasets consist of Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases. The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

This dataset is a filtered view of LASD-published year-to-date crime data for the City of West Hollywood, updated monthly. It is presented in its raw format and is completely unaltered.

Please contact the Los Angeles Sheriff's Department with any questions regarding the underlying data.

Incident Date = Date the crime incident occurred Incident Reported Date = Date the crime was reported to LASD Category = Incident crime category Stat = A three digit numerical coding system to identify the primary crime category for an incident Stat Desc = The definition of the statistical code number Address (last two digits of # rounded to 00) = The street number, street name, state and zip where the incident occurred Street (last two digits of # rounded to 00) = The street number and street name where the incident occurred City = The city where the incident occurred Zip = The zip code of the location where the incident occurred Incident ID = The URN #, or Uniform Report Number, is a unique # assigned to every criminal and noncriminal incident Reporting District = A geographical area defined by LASD which is within a city or unincorporated area where the incident occurred Seq = Each incident for each station is issued a unique sequence # within a given year Gang Related = Indicates if the crime incident was gang related (column added 08/02/2012) Unit ID = ORI # is a number issued by the FBI for every law enforcement agency Unit Name = Station Name Longitude (truncated to 3 decimals, equivalent to half-block rounding) (column added 01/04/2021) Latitude (truncated to 3 decimals, equivalent to half-block rounding) (column added 01/04/2021) Part Category = Part I Crime or Part II Crime indicator (replaced DELETED column 01/04/2021)

In 2023, a total of ******* violent crimes were committed in Texas, the most out of any U.S. state. New York followed, with ******* violent crimes committed. California, Illinois, and Michigan rounded out the top five states for violent crimes in that year.

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...

Black people were over twice as likely to be arrested as white people – there were 20.4 arrests for every 1,000 black people, and 9.4 for every 1,000 white people.

Incident-based crime statistics (actual incidents, rate per 100,000 population, percentage change in rate, unfounded incidents, percent unfounded, total cleared, cleared by charge, cleared otherwise, persons charged, adults charged, youth charged / not charged), by detailed violations (violent, property, traffic, drugs, other Federal Statutes), police services in Ontario, 1998 to 2024.

In 2023, an estimated 1,21,467 violent crimes occurred in the United States. This is a decrease from the year before, when 1,256,671 violent crimes were reported. Violent crime in the United States The Federal Bureau of Investigation reported that violent crime fell nationwide in the period from 1990 to 2023. Violent crime was at a height of 1.93 million crimes in 1992, but has since reached a low of 1.15 million violent crimes in 2014. When conducting crime reporting, the FBI’s Uniform Crime Reporting Program considered murder, nonnegligent manslaughter, forcible rape, robbery and aggravated assault to be violent crimes, because they are offenses which involve force or threat of violence. In 2023, there were 19,252 reported murder and nonnegligent manslaughter cases in the United States. California ranked first on a list of U.S. states by number of murders, followed by Texas, and Florida.The greatest number of murders were committed by murderers of unknown relationship to their victim. “Girlfriend” was the fourth most common relationship of victim to offender in 2023, with a reported 568 partners murdering their girlfriends that year, while the sixth most common was “wife.” In addition, seven people were murdered by their employees and 12 people were murdered by their employers. The most used murder weapon in 2023 was the handgun, which was used in 7,1 murders that year. According to the FBI, firearms (of all types) were used in more than half of the nation’s murders. The total number of firearms manufactured in the U.S. annually has reached over 13 million units.

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.com

Data Dictionary:

INCIDENT_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence rooms

DATE_REPORTED - the date the incident was reported to LMPD

DATE_OCCURED - the date the incident actually occurred

UOR_DESC - Uniform Offense Reporting code for the criminal act committed

CRIME_TYPE - the crime type category

NIBRS_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/view

UCR_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 occurred

LMPD_BEAT - the LMPD beat in which the incident actually occurred

PREMISE_TYPE - the type of location in which the incident occurred (e.g. Restaurant)

BLOCK_ADDRESS - the location the incident occurred

CITY - the city associated to the incident block location

ZIP_CODE - the zip code associated to the incident block location

ID - Unique identifier for internal database

Contact:

Crime Information Center

CrimeInfoCenterDL@louisvilleky.gov

The graph shows the number of violent crime victims in the United States by gender and type in 2024. The x-axis represents the crime type, while the y-axis indicates the number of male and female victims reported annually.

Context

Approximately 10 people are shot on an average day in Chicago.

http://www.chicagotribune.com/news/data/ct-shooting-victims-map-charts-htmlstory.html http://www.chicagotribune.com/news/local/breaking/ct-chicago-homicides-data-tracker-htmlstory.html http://www.chicagotribune.com/news/local/breaking/ct-homicide-victims-2017-htmlstory.html

Content

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. This data includes unverified reports supplied to the Police Department. 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.

Update Frequency: Daily

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:chicago_crime

https://cloud.google.com/bigquery/public-data/chicago-crime-data

Dataset Source: City of Chicago

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source —https://data.cityofchicago.org — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Ferdinand Stohr from Unplash.

Inspiration

What categories of crime exhibited the greatest year-over-year increase between 2015 and 2016?

Which month generally has the greatest number of motor vehicle thefts?

How does temperature affect the incident rate of violent crime (assault or battery)?

https://cloud.google.com/bigquery/images/chicago-scatter.png" alt=""> https://cloud.google.com/bigquery/images/chicago-scatter.png