This map shows a comparable measure of crime in the United States. The crime index compares the average local crime level to that of the United States as a whole. An index of 100 is average. A crime index of 120 indicates that crime in that area is 20 percent above the national average.The crime data is provided by Applied Geographic Solutions, Inc. (AGS). AGS created models using the FBI Uniform Crime Report databases as the primary data source and using an initial range of about 65 socio-economic characteristics taken from the 2000 Census and AGS’ current year estimates. The crimes included in the models include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. The total crime index incorporates all crimes and provides a useful measure of the relative “overall” crime rate in an area. However, these are unweighted indexes, meaning that a murder is weighted no more heavily than a purse snatching in the computations. The geography depicts states, counties, Census tracts and Census block groups. An urban/rural "mask" layer helps you identify crime patterns in rural and urban settings. The Census tracts and block groups help identify neighborhood-level variation in the crime data.------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

View the dataFor best results:View the dashboard in full screen.Use Chrome or Firefox as your browser.Read the dataData viewsThere are two views with this dashboard. You can toggle between them by clicking the button on the top right of the dashboard.The views are:Crime summary viewCrime details viewViewing modesThere are ways to view with this dashboard. You can toggle between them by clicking the button.The modes to view the data are:DarkLightSearch the dataCrime summary viewThe search options allow you to select:Location: Options are citywide, each of the precincts, each of the wards, or each of the neighborhoods.Select Crime: Select a type of crime to display.Select Chart: Select a way to display the crime data.Crime detail viewThe search options allow you to select:Date range: Select a custom date range.Location: Options are citywide, each of the precincts, each of the wards, or each of the neighborhoods.Select Type: Select a type of crime.Select Categories: Select one or more categories of crime to display.Select Details: Select one or more details to filter the data displayed.Select Chart: Select a way to display the crime data.View dashboard data definitions and detailed directionsView the open data set

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Douglas County, MN (DISCONTINUED) (FBITC027041) from 2005 to 2021 about Douglas County, MN; crime; violent crime; property crime; MN; and USA.

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.

The major objective of this study was to examine how physical characteristics of commercial centers and demographic characteristics of residential areas contribute to crime and how these characteristics affect reactions to crime in mixed commercial-residential settings. Information on physical characteristics includes type of business, store hours, arrangement of buildings, and defensive modifications in the area. Demographic variables cover racial composition, average household size and income, and percent change of occupancy. The crime data describe six types of crime: robbery, burglary, assault, rape, personal theft, and shoplifting.

In February 2019, we updated the neighborhood assignment with regards to the new police record system.

The data set is refreshed on the third day of the month at 8:45 AM. The website will reflect the last time the data set was updated and the total count of rows. The grid on the “Data” tab will display the up to date data. However, in certain situations there is a delay in the refresh of the downloadable data file. Sometimes the downloadable file does not reflect the updates to the data in the portal. After a delay (duration has been variable; up to 30 minutes), the file will be updated on the server and then downloads will include the updated data.

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.

Police Incidents for 2018 from the Police Information Management System (PIMS) starting in June 2018. The reportedDateTime field is shown in UTC.Field Descriptions

Begin Date: Date incident began. Time in the field is UTC not local, so a separate column is created for accurate time information. Time: Begin date time field. CCN: A concatenation of the 4 digit year in which the incident was created, followed by a dash and then a 6 digit number of sequence for the agency. The MP at the beginning signifies a report taken by Minneapolis Police. This is used because Minneapolis shares the record management system with the University of Minnesota Police ControlNbr: A unique identifier for case. Offense: Code of criminal act reported. Description: Description of the criminal code of incident. EnteredDate: The timestamp of when the incident was created in the system. GBSID: The anonymized street centerline ID. LastChanged: Date the record was last altered in system. LastUpdateDate: Date the record was last moved to open data. Lat: The anonymized latitude of the incident. Long: The anonymized longitude of the incident. Neighborhood: The neighborhood of the incident. Note that occasionally due to the anonymization process, if a point is on the boundary of a neighborhood, it may fall into either neighborhood. OBJECTID: A unique identifier for open data portal. Precinct: The police precinct of the incident. Note that occasionally due to the anonymization process, if a point is on the boundary of a precinct, it may fall into either precinct. PublicAddress: Address of incident anonymized to the block. ReportedDate: Date incident is reported to police. UCRCode: Code that signifies the type of crime that was committed.

1 = MURDER 3 = RAPE 4 = ROBBERY 5 = ASSAULT 6 = BURGLARY 7 = LARCENY 8 = AUTO THEFT 10 = ARSON

Dataset showing reported crime counts and rates by offense category for Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties. Crime rates are calculated using Census estimates of each county's resident population.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Cook County, MN (DISCONTINUED) (FBITC027031) from 2006 to 2021 about Cook County, MN; crime; violent crime; property crime; MN; and USA.

Recognizing that violence can be an intractable problem in many communities and that there are numerous approaches to both an immediate violence problem and the range of root causes behind violence, the National Institute of Justice funded an investigation into what factors underlie violence and efforts being implemented to address those factors and potentially reduce violence at the community level. In this mixed methods study, the RAND Corporation drew on data from key informant interviews, community surveys, administrative data, and geographic data to examine specific factors that contribute to violence, as well as a range of anti-violence efforts that have been used to address violence levels in two U.S. communities: the Bullseye area of Durham, North Carolina, and the Northside (North Minneapolis) neighborhood of Minneapolis, Minnesota. Specifically, the research project aimed to answer the following questions: What are community level factors that can contribute to persistent violence? What are the key factors in both cities that distinguish high violent crime areas compared to low violent crime areas? This collection contains final analytic datasets for Durham (DS1) and Minneapolis (DS2), violent crime rate data (DS3), community survey data for Durham (DS4) and Minneapolis (DS5), and multiple datasets containing community-level contextual factors from the American Community Survey (ACS) and geographical data from the U.S. Census Bureau (2009-2018) that were used to build the final analytic datasets (DS6-DS11). Qualitative data from key informant interviews and GIS data are not available for download at this time. Access to Durham and Minneapolis community survey data is restricted.

This study is a secondary analysis of CRIME, FEAR, AND CONTROL IN NEIGHBORHOOD COMMERCIAL CENTERS: MINNEAPOLIS AND ST. PAUL, 1970-1982 (ICPSR 8167), which was designed to explore the relationship between small commercial centers and their surrounding neighborhoods. Some variables from the original study were recoded and new variables were created in order to examine the impact of community structure, crime, physical deterioration, and other signs of incivility on residents' and merchants' cognitive and emotional responses to disorder. This revised collection sought to measure separately the contextual and individual determinants of commitment to locale, informal social control, responses to crime, and fear of crime. Contextual determinants included housing, business, and neighborhood characteristics, as well as crime data on robbery, burglary, assault, rape, personal theft, and shoplifting and measures of pedestrian activity in the commercial centers. Individual variables were constructed from interviews with business leaders and surveys of residents to measure victimization, fear of crime, and attitudes toward businesses and neighborhoods. Part 1, Area Data, contains housing, neighborhood, and resident characteristics. Variables include the age and value of homes, types of businesses, amount of litter and graffiti, traffic patterns, demographics of residents such as race and marital status from the 1970 and 1980 Censuses, and crime data. Many of the variables are Z-scores. Part 2, Pedestrian Activity Data, describes pedestrians in the small commercial centers and their activities on the day of observation. Variables include primary activity, business establishment visited, and demographics such as age, sex, and race of the pedestrians. Part 3, Business Interview Data, includes employment, business, neighborhood, and attitudinal information. Variables include type of business, length of employment, number of employees, location, hours, operating costs, quality of neighborhood, transportation, crime, labor supply, views about police, experiences with victimization, fear of strangers, and security measures. Part 4, Resident Survey Data, includes measures of commitment to the neighborhood, fear of crime, attitudes toward local businesses, perceived neighborhood incivilities, and police contact. There are also demographic variables, such as sex, ethnicity, age, employment, education, and income.

A leading sociological theory of crime is the "routine activities" approach (Cohen and Felson, 1979). The premise of this theory is that the rate of occurrence of crime is affected by the convergence in time and space of three elements: motivated offenders, suitable targets, and the absence of guardianship against crime. The purpose of this study was to provide empirical evidence for the routine activities theory by investigating criminal data on places. This study deviates from traditional criminology research by analyzing places instead of collectivities as units of spatial analysis. There are two phases to this study. The purpose of the first phase was to test whether crime occurs randomly in space or is concentrated in "hot spots". Telephone calls for police service made in 1985 and 1986 to the Minneapolis Police Department were analyzed for patterns and concentration of repeat calls and were statistically tested for randomness. For the second phase of the study, two field experiments were designed to test the effectiveness of a proactive police strategy called Repeat Complaint Address Policing (RECAP). Samples of residential and commercial addresses that generated the most concentrated and most frequent repeat calls were divided into groups of experimental and control addresses, resulting in matched pairs. The experimental addresses were then subjected to a more focused proactive policing. The purposes of the RECAP experimentation were to test the effectiveness of proactive police strategy, as measured through the reduction in the incidence of calls to the police and, in so doing, to provide empirical evidence on the routine activities theory. Variables in this collection include the number of calls for police service in both 1986 and 1987 to the control addresses for each experimental pair, the number of calls for police service in both 1986 and 1987 to the experimental addresses for each experimental pair, numerical differences between calls in 1987 and 1986 for both the control addresses and experimental addresses in each experimental pair, percentage difference between calls in 1987 and 1986 for both the control addresses and the experimental addresses in each experimental pair, and a variable that indicates whether the experimental pair was used in the experimental analysis. The unit of observation for the first phase of the study is the recorded telephone call to the Minneapolis Police Department for police service and assistance. The unit of analysis for the second phase is the matched pair of control and experimental addresses for both the residential and commercial address samples of the RECAP experiments.

This data collection was designed to test the "incivilities thesis": that incivilities such as extant neighborhood physical conditions of disrepair or abandonment and troubling street behaviors contribute to residents' concerns for personal safety and their desire to leave their neighborhood. The collection examines between-individual versus between-neighborhood and between-city differences with respect to fear of crime and neighborhood commitment and also explores whether some perceived incivilities are more relevant to these outcomes than others. The data represent a secondary analysis of five ICPSR collections: (1) CHARACTERISTICS OF HIGH AND LOW CRIME NEIGHBORHOODS IN ATLANTA, 1980 (ICPSR 7951), (2) CRIME CHANGES IN BALTIMORE, 1970-1994 (ICPSR 2352), (3) CITIZEN PARTICIPATION AND COMMUNITY CRIME PREVENTION, 1979: CHICAGO METROPOLITAN AREA SURVEY (ICPSR 8086), (4) CRIME, FEAR, AND CONTROL IN NEIGHBORHOOD COMMERCIAL CENTERS: MINNEAPOLIS AND ST. PAUL, 1970-1982 (ICPSR 8167), and (5) TESTING THEORIES OF CRIMINALITY AND VICTIMIZATION IN SEATTLE, 1960-1990 (ICPSR 9741). Part 1, Survey Data, is an individual-level file that contains measures of residents' fear of victimization, avoidance of dangerous places, self-protection, neighborhood satisfaction, perceived incivilities (presence of litter, abandoned buildings, vandalism, and teens congregating), and demographic variables such as sex, age, and education. Part 2, Neighborhood Data, contains crime data and demographic variables from Part 1 aggregated to the neighborhood level, including percentage of the neighborhood that was African-American, gender percentages, average age and educational attainment of residents, average household size and length of residence, and information on home ownership.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in St. Louis County, MN (DISCONTINUED) (FBITC027137) from 2004 to 2021 about St. Louis County, MN; Duluth; crime; violent crime; property crime; MN; and USA.

Police Incidents for 2018 from the Police Information Management System (PIMS) starting in June 2018. The reportedDateTime field is shown in UTC.Field Descriptions

Begin Date: Date incident began. Time in the field is UTC not local, so a separate column is created for accurate time information. Time: Begin date time field. CCN: A concatenation of the 4 digit year in which the incident was created, followed by a dash and then a 6 digit number of sequence for the agency. The MP at the beginning signifies a report taken by Minneapolis Police. This is used because Minneapolis shares the record management system with the University of Minnesota Police ControlNbr: A unique identifier for case. Offense: Code of criminal act reported. Description: Description of the criminal code of incident. EnteredDate: The timestamp of when the incident was created in the system. GBSID: The anonymized street centerline ID. LastChanged: Date the record was last altered in system. LastUpdateDate: Date the record was last moved to open data. Lat: The anonymized latitude of the incident. Long: The anonymized longitude of the incident. Neighborhood: The neighborhood of the incident. Note that occasionally due to the anonymization process, if a point is on the boundary of a neighborhood, it may fall into either neighborhood. OBJECTID: A unique identifier for open data portal. Precinct: The police precinct of the incident. Note that occasionally due to the anonymization process, if a point is on the boundary of a precinct, it may fall into either precinct. PublicAddress: Address of incident anonymized to the block. ReportedDate: Date incident is reported to police. UCRCode: Code that signifies the type of crime that was committed.

1 = MURDER 3 = RAPE 4 = ROBBERY 5 = ASSAULT 6 = BURGLARY 7 = LARCENY 8 = AUTO THEFT 10 = ARSON

The rate of fatal police shootings in the United States shows large differences based on ethnicity. Among Black Americans, the rate of fatal police shootings between 2015 and December 2024 stood at 6.1 per million of the population per year, while for white Americans, the rate stood at 2.4 fatal police shootings per million of the population per year. Police brutality in the United States Police brutality is a major issue in the United States, but recently saw a spike in online awareness and protests following the murder of George Floyd, an African American who was killed by a Minneapolis police officer. Just a few months before, Breonna Taylor was fatally shot in her apartment when Louisville police officers forced entry into her apartment. Despite the repeated fatal police shootings across the country, police accountability has not been adequate according to many Americans. A majority of Black Americans thought that police officers were not held accountable for their misconduct, while less than half of White Americans thought the same. Political opinions Not only are there differences in opinion between ethnicities on police brutality, but there are also major differences between political parties. A majority of Democrats in the United States thought that police officers were not held accountable for their misconduct, while a majority of Republicans that they were held accountable. Despite opposing views on police accountability, both Democrats and Republicans agree that police should be required to be trained in nonviolent alternatives to deadly force.

India Railway Crime: Value of Property Recovered data was reported at 3.100 INR mn in 2017. This records a decrease from the previous number of 47.900 INR mn for 2016. India Railway Crime: Value of Property Recovered data is updated yearly, averaging 38.100 INR mn from Mar 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 51.500 INR mn in 2012 and a record low of 3.100 INR mn in 2017. India Railway Crime: Value of Property Recovered data remains active status in CEIC and is reported by Ministry of Railways. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TB018: Railway Statistics: Railway Crime.

India Railway Crime: Railway Protection Force: Railways Act: Number of Persons Prosecuted data was reported at 23.850 Person mn in 2017. This records an increase from the previous number of 22.470 Person mn for 2016. India Railway Crime: Railway Protection Force: Railways Act: Number of Persons Prosecuted data is updated yearly, averaging 1.790 Person mn from Mar 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 23.850 Person mn in 2017 and a record low of 1.526 Person mn in 2012. India Railway Crime: Railway Protection Force: Railways Act: Number of Persons Prosecuted data remains active status in CEIC and is reported by Ministry of Railways. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TB018: Railway Statistics: Railway Crime.

India Railway Crime: Railway Protection Force: Railways Act: Amount of Fine Realized data was reported at 69.400 INR mn in 2017. This records an increase from the previous number of 63.870 INR mn for 2016. India Railway Crime: Railway Protection Force: Railways Act: Amount of Fine Realized data is updated yearly, averaging 421.200 INR mn from Mar 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 530.900 INR mn in 2014 and a record low of 63.870 INR mn in 2016. India Railway Crime: Railway Protection Force: Railways Act: Amount of Fine Realized data remains active status in CEIC and is reported by Ministry of Railways. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TB018: Railway Statistics: Railway Crime.