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.

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.

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.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Hennepin County, MN was 67.00000 Known Incidents in January of 2021, according to the United States Federal Reserve. Historically, Combined Violent and Property Crime Offenses Known to Law Enforcement in Hennepin County, MN reached a record high of 239.00000 in January of 2004 and a record low of 64.00000 in January of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Combined Violent and Property Crime Offenses Known to Law Enforcement in Hennepin County, MN - last updated from the United States Federal Reserve on June of 2025.

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.

Data on weather and aggravated assaults were obtained to determine whether the curvilinear relationship between temperature and violence previously observed in Minneapolis, Minnesota (E.G. Cohn and J. Rotton, 1997), could be replicated. The data consisted of calls for services received by police in Dallas between January 1, 1994, and December 31, 1995. Controlling for holidays, school closings, time of day, day of the week, season of the year, and their interactions, moderator-variable autoregression analyses indicated that assaults were an inverted U-shaped function of temperature. Replicating past research, the curvilinear relationship was dominant during daylight hours and spring months, whereas linear relationships were observed during nighttime hours and other seasons. The results are interpreted in terms of routine activity theory and the negative affect escape model of aggression.

Mongolia MN: Intentional Homicides: Male: per 100,000 Male data was reported at 8.818 Ratio in 2016. This records a decrease from the previous number of 9.566 Ratio for 2015. Mongolia MN: Intentional Homicides: Male: per 100,000 Male data is updated yearly, averaging 11.529 Ratio from Dec 2007 (Median) to 2016, with 10 observations. The data reached an all-time high of 17.627 Ratio in 2007 and a record low of 8.818 Ratio in 2016. Mongolia MN: Intentional Homicides: Male: per 100,000 Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mongolia – Table MN.World Bank: Health Statistics. Intentional homicides, male are estimates of unlawful male homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

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.

Mongolia MN: Intentional Homicides: per 100,000 People data was reported at 7.200 Ratio in 2015. This records a decrease from the previous number of 7.500 Ratio for 2014. Mongolia MN: Intentional Homicides: per 100,000 People data is updated yearly, averaging 8.800 Ratio from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 15.800 Ratio in 2005 and a record low of 7.100 Ratio in 2012. Mongolia MN: Intentional Homicides: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mongolia – Table MN.World Bank: Health Statistics. Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; Weighted average;

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.

Mongolia MN: Intentional Homicides: Female: per 100,000 Female data was reported at 2.555 Ratio in 2016. This records a decrease from the previous number of 2.861 Ratio for 2015. Mongolia MN: Intentional Homicides: Female: per 100,000 Female data is updated yearly, averaging 3.943 Ratio from Dec 2007 (Median) to 2016, with 10 observations. The data reached an all-time high of 5.062 Ratio in 2007 and a record low of 2.555 Ratio in 2016. Mongolia MN: Intentional Homicides: Female: per 100,000 Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mongolia – Table MN.World Bank: Health Statistics. Intentional homicides, female are estimates of unlawful female homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

India Railway Crime: Railway Protection Force: Railways Act: Number of Persons Convicted data was reported at 22.780 Person mn in 2017. This records an increase from the previous number of 21.330 Person mn for 2016. India Railway Crime: Railway Protection Force: Railways Act: Number of Persons Convicted data is updated yearly, averaging 1.725 Person mn from Mar 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 22.780 Person mn in 2017 and a record low of 1.475 Person mn in 2012. India Railway Crime: Railway Protection Force: Railways Act: Number of Persons Convicted 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.

Railway Crime: Value of Property Recovered在2017达3.100 INR mn，相较于2016的47.900 INR mn有所下降。Railway Crime: Value of Property Recovered数据按每年更新，2011至2017期间平均值为38.100 INR mn，共7份观测结果。该数据的历史最高值出现于2012，达51.500 INR mn，而历史最低值则出现于2017，为3.100 INR mn。CEIC提供的Railway Crime: Value of Property Recovered数据处于定期更新的状态，数据来源于Ministry of Railways，数据归类于India Premium Database的Transportation, Post and Telecom Sector – Table IN.TB018: Railway Statistics: Railway Crime。

Railway Crime: Railway Protection Force: Railways Act: Amount of Fine Realized在2017达69.400 INR mn，相较于2016的63.870 INR mn有所增长。Railway Crime: Railway Protection Force: Railways Act: Amount of Fine Realized数据按每年更新，2011至2017期间平均值为421.200 INR mn，共7份观测结果。该数据的历史最高值出现于2014，达530.900 INR mn，而历史最低值则出现于2016，为63.870 INR mn。CEIC提供的Railway Crime: Railway Protection Force: Railways Act: Amount of Fine Realized数据处于定期更新的状态，数据来源于Ministry of Railways，数据归类于India Premium Database的Transportation, Post and Telecom Sector – Table IN.TB018: Railway Statistics: Railway Crime。