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.

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.

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.; ;

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 project sought to investigate a possible relationship between sentencing guidelines and family structure in the United States. The research team developed three research modules that employed a variety of data sources and approaches to understand family destabilization and community distress, which cannot be observed directly. These three research modules were used to discover causal relationships between male withdrawal from productive spheres of the economy and resulting changes in the community and families. The research modules approached the issue of sentencing guidelines and family structure by studying: (1) the flow of inmates into prison (Module A), (2) the role of and issues related to sentencing reform (Module B), and family disruption in a single state (Module C). Module A utilized the Uniform Crime Reporting (UCR) Program data for 1984 and 1993 (Parts 1 and 2), the 1984 and 1993 National Correctional Reporting Program (NCRP) data (Parts 3-6), the Urban Institute's 1980 and 1990 Underclass Database (UDB) (Part 7), the 1985 and 1994 National Longitudinal Survey on Youth (NLSY) (Parts 8 and 9), and county population, social, and economic data from the Current Population Survey, County Business Patterns, and United States Vital Statistics (Parts 10-12). The focus of this module was the relationship between family instability, as measured by female-headed families, and three societal characteristics, namely underclass measures in county of residence, individual characteristics, and flows of inmates. Module B examined the effects of statewide incarceration and sentencing changes on marriage markets and family structure. Module B utilized data from the Current Population Survey for 1985 and 1994 (Part 12) and the United States Statistical Abstracts (Part 13), as well as state-level data (Parts 14 and 15) to measure the Darity-Myers sex ratio and expected welfare income. The relationship between these two factors and family structure, sentencing guidelines, and minimum sentences for drug-related crimes was then measured. Module C used data collected from inmates entering the Minnesota prison system in 1997 and 1998 (Part 16), information from the 1990 Census (Part 17), and the Minnesota Crime Survey (Part 18) to assess any connections between incarceration and family structure. Module C focused on a single state with sentencing guidelines with the goal of understanding how sentencing reforms and the impacts of the local community factors affect inmate family structure. The researchers wanted to know if the aspects of locations that lose marriageable males to prison were more important than individual inmate characteristics with respect to the probability that someone will be imprisoned and leave behind dependent children. Variables in Parts 1 and 2 document arrests by race for arson, assault, auto theft, burglary, drugs, homicide, larceny, manslaughter, rape, robbery, sexual assault, and weapons. Variables in Parts 3 and 4 document prison admissions, while variables in Parts 5 and 6 document prison releases. Variables in Part 7 include the number of households on public assistance, education and income levels of residents by race, labor force participation by race, unemployment by race, percentage of population of different races, poverty rate by race, men in the military by race, and marriage pool by race. Variables in Parts 8 and 9 include age, county, education, employment status, family income, marital status, race, residence type, sex, and state. Part 10 provides county population data. Part 11 contains two different state identifiers. Variables in Part 12 describe mortality data and welfare data. Part 13 contains data from the United States Statistical Abstracts, including welfare and poverty variables. Variables in Parts 14 and 15 include number of children, age, education, family type, gender, head of household, marital status, race, religion, and state. Variables in Part 16 cover admission date, admission type, age, county, education, language, length of sentence, marital status, military status, sentence, sex, state, and ZIP code. Part 17 contains demographic data by Minnesota ZIP code, such as age categories, race, divorces, number of children, home ownership, and unemployment. Part 18 includes Minnesota crime data as well as some demographic variables, such as race, education, and poverty ratio.

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;

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: 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.

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。