Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Marshall County, MN (DISCONTINUED) (FBITC027089) from 2005 to 2021 about Marshall County, MN; crime; violent crime; property crime; MN; and USA.

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.

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.

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, 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.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Lincoln County, MN (DISCONTINUED) (FBITC027081) from 2005 to 2021 about Lincoln County, MN; crime; violent crime; property crime; MN; and USA.

Dataset showing reported crime by county by general offense type. Crime rates are calculated using Census population estimates.

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.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Stevens County, MN (DISCONTINUED) (FBITC027149) from 2005 to 2021 about Stevens County, MN; crime; violent crime; property crime; MN; and USA.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Crow Wing County, MN was 227.00000 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 Crow Wing County, MN reached a record high of 725.00000 in January of 2004 and a record low of 225.00000 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 Crow Wing County, MN - last updated from the United States Federal Reserve on March of 2025.

Combined Violent and Property Crime Offenses Known to Law Enforcement in Cottonwood County, MN was 11.00000 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 Cottonwood County, MN reached a record high of 55.00000 in January of 2012 and a record low of 11.00000 in January of 2021. 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 Cottonwood County, MN - last updated from the United States Federal Reserve on March of 2025.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Clay County, MN (DISCONTINUED) (FBITC027027) from 2004 to 2021 about Clay County, MN; Fargo; crime; violent crime; property crime; MN; and USA.

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

Combined Violent and Property Crime Offenses Known to Law Enforcement in Dodge County, MN was 137.00000 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 Dodge County, MN reached a record high of 311.00000 in January of 2006 and a record low of 75.00000 in January of 2016. 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 Dodge County, MN - last updated from the United States Federal Reserve on March of 2025.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Fillmore County, MN (DISCONTINUED) (FBITC027045) from 2005 to 2021 about Fillmore County, MN; crime; violent crime; property crime; MN; and USA.

In 2023, the FBI reported that there were 9,284 Black murder victims in the United States and 7,289 white murder victims. In comparison, there were 554 murder victims of unknown race and 586 victims of another race. Victims of inequality? In recent years, the role of racial inequality in violent crimes such as robberies, assaults, and homicides has gained public attention. In particular, the issue of police brutality has led to increasing attention following the murder of George Floyd, an African American who was killed by a Minneapolis police officer. Studies show that the rate of fatal police shootings for Black Americans was more than double the rate reported of other races. Crime reporting National crime data in the United States is based off the Federal Bureau of Investigation’s new crime reporting system, which requires law enforcement agencies to self-report their data in detail. Due to the recent implementation of this system, less crime data has been reported, with some states such as Delaware and Pennsylvania declining to report any data to the FBI at all in the last few years, suggesting that the Bureau's data may not fully reflect accurate information on crime in the United States.

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.; ;

Combined Violent and Property Crime Offenses Known to Law Enforcement in Chisago County, MN was 307.00000 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 Chisago County, MN reached a record high of 705.00000 in January of 2004 and a record low of 85.00000 in January of 2018. 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 Chisago County, MN - last updated from the United States Federal Reserve on March of 2025.

Graph and download economic data for Combined Violent and Property Crime Offenses Known to Law Enforcement in Wright County, MN (DISCONTINUED) (FBITC027171) from 2004 to 2021 about Wright County, MN; crime; violent crime; property crime; Minneapolis; MN; and USA.

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.