12 datasets found
  1. Reported violent crime rate U.S. 2023, by state

    • statista.com
    Updated Nov 14, 2024
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    Statista (2024). Reported violent crime rate U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/200445/reported-violent-crime-rate-in-the-us-states/
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    Dataset updated
    Nov 14, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    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.

  2. a

    DC Crime Cards

    • hub.arcgis.com
    • catalog.data.gov
    Updated Mar 9, 2018
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    City of Washington, DC (2018). DC Crime Cards [Dataset]. https://hub.arcgis.com/datasets/3d553e9f7ba941918537d51f26d746e7
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    Dataset updated
    Mar 9, 2018
    Dataset authored and provided by
    City of Washington, DC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    An interactive public crime mapping application providing DC residents and visitors easy-to-understand data visualizations of crime locations, types and trends across all eight wards. Crime Cards was created by the DC Metropolitan Police Department (MPD) and Office of the Chief Technology Officer (OCTO). Special thanks to the community members who participated in reviews with MPD Officers and IT staff, and those who joined us for the #SaferStrongerSmarterDC roundtable design review. All statistics presented in Crime Cards are based on preliminary DC Index crime data reported from 2009 to midnight of today’s date. They are compiled based on the date the offense was reported (Report Date) to MPD. The application displays two main crime categories: Violent Crime and Property Crime. Violent Crimes include homicide, sex abuse, assault with a dangerous weapon (ADW), and robbery. Violent crimes can be further searched by the weapon used. Property Crimes include burglary, motor vehicle theft, theft from vehicle, theft (other), and arson.CrimeCards collaboration between the Metropolitan Police Department (MPD), the Office of the Chief Technology Officer (OCTO), and community members who participated at the #SafterStrongerSmarterDC roundtable design review.

  3. c

    Data from: Anticipating and Combating Community Decay and Crime in...

    • s.cnmilf.com
    • icpsr.umich.edu
    • +2more
    Updated Mar 12, 2025
    + more versions
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    National Institute of Justice (2025). Anticipating and Combating Community Decay and Crime in Washington, DC, and Cleveland, Ohio, 1980-1990 [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/anticipating-and-combating-community-decay-and-crime-in-washington-dc-and-cleveland-o-1980-4d93c
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    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justice
    Area covered
    Cleveland, Ohio, Washington
    Description

    The Urban Institute undertook a comprehensive assessment of communities approaching decay to provide public officials with strategies for identifying communities in the early stages of decay and intervening effectively to prevent continued deterioration and crime. Although community decline is a dynamic spiral downward in which the physical condition of the neighborhood, adherence to laws and conventional behavioral norms, and economic resources worsen, the question of whether decay fosters or signals increasing risk of crime, or crime fosters decay (as investors and residents flee as reactions to crime), or both, is not easily answered. Using specific indicators to identify future trends, predictor models for Washington, DC, and Cleveland were prepared, based on data available for each city. The models were designed to predict whether a census tract should be identified as at risk for very high crime and were tested using logistic regression. The classification of a tract as a "very high crime" tract was based on its crime rate compared to crime rates for other tracts in the same city. To control for differences in population and to facilitate cross-tract comparisons, counts of crime incidents and other events were converted to rates per 1,000 residents. Tracts with less than 100 residents were considered nonresidential or institutional and were deleted from the analysis. Washington, DC, variables include rates for arson and drug sales or possession, percentage of lots zoned for commercial use, percentage of housing occupied by owners, scale of family poverty, presence of public housing units for 1980, 1983, and 1988, and rates for aggravated assaults, auto thefts, burglaries, homicides, rapes, and robberies for 1980, 1983, 1988, and 1990. Cleveland variables include rates for auto thefts, burglaries, homicides, rapes, robberies, drug sales or possession, and delinquency filings in juvenile court, and scale of family poverty for 1980 through 1989. Rates for aggravated assaults are provided for 1986 through 1989 and rates for arson are provided for 1983 through 1988.

  4. Property crime rate in the U.S. 2023, by state

    • statista.com
    Updated Nov 19, 2024
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    Statista (2024). Property crime rate in the U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/232575/property-crime-rate-in-the-us-by-state/
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    Dataset updated
    Nov 19, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, the property crime rate in the District of Columbia was 4,307.4 reported property crimes per 100,000 residents. New Mexico, Washington, Colorado and Louisiana rounded out the top five states with the highest rates of property crime in that year.

  5. Murder and non-negligent manslaughter rate in the U.S. 2023, by state

    • statista.com
    Updated Nov 12, 2024
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    Statista (2024). Murder and non-negligent manslaughter rate in the U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/232561/murder-and-non-negligent-manslaughter-rate-in-the-us-by-state/
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    Dataset updated
    Nov 12, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, the District of Columbia had the highest rate of murder and non-negligent manslaughter in the United States with a rate of 39 murders or non-negligent manslaughters per 100,000 inhabitants. Louisiana, New Mexico, Alabama, and Tennessee rounded out the top five states with the highest murder rates.

  6. d

    Felony Crime Incidents in 2016

    • datasets.ai
    • opendata.dc.gov
    • +4more
    0, 15, 21, 3, 8
    + more versions
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    District of Columbia, Felony Crime Incidents in 2016 [Dataset]. https://datasets.ai/datasets/felony-crime-incidents-in-2016-02202
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    3, 8, 15, 21, 0Available download formats
    Dataset authored and provided by
    District of Columbia
    Description

    The dataset contains records of felony crime incidents recorded by the District of Columbia Metropolitan Police Department in 2016. Visit mpdc.dc.gov/page/data-and-statistics for more information.

  7. w

    Crime Mapper: Robe (DC) Local Government Area

    • data.wu.ac.at
    • data.gov.au
    html
    Updated Oct 27, 2016
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    South Australian Governments (2016). Crime Mapper: Robe (DC) Local Government Area [Dataset]. https://data.wu.ac.at/schema/data_gov_au/YTUyMWJiZjUtZTY1Yi00Yjg2LTgwNDctZmMyNDI0YzE4Zjg4
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    html(76858.0)Available download formats
    Dataset updated
    Oct 27, 2016
    Dataset provided by
    South Australian Governments
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

  8. w

    Crime Mapper: Mid Murray (DC) Local Government Area

    • data.wu.ac.at
    • data.gov.au
    xls
    Updated Oct 27, 2016
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    South Australian Governments (2016). Crime Mapper: Mid Murray (DC) Local Government Area [Dataset]. https://data.wu.ac.at/schema/data_gov_au/N2E4NjIwNmUtNTY1ZC00NzA0LWE5OTMtNWUyNTM3YzQ3OTcz
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    xlsAvailable download formats
    Dataset updated
    Oct 27, 2016
    Dataset provided by
    South Australian Governments
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

  9. d

    Dashboards and Visualizations Gallery

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Feb 4, 2025
    + more versions
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    City of Washington, DC (2025). Dashboards and Visualizations Gallery [Dataset]. https://catalog.data.gov/dataset/dashboards-and-visualizations-gallery
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    Dataset updated
    Feb 4, 2025
    Dataset provided by
    City of Washington, DC
    Description

    The District of Columbia offers several interactive online visualizations highlighting data and information from various fields of interest such as crime statistics, public school profiles, detailed property information and more. The web visualizations in this group present data coming from agencies across the Government of the District of Columbia. Click each to read a brief introduction and to access the site. This app is embedded in https://opendata.dc.gov/pages/dashboards.

  10. w

    Crime Mapper: Yankalilla (DC) Local Government Area

    • data.wu.ac.at
    • data.gov.au
    html
    Updated Oct 27, 2016
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    South Australian Governments (2016). Crime Mapper: Yankalilla (DC) Local Government Area [Dataset]. https://data.wu.ac.at/odso/data_gov_au/NDdmNjk1ZDUtYmEyNC00OTQ2LWE0YTQtNmZkY2M1ODk1ZGM3
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    html(77782.0)Available download formats
    Dataset updated
    Oct 27, 2016
    Dataset provided by
    South Australian Governments
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

  11. d

    Data from: Drugs and Crime in Public Housing, 1986-1989: Los Angeles,...

    • catalog.data.gov
    • gimi9.com
    • +2more
    Updated Mar 12, 2025
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    National Institute of Justice (2025). Drugs and Crime in Public Housing, 1986-1989: Los Angeles, Phoenix, and Washington, DC [Dataset]. https://catalog.data.gov/dataset/drugs-and-crime-in-public-housing-1986-1989-los-angeles-phoenix-and-washington-dc-72d17
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    Dataset updated
    Mar 12, 2025
    Dataset provided by
    National Institute of Justice
    Area covered
    Phoenix, Los Angeles, Washington
    Description

    This study investigates rates of serious crime for selected public housing developments in Washington, DC, Phoenix, Arizona, and Los Angeles, California, for the years 1986 to 1989. Offense rates in housing developments were compared to rates in nearby areas of private housing as well as to city-wide rates. In addition, the extent of law enforcement activity in housing developments as represented by arrests was considered and compared to arrest levels in other areas. This process allowed both intra-city and inter-city comparisons to be made. Variables cover study site, origin of data, year of event, offense codes, and location of event. Los Angeles files also include police division.

  12. w

    Crime Mapper: Adelaide Hills (DC) Local Government Area

    • data.wu.ac.at
    • data.gov.au
    html
    Updated Oct 27, 2016
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    South Australian Governments (2016). Crime Mapper: Adelaide Hills (DC) Local Government Area [Dataset]. https://data.wu.ac.at/schema/data_gov_au/NGY5Y2NlMjQtMWVkMC00MGNiLTllOWItYmQ0OWFkOGIxN2Nm
    Explore at:
    html(78122.0)Available download formats
    Dataset updated
    Oct 27, 2016
    Dataset provided by
    South Australian Governments
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    Crime Mapper is an online application that provides the geographic distribution of recorded crime across South Australia. Two units of measurement are reported: 1. Number of offences - provides a count of all offences listed on all incident reports recorded by South Australia Police . 2. Rate per 1,000 estimated resident population - provides the number of offences as a rate per 1,000 population residing in each given location. Offences are categorised as follows: • Offences against the person (homicide; major assault; other); • Sexual offences (rape; indecent assault; unlawful sexual intercourse; other); • Robbery and extortion offences (armed robbery; unarmed robbery; extortion); • Offences against property (serious criminal trespass/break and enter; fraud and misappropriation; receiving/illegal possession of stolen goods; larceny/illegal use of a motor vehicle; other larceny; larceny from shops; larceny from a motor vehicle; arson/explosives; property damage and environmental offences); • Offences against good order; • Drug offences (possess/use drugs; sell/trade drugs; produce/manufacture drugs; possess implement for drug use; other); • Driving offences (driving under the influence of alcohol/drugs; dangerous driving; driving licence offences; traffic offences; motor vehicle registration offences; other); or • Other offences. When using Crime Mapper it is important to understand that the statistics it contains may not provide an accurate measure of the true prevalence or incidence of crime in a community. Crime Mapper statistics represent only those offences reported to police or which come to the attention of police. They can, therefore, be influenced by a number of factors, including victim reporting rates, the identification or detection of offences by police (in the case of ‘victimless’ crimes) and police interpretation and decision as to whether a crime has occurred. In addition, Crime Mapper does not include offences that are dealt with by way of expiation (e.g., speeding, littering, etc.). Please also see explanatory notes: http://www.ocsar.sa.gov.au/about2.html

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Statista (2024). Reported violent crime rate U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/200445/reported-violent-crime-rate-in-the-us-states/
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Reported violent crime rate U.S. 2023, by state

Explore at:
3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Nov 14, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2023
Area covered
United States
Description

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.

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