39 datasets found
  1. d

    DC Crime Cards

    • catalog.data.gov
    • opendata.dc.gov
    • +1more
    Updated Feb 5, 2025
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    City of Washington, DC (2025). DC Crime Cards [Dataset]. https://catalog.data.gov/dataset/dc-crime-cards
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    Dataset updated
    Feb 5, 2025
    Dataset provided by
    City of Washington, DC
    Area covered
    Washington
    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.

  2. d

    Crime Incidents in 2017

    • catalog.data.gov
    • opendata.dc.gov
    • +2more
    Updated Feb 4, 2025
    + more versions
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    Metropolitan Police Department (2025). Crime Incidents in 2017 [Dataset]. https://catalog.data.gov/dataset/crime-incidents-in-2017
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    Dataset updated
    Feb 4, 2025
    Dataset provided by
    Metropolitan Police Department
    Description

    The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

  3. d

    Crime Incidents in the Last 30 Days

    • catalog.data.gov
    • opendata.dc.gov
    • +4more
    Updated Jun 18, 2025
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    Metropolitan Police Department (2025). Crime Incidents in the Last 30 Days [Dataset]. https://catalog.data.gov/dataset/crime-incidents-in-the-last-30-days
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    Dataset updated
    Jun 18, 2025
    Dataset provided by
    Metropolitan Police Department
    Description

    The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit https://crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

  4. 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.

  5. d

    Crime Incidents in 2022

    • opendata.dc.gov
    • catalog.data.gov
    • +3more
    Updated Jan 1, 2022
    + more versions
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    City of Washington, DC (2022). Crime Incidents in 2022 [Dataset]. https://opendata.dc.gov/items/f9cc541fc8c04106a05a1a4f1e7e813c
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    Dataset updated
    Jan 1, 2022
    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

    The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

  6. d

    Crime Incidents in 2025

    • opendata.dc.gov
    • catalog.data.gov
    • +1more
    Updated Apr 29, 2015
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    City of Washington, DC (2015). Crime Incidents in 2025 [Dataset]. https://opendata.dc.gov/datasets/crime-incidents-in-2025/about
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    Dataset updated
    Apr 29, 2015
    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

    The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

  7. a

    District Gun Violence Dashboards

    • datahub-dc-dcgis.hub.arcgis.com
    • opendata.dc.gov
    • +1more
    Updated Mar 17, 2025
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    City of Washington, DC (2025). District Gun Violence Dashboards [Dataset]. https://datahub-dc-dcgis.hub.arcgis.com/items/35545690a30846bd95d9a29b81e379e3
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    Dataset updated
    Mar 17, 2025
    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

    The Office of Gun Violence Prevention (OGVP) shares real-time gun violence data to increase government transparency, improve the public's awareness, and support community-based gun violence prevention and reduction partners. All District crime data is available through Crime Cards. The dashboards below focus on gun violence only. The data in these dashboards is updated daily at 7:40AM with the incidents from the day before. View data covering 7-Day Look-back of Gun Violence and Year-to-date Gun Violence.All statistics presented here are based on preliminary DC criminal code offense definitions. The data do not represent official statistics submitted to the FBI under the Uniform Crime Reporting program (UCR) or National Incident Based Reporting System (NIBRS). All preliminary offenses are coded based on DC criminal code and not the FBI offense classifications. Please understand that any comparisons between MPD preliminary data as published on this website and the official crime statistics published by the FBI under the Uniform Crime Reporting Program (UCR) are inaccurate and misleading. The MPD does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information. The MPD will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. Read complete data notes at buildingblocks.dc.gov/data.

  8. Felony Crime Incidents in 2016

    • catalog.data.gov
    • opendata.dc.gov
    • +4more
    Updated Feb 5, 2025
    + more versions
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    Metropolitan Police Department (2025). Felony Crime Incidents in 2016 [Dataset]. https://catalog.data.gov/dataset/felony-crime-incidents-in-2016-02202
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    Dataset updated
    Feb 5, 2025
    Dataset provided by
    Metropolitan Police Department of the District of Columbiahttps://mpdc.dc.gov/
    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.

  9. a

    Police Service Area Details

    • hub.arcgis.com
    • datasets.ai
    • +3more
    Updated Aug 10, 2017
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    City of Washington, DC (2017). Police Service Area Details [Dataset]. https://hub.arcgis.com/app/DCGIS::police-service-area-details
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    Dataset updated
    Aug 10, 2017
    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

    A web map used for the Police Service Area Details web application.In addition to Police Districts, every resident lives in a Police Service Area (PSA), and every PSA has a team of police officers and officials assigned to it. Residents should get to know their PSA team members and learn how to work with them to fight crime and disorder in their neighborhoods. Each police district has between seven and nine PSAs. There are a total of 56 PSAs in the District of Columbia.Printable PDF versions of each district map are available on the district pages. Residents and visitors may also access the PSA Finder to easily locate a PSA and other resources within a geographic area. Just enter an address or place name and click the magnifying glass to search, or just click on the map. The results will provide the geopolitical and public safety information for the address; it will also display a map of the nearest police station(s).Each Police Service Area generally holds meetings once a month. To learn more about the meeting time and location in your PSA, please contact your Community Outreach Coordinator. To reach a coordinator, choose your police district from the list below. The coordinators are included as part of each district's Roster.Visit https://mpdc.dc.gov for more information.

  10. a

    Bias Crime

    • private-demo-dcdev.opendata.arcgis.com
    • hub.arcgis.com
    • +1more
    Updated Feb 21, 2024
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    City of Washington, DC (2024). Bias Crime [Dataset]. https://private-demo-dcdev.opendata.arcgis.com/items/452087bce8c749998cee5598bc73bbf2
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    Dataset updated
    Feb 21, 2024
    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

    It is important for the community to understand what is – and is not – a hate crime. First and foremost, the incident must be a crime. Although that may seem obvious, most speech is not a hate crime, regardless of how offensive it may be. In addition, a hate crime is not a crime, but a possible motive for a crime.It can be difficult to establish a motive for a crime. Therefore, the classification as a hate crime is subject to change as an investigation proceeds – even as prosecutors continue an investigation. If a person is found guilty of a hate crime, the court may fine the offender up to 1½ times the maximum fine and imprison him or her for up to 1½ times the maximum term authorized for the underlying crime.While the District strives to reduce crime for all residents of and visitors to the city, hate crimes can make a particular community feel vulnerable and more fearful. This is unacceptable, and is the reason everyone must work together not just to address allegations of hate crimes, but also to proactively educate the public about hate crimes.The figures in this data align with DC Official Code 22-3700. Because the DC statute differs from the FBI Uniform Crime Reporting (UCR) and National Incident-Based Reporting System (NIBRS) definitions, these figures may be higher than those reported to the FBI.Each month, an MPD team reviews crimes that have been identified as potentially motivated by hate/bias to determine whether there is sufficient information to support that designation. The data in this document is current through the end of the most recent month.The hate crimes dataset is not an official MPD database of record and may not match details in records pulled from the official Records Management System (RMS).Unknown or blank values in the Targeted Group field may be present prior to 2016 data. As of January 2022, an offense with multiple bias categories would be reflected as such.Data is updated on the 15th of every month.

  11. UCI Communities and Crime Unnormalized Data Set

    • kaggle.com
    Updated Feb 21, 2018
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    Kavitha (2018). UCI Communities and Crime Unnormalized Data Set [Dataset]. https://www.kaggle.com/datasets/kkanda/communities%20and%20crime%20unnormalized%20data%20set/discussion?sort=undefined
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 21, 2018
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Kavitha
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    Context

    Introduction: The dataset used for this experiment is real and authentic. The dataset is acquired from UCI machine learning repository website [13]. The title of the dataset is ‘Crime and Communities’. It is prepared using real data from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crimedata from the 1995 FBI UCR [13]. This dataset contains a total number of 147 attributes and 2216 instances.

    The per capita crimes variables were calculated using population values included in the 1995 FBI data (which differ from the 1990 Census values).

    Content

    The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units. The crime attributes (N=18) that could be predicted are the 8 crimes considered 'Index Crimes' by the FBI)(Murders, Rape, Robbery, .... ), per capita (actually per 100,000 population) versions of each, and Per Capita Violent Crimes and Per Capita Nonviolent Crimes)

    predictive variables : 125 non-predictive variables : 4 potential goal/response variables : 18

    Acknowledgements

    http://archive.ics.uci.edu/ml/datasets/Communities%20and%20Crime%20Unnormalized

    U. S. Department of Commerce, Bureau of the Census, Census Of Population And Housing 1990 United States: Summary Tape File 1a & 3a (Computer Files),

    U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

    U.S. Department of Justice, Bureau of Justice Statistics, Law Enforcement Management And Administrative Statistics (Computer File) U.S. Department Of Commerce, Bureau Of The Census Producer, Washington, DC and Inter-university Consortium for Political and Social Research Ann Arbor, Michigan. (1992)

    U.S. Department of Justice, Federal Bureau of Investigation, Crime in the United States (Computer File) (1995)

    Inspiration

    Your data will be in front of the world's largest data science community. What questions do you want to see answered?

    Data available in the dataset may not act as a complete source of information for identifying factors that contribute to more violent and non-violent crimes as many relevant factors may still be missing.

    However, I would like to try and answer the following questions answered.

    1. Analyze if number of vacant and occupied houses and the period of time the houses were vacant had contributed to any significant change in violent and non-violent crime rates in communities

    2. How has unemployment changed crime rate(violent and non-violent) in the communities?

    3. Were people from a particular age group more vulnerable to crime?

    4. Does ethnicity play a role in crime rate?

    5. Has education played a role in bringing down the crime rate?

  12. d

    Crime Mapper: Wakefield (DC) Local Government Area

    • data.gov.au
    • data.wu.ac.at
    html
    Updated Jul 13, 2016
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    Attorney-General's Department (2016). Crime Mapper: Wakefield (DC) Local Government Area [Dataset]. https://data.gov.au/dataset/ds-sa-b748e223-c41c-4fd9-a102-4aa0bf616a11
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Jul 13, 2016
    Dataset provided by
    Attorney-General's Departmenthttp://www.ag.gov.au/
    License

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

    Area covered
    Washington, Wakefield
    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 …Show full descriptionCrime 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

  13. w

    Crime Mapper: Goyder (DC) Local Government Area

    • data.wu.ac.at
    • data.gov.au
    html
    Updated Jul 14, 2016
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    Attorney-General's Dept (2016). Crime Mapper: Goyder (DC) Local Government Area [Dataset]. https://data.wu.ac.at/schema/data_sa_gov_au/YjczZTA5OTYtNzJkMi00MmY0LTg1NDMtNDJhNjUxMGYwMDgy
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    html(77478.0)Available download formats
    Dataset updated
    Jul 14, 2016
    Dataset provided by
    Attorney-General's Dept
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    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

  14. w

    Crime Mapper: Alexandrina (DC) Local Government Area

    • data.wu.ac.at
    • data.gov.au
    html
    Updated Jul 14, 2016
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    Attorney-General's Dept (2016). Crime Mapper: Alexandrina (DC) Local Government Area [Dataset]. https://data.wu.ac.at/schema/data_sa_gov_au/YzUyOGQ1ZDEtNDc4NC00NjgwLWIxMGItZDYxMTk2ZDRlOTNi
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    html(78146.0)Available download formats
    Dataset updated
    Jul 14, 2016
    Dataset provided by
    Attorney-General's Dept
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    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

  15. 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

  16. 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

  17. Crime Incidents in 2010

    • catalog.data.gov
    • opendata.dc.gov
    • +2more
    Updated Feb 4, 2025
    + more versions
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    Metropolitan Police Department (2025). Crime Incidents in 2010 [Dataset]. https://catalog.data.gov/dataset/crime-incidents-in-2010
    Explore at:
    Dataset updated
    Feb 4, 2025
    Dataset provided by
    Metropolitan Police Department of the District of Columbiahttps://mpdc.dc.gov/
    Description

    The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

  18. w

    Crime Mapper: Elliston (DC) Local Government Area

    • data.wu.ac.at
    • data.gov.au
    html
    Updated Oct 27, 2016
    + more versions
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    South Australian Governments (2016). Crime Mapper: Elliston (DC) Local Government Area [Dataset]. https://data.wu.ac.at/schema/data_gov_au/NzcyY2E2NmItMjM2Zi00MjA1LWI3ZDItMGMzMWIxYTM5NGY5
    Explore at:
    html(76550.0)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

  19. w

    Crime Mapper: Kimba (DC) Local Government Area

    • data.wu.ac.at
    html
    Updated Jul 14, 2016
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    Attorney-General's Dept (2016). Crime Mapper: Kimba (DC) Local Government Area [Dataset]. https://data.wu.ac.at/schema/data_sa_gov_au/NzYyMzAwODAtMWI2Zi00MTlhLWFkYmEtOTljYzcyM2EyMzE0
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    html(76340.0)Available download formats
    Dataset updated
    Jul 14, 2016
    Dataset provided by
    Attorney-General's Dept
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    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

  20. w

    Crime Mapper: Renmark Paringa (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: Renmark Paringa (DC) Local Government Area [Dataset]. https://data.wu.ac.at/odso/data_gov_au/Y2E4NmI2NGUtMzY1MS00NWJmLTk4YWQtMDFhM2MxYTlhNTVm
    Explore at:
    html(78018.0)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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City of Washington, DC (2025). DC Crime Cards [Dataset]. https://catalog.data.gov/dataset/dc-crime-cards

DC Crime Cards

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Dataset updated
Feb 5, 2025
Dataset provided by
City of Washington, DC
Area covered
Washington
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.

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