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.

Police Incidents for 2018 from the Police Information Management System (PIMS) starting in June 2018. The reportedDateTime field is shown in UTC.Field Descriptions

Begin Date: Date incident began. Time in the field is UTC not local, so a separate column is created for accurate time information. Time: Begin date time field. CCN: A concatenation of the 4 digit year in which the incident was created, followed by a dash and then a 6 digit number of sequence for the agency. The MP at the beginning signifies a report taken by Minneapolis Police. This is used because Minneapolis shares the record management system with the University of Minnesota Police ControlNbr: A unique identifier for case. Offense: Code of criminal act reported. Description: Description of the criminal code of incident. EnteredDate: The timestamp of when the incident was created in the system. GBSID: The anonymized street centerline ID. LastChanged: Date the record was last altered in system. LastUpdateDate: Date the record was last moved to open data. Lat: The anonymized latitude of the incident. Long: The anonymized longitude of the incident. Neighborhood: The neighborhood of the incident. Note that occasionally due to the anonymization process, if a point is on the boundary of a neighborhood, it may fall into either neighborhood. OBJECTID: A unique identifier for open data portal. Precinct: The police precinct of the incident. Note that occasionally due to the anonymization process, if a point is on the boundary of a precinct, it may fall into either precinct. PublicAddress: Address of incident anonymized to the block. ReportedDate: Date incident is reported to police. UCRCode: Code that signifies the type of crime that was committed.

1 = MURDER 3 = RAPE 4 = ROBBERY 5 = ASSAULT 6 = BURGLARY 7 = LARCENY 8 = AUTO THEFT 10 = ARSON

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

In February 2019, we updated the neighborhood assignment with regards to the new police record system.

The data set is refreshed on the third day of the month at 8:45 AM. The website will reflect the last time the data set was updated and the total count of rows. The grid on the “Data” tab will display the up to date data. However, in certain situations there is a delay in the refresh of the downloadable data file. Sometimes the downloadable file does not reflect the updates to the data in the portal. After a delay (duration has been variable; up to 30 minutes), the file will be updated on the server and then downloads will include the updated data.

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.

A leading sociological theory of crime is the "routine activities" approach (Cohen and Felson, 1979). The premise of this theory is that the rate of occurrence of crime is affected by the convergence in time and space of three elements: motivated offenders, suitable targets, and the absence of guardianship against crime. The purpose of this study was to provide empirical evidence for the routine activities theory by investigating criminal data on places. This study deviates from traditional criminology research by analyzing places instead of collectivities as units of spatial analysis. There are two phases to this study. The purpose of the first phase was to test whether crime occurs randomly in space or is concentrated in "hot spots". Telephone calls for police service made in 1985 and 1986 to the Minneapolis Police Department were analyzed for patterns and concentration of repeat calls and were statistically tested for randomness. For the second phase of the study, two field experiments were designed to test the effectiveness of a proactive police strategy called Repeat Complaint Address Policing (RECAP). Samples of residential and commercial addresses that generated the most concentrated and most frequent repeat calls were divided into groups of experimental and control addresses, resulting in matched pairs. The experimental addresses were then subjected to a more focused proactive policing. The purposes of the RECAP experimentation were to test the effectiveness of proactive police strategy, as measured through the reduction in the incidence of calls to the police and, in so doing, to provide empirical evidence on the routine activities theory. Variables in this collection include the number of calls for police service in both 1986 and 1987 to the control addresses for each experimental pair, the number of calls for police service in both 1986 and 1987 to the experimental addresses for each experimental pair, numerical differences between calls in 1987 and 1986 for both the control addresses and experimental addresses in each experimental pair, percentage difference between calls in 1987 and 1986 for both the control addresses and the experimental addresses in each experimental pair, and a variable that indicates whether the experimental pair was used in the experimental analysis. The unit of observation for the first phase of the study is the recorded telephone call to the Minneapolis Police Department for police service and assistance. The unit of analysis for the second phase is the matched pair of control and experimental addresses for both the residential and commercial address samples of the RECAP experiments.

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.

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.

Financial overview and grant giving statistics of Police Officers Federation Of Minneapolis

This data collection contains information on 330 incidents of domestic violence in Minneapolis. Part 1, Police Data, contains data from the initial police reports filled out after each incident. Parts 2-5 are based on interviews that were conducted with all parties to the domestic assaults. Information for Part 2, Initial Data, was gathered from the victims after the incidents. Part 3, Follow-Up Data, consists of data from follow-up interviews with the victims and with relatives and acquaintances of both victims and suspects. There could be up to 12 contacts per case. Suspect interviews are the source for Part 4, Suspect Data. An experimental section, Part 5, Repeat Data, contains information on repeat incidents of domestic assault from interviews with victims. Parts 2-5 include items such as socioeconomic and demographic data describing the suspect and the victim, relationship (husband, wife, boyfriend, girlfriend, lover, divorced, separated), nature of the argument that spurred the assault, presence or absence of physical violence, and the nature and extent of police contact in the incident. The collection also includes police records, which are the basis for Parts 6-9. These files record the date of the crime, ethnicity of the participants, presence or absence of alcohol or drugs and weapons, and whether a police assault occurred.

Police Incidents for 2018 from the Police Information Management System (PIMS) starting in June 2018. The reportedDateTime field is shown in UTC.Field Descriptions

Begin Date: Date incident began. Time in the field is UTC not local, so a separate column is created for accurate time information. Time: Begin date time field. CCN: A concatenation of the 4 digit year in which the incident was created, followed by a dash and then a 6 digit number of sequence for the agency. The MP at the beginning signifies a report taken by Minneapolis Police. This is used because Minneapolis shares the record management system with the University of Minnesota Police ControlNbr: A unique identifier for case. Offense: Code of criminal act reported. Description: Description of the criminal code of incident. EnteredDate: The timestamp of when the incident was created in the system. GBSID: The anonymized street centerline ID. LastChanged: Date the record was last altered in system. LastUpdateDate: Date the record was last moved to open data. Lat: The anonymized latitude of the incident. Long: The anonymized longitude of the incident. Neighborhood: The neighborhood of the incident. Note that occasionally due to the anonymization process, if a point is on the boundary of a neighborhood, it may fall into either neighborhood. OBJECTID: A unique identifier for open data portal. Precinct: The police precinct of the incident. Note that occasionally due to the anonymization process, if a point is on the boundary of a precinct, it may fall into either precinct. PublicAddress: Address of incident anonymized to the block. ReportedDate: Date incident is reported to police. UCRCode: Code that signifies the type of crime that was committed.

1 = MURDER 3 = RAPE 4 = ROBBERY 5 = ASSAULT 6 = BURGLARY 7 = LARCENY 8 = AUTO THEFT 10 = ARSON

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.

In 2023, an estimated 1,21,467 violent crimes occurred in the United States. This is a decrease from the year before, when 1,256,671 violent crimes were reported. Violent crime in the United States The Federal Bureau of Investigation reported that violent crime fell nationwide in the period from 1990 to 2023. Violent crime was at a height of 1.93 million crimes in 1992, but has since reached a low of 1.15 million violent crimes in 2014. When conducting crime reporting, the FBI’s Uniform Crime Reporting Program considered murder, nonnegligent manslaughter, forcible rape, robbery and aggravated assault to be violent crimes, because they are offenses which involve force or threat of violence. In 2023, there were 19,252 reported murder and nonnegligent manslaughter cases in the United States. California ranked first on a list of U.S. states by number of murders, followed by Texas, and Florida.The greatest number of murders were committed by murderers of unknown relationship to their victim. “Girlfriend” was the fourth most common relationship of victim to offender in 2023, with a reported 568 partners murdering their girlfriends that year, while the sixth most common was “wife.” In addition, seven people were murdered by their employees and 12 people were murdered by their employers. The most used murder weapon in 2023 was the handgun, which was used in 7,1 murders that year. According to the FBI, firearms (of all types) were used in more than half of the nation’s murders. The total number of firearms manufactured in the U.S. annually has reached over 13 million units.

The purpose of this study was to replicate an experiment in Minneapolis (MINNEAPOLIS INTERVENTION PROJECT, 1986-1987 [ICPSR 9808]) testing alternative police response to cases of spouse assault, using a larger number of subjects and a more complex research design. The study focused on how police response affected subsequent incidents of spouse assault. Police responses studied included arrest, issuing emergency protection orders, referring the suspect to counseling, separating the suspect and the victim, and restoring order only (no specific action). Data were obtained through initial incident reports, counseling information, and personal interviews. Follow-up interviews were conducted at three- and six-month periods, and recidivists were identified through police and court record checks. Variables from initial incident reports include number of charges, date, location, and disposition of charges, weapon(s) used, victim injuries, medical attention received, behavior towards police, victim and suspect comments, and demographic information such as race, sex, relationship to victim/offender, age, and past victim/offender history. Data collected from counseling forms provide information on demographic characteristics of the suspect, type of counseling, topics covered in counseling, suspect's level of participation, and therapist comments. Court records investigate victim and suspect criminal histories, including descriptions of charges and their disposition, conditions of pretrial release, and the victim's contact with pretrial services. Other variables included in follow-up checks focus on criminal and offense history of the suspect. The data collection includes separate data files for the original, second, and final versions of some of the forms that were used.

The rate of fatal police shootings in the United States shows large differences based on ethnicity. Among Black Americans, the rate of fatal police shootings between 2015 and December 2024 stood at 6.1 per million of the population per year, while for white Americans, the rate stood at 2.4 fatal police shootings per million of the population per year. Police brutality in the United States Police brutality is a major issue in the United States, but recently saw a spike in online awareness and protests following the murder of George Floyd, an African American who was killed by a Minneapolis police officer. Just a few months before, Breonna Taylor was fatally shot in her apartment when Louisville police officers forced entry into her apartment. Despite the repeated fatal police shootings across the country, police accountability has not been adequate according to many Americans. A majority of Black Americans thought that police officers were not held accountable for their misconduct, while less than half of White Americans thought the same. Political opinions Not only are there differences in opinion between ethnicities on police brutality, but there are also major differences between political parties. A majority of Democrats in the United States thought that police officers were not held accountable for their misconduct, while a majority of Republicans that they were held accountable. Despite opposing views on police accountability, both Democrats and Republicans agree that police should be required to be trained in nonviolent alternatives to deadly force.

China Government Expenditure: Public Security: Armed Police data was reported at 156,215.000 RMB mn in 2023. This records an increase from the previous number of 145,460.000 RMB mn for 2022. China Government Expenditure: Public Security: Armed Police data is updated yearly, averaging 108,202.000 RMB mn from Dec 1999 (Median) to 2023, with 25 observations. The data reached an all-time high of 205,571.000 RMB mn in 2018 and a record low of 15,844.000 RMB mn in 1999. China Government Expenditure: Public Security: Armed Police data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under China Premium Database’s Government and Public Finance – Table CN.FAS: Final Account: General Public Budget Revenue & Expenditure: National.

Mongolian Crime Data

This dataset consists of crime reports, their location, and the date of the crime from 2014-2017. From exploring the data it is clear that the 2014 and 2017 data is not complete, so analysis on these years should be done with that in mind. The years 2015 and 2016 appear complete.

The data was scrapped from the Mongolian Police Agency's Crimemap website at http://crimemap.police.gov.mn. As more data becomes available I will update the dataset.

Description of Columns

Here is a translation and description of each column from Mongolian to English: - Гэмт хэргийн төрөл: Crime category.
- Хэргийн дугаар: Case number
- Хэргийн огноо: Case date
- Хэргийн байршил: Crime location
- Хот/Аймаг: City/Province
- Дүүрэг: District (only in Ulaanbaatar, the capital city)
- Хороо/Сум: Subdistrict (in Ulaanbaatar), Outside of Ulaanbaatar the smallest administrative unit (soum)
- Шалгасан ЦХ: Police station where crime was reported

Other crime data

Mongolia's National Statistical Office records yearly crime data at http://1212.mn. Cross-referencing these two datasets could validate their completeness.

Inspiration

I hope to build a crime forecasting tool to show the power of this type of data. A different set of challenges exist in Mongolia for crime forecasting, as there is little racial diversity in Mongolia compared to the United States or Europe.

This study represents a modified replication of the Minneapolis Domestic Violence Experiment (SPECIFIC DETERRENT EFFECTS OF ARREST FOR DOMESTIC ASSAULT: MINNEAPOLIS, 1981-1982 [ICPSR 8250]). The Minneapolis study found arrest to be an effective deterrent against repeat domestic violence. The two key purposes of the current study were (1) to examine the possible differences in reactions to arrest, and (2) to compare the effects of short and long incarceration associated with arrest. Research protocol involved 35 patrol officers in four Milwaukee police districts screening domestic violence cases for eligibility, then calling police headquarters to request a randomly-assigned disposition. The three possible randomly assigned dispositions were (1) Code 1, which consisted of arrest and at least one night in jail, unless the suspect posted bond, (2) Code 2, which consisted of arrest and immediate release on recognizance from the booking area at police headquarters, or as soon as possible, and (3) Code 3, which consisted of a standard Miranda-style script warning read by police to both suspect and victim. A battered women's shelter hotline system provided the primary measurement of the frequency of violence by the same suspects both before and after each case leading to a randomized police action. Other forms of measurement included arrests of the suspect both before and after the offense, as well as offenses against the same victim. Initial victim interviews were attempted within one month after the first 900 incidents were compiled. A second victim interview was attempted six months after the incident for all 1,200 cases. Data collected for this study included detailed data on each of the 1,200 randomized events, less detailed data on an additional 854 cases found ineligible, "pipeline" data on the frequency of domestic violence in the four Milwaukee police districts, official measures of prior and subsequent domestic violence for both suspects and victims, interviews of arrested suspects for eligible and ineligible cases, criminal justice system dispositions of the randomized arrests, results of urinalysis tests of drug and alcohol use for some arrestees, and log attempts to obtain interviews from suspects and victims. Demographic variables include victim and suspect age, race, education, employment status, and marital status. Additional information obtained includes victim-offender relationships, alcohol and drug use during incident, substance of conflict, nature of victim injury and medical treatment as reported by police and victims, characteristics of suspects in the Code 1 and 2 arrest groups, victim and suspect reports of who called police, and victim and suspect versions of speed of police response.

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.

Thailand BB Forecast: Non Taxes Revenue: Actual: The National Police Office data was reported at 8,043.400 THB mn in 2019. This records a decrease from the previous number of 9,399.100 THB mn for 2018. Thailand BB Forecast: Non Taxes Revenue: Actual: The National Police Office data is updated yearly, averaging 6,245.700 THB mn from Sep 2011 (Median) to 2019, with 9 observations. The data reached an all-time high of 9,399.100 THB mn in 2018 and a record low of 276.100 THB mn in 2011. Thailand BB Forecast: Non Taxes Revenue: Actual: The National Police Office data remains active status in CEIC and is reported by Bureau of the Budget. The data is categorized under Global Database’s Thailand – Table TH.F016: Government Budget: Forecast: Bureau of the Budget.