This dataset contains county-level totals for the years 2002-2014 for eight types of crime: murder, rape, robbery, aggravated assault, burglary, larceny, motor vehicle theft, and arson. These crimes are classed as Part I criminal offenses by the United States Federal Bureau of Investigations (FBI) in their Uniform Crime Reporting (UCR) program. Each record in the dataset represents the total of each type of criminal offense reported in (or, in the case of missing data, attributed to) the county in a given year.A curated version of this data is available through ICPSR at https://www.icpsr.umich.edu/web/ICPSR/studies/38649/versions/V1

Serious violent crimes consist of Part 1 offenses as defined by the U.S. Department of Justice’s Uniform Reporting Statistics. These include murders, nonnegligent homicides, rapes (legacy and revised), robberies, and aggravated assaults. LAPD data were used for City of Los Angeles, LASD data were used for unincorporated areas and cities that contract with LASD for law enforcement services, and CA Attorney General data were used for all other cities with local police departments. This indicator is based on location of residence. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Neighborhood violence and crime can have a harmful impact on all members of a community. Living in communities with high rates of violence and crime not only exposes residents to a greater personal risk of injury or death, but it can also render individuals more susceptible to many adverse health outcomes. People who are regularly exposed to violence and crime are more likely to suffer from chronic stress, depression, anxiety, and other mental health conditions. They are also less likely to be able to use their parks and neighborhoods for recreation and physical activity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The primary purpose of the second wave of the National Neighborhood Crime Study (NNCS2) was to develop a panel dataset of serious reported crimes in urban neighborhoods circa two time points - 2000 and 2010. These data offer the opportunity to assess the sources and consequences of neighborhood crime change for "communities" of different ethno-racial and economic compositions across the United States. The study also sought to examine the role of a neighborhood's broader ecology on crime levels and crime change by integrating indicators of city and/or metropolitan conditions. The NNCS2 includes two datasets. The first dataset, the NNCS2-Panel file (NNCS2-P), contains information on the Federal Bureau of Investigation's (FBI) Part 1 Index crimes (except arson), socioeconomic and demographic characteristics, and a variety of other neighborhood and city level controls for circa 2000 and 2010 for tracts in 81 of the 91 cities in the NNCS, wave 1. The second dataset, the NNCS2-Cross-Sectional file (NNCS2-CS), allows for examination of the local and contextual sources of neighborhood crime inequality circa 2010. The NNCS2-CS incorporates parallel data for census tracts and cities as in the Panel file, but includes a few additional cities for which panel data could not be compiled, as well information on the metropolitan areas within which cities are located.

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.

This data collection contains responses to victimization surveys that were administered as part of both the planning and evaluation stages of the Hartford Project, a crime opportunity reduction program implemented in a residential neighborhood in Hartford, Connecticut, in 1976. The Hartford Project was an experiment in how to reduce residential burglary and street robbery/purse snatching and the fear of those crimes. Funded through the Hartford Institute of Criminal and Social Justice, the project began in 1973. It was based on a new "environmental" approach to crime prevention: a comprehensive and integrative view addressing not only the relationship among citizens, police, and offenders, but also the effect of the physical environment on their attitudes and behavior. The surveys were administered by the Center for Survey Research at the University of Massachusetts at Boston. The Center collected Hartford resident survey data in five different years: 1973, 1975, 1976, 1977, and 1979. The 1973 survey provided basic data for problem analysis and planning. These data were updated twice: in 1975 to gather baseline data for the time of program implementation, and in the spring of 1976 with a survey of households in one targeted neighborhood of Hartford to provide data for the time of implementation of physical changes there. Program evaluation surveys were carried out in the spring of 1977 and two years later in 1979. The procedures for each survey were essentially identical each year in order to ensure comparability across time. The one exception was the 1976 sample, which was not independent of the one taken in 1975. In each survey except 1979, respondents reported on experiences during the preceding 12-month period. In 1979 the time reference was the past two years. The survey questions were very similar from year to year, with 1973 being the most unique. All surveys focused on victimization, fear, and perceived risk of being victims of the target crimes. Other questions explored perceptions of and attitudes toward police, neighborhood problems, and neighbors. The surveys also included questions on household and respondent characteristics.

Data is no longer provided by the Calgary Police Service. To access latest data click here. This data is considered cumulative as late-reported incidents are often received well after an offence has occurred. Therefore, crime counts are subject to change as they are updated. Crime count is based on the most serious violation (MSV) per incident. Violence: These figures include all violent crime offences as defined by the Centre for Canadian Justice Statistics Universal Crime Reporting (UCR) rules. Domestic violence is excluded. Break and Enter: Residential B&E includes both House and ‘Other’ structure break and enters due to the predominantly residential nature of this type of break in (e.g. detached garages, sheds). B&Es incidents include attempts.

This study explores the relationship between crime and neighborhood deterioration in eight neighborhoods in Chicago. The neighborhoods were selected on the basis of slowly or rapidly appreciating real estate values, stable or changing racial composition, and high or low crime rates. These data provide the results of a telephone survey administered to approximately 400 heads of households in each study neighborhood, a total of 3,310 completed interviews. The survey was designed to measure victimization experience, fear and perceptions of crime, protective measures taken, attitudes toward neighborhood quality and resources, attitudes toward the neighborhood as an investment, and density of community involvement. Each record includes appearance ratings for the block of the respondent's residence and aggregate figures on personal and property victimization for that city block. The aggregate appearance ratings were compiled from windshield surveys taken by trained personnel of the National Opinion Research Center. The criminal victimization figures came from Chicago City Police files.

Abstract (en): This dataset contains county-level totals for the years 2002-2014 for eight types of crime: murder, rape, robbery, aggravated assault, burglary, larceny, motor vehicle theft, and arson. These crimes are classed as Part I criminal offenses by the United States Federal Bureau of Investigations (FBI) in their Uniform Crime Reporting (UCR) program. Each record in the dataset represents the total of each type of criminal offense reported in (or, in the case of missing data, attributed to) the county in a given year. All counties in the United States, excluding US island territories.Smallest Geographic Unit: county

Crime is a significant social, economic, and legal issue. This paper presents an open-access spatiotemporal repository of street and neighborhood crime data, comprising approximately one million records of crimes in China, with specific geographic coordinates (latitude and longitude) and timestamps for each incident. The dataset is based on publicly available law court judgment documents. Artificial intelligence (AI) technologies are employed to extract crime events at the neighborhood or even building level from vast amounts of unstructured judicial text. This dataset enables more precise spatial analysis of crime incidents, offering valuable insights across interdisciplinary fields such as economics, sociology, and geography. It contributes significantly to the achievement of the United Nations Sustainable Development Goals (SDGs), particularly in fostering sustainable cities and communities, and plays a crucial role in advancing efforts to reduce all forms of violence and related mortality rates.citation: Zhang Y, Kwan M P, Fang L. An LLM driven dataset on the spatiotemporal distributions of street and neighborhood crime in China[J]. Scientific Data, 2025, 12(1): 467.关于该数据的问题可以访问我的个人网站获取我的联系方式：https://www.giserzhang.xyz/

The property crime rate measures the number of Part 1 crimes identified as being property-based (burglary and auto theft) that are reported to the Police Department. These incidents are per 1,000 residents in the neighborhood to allow for comparison across areas. Source: Baltimore Police DepartmentYears Available: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

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.

Dataset highlighting crimes reported to the Albany Police Department for the past year to date geocoded by Neighborhood. It is the same data as Patrol Zone.

For purposes of crime statistics, the FBI Uniform Crime Report Hierarchy Rule requires when more than one offense occurs in an incident the highest priority crime is selected as the primary offense.

The Part 1 crime rate captures incidents of homicide, rape, aggravated assault, robbery, burglary, larceny, and auto theft that are reported to the Police Department. These incidents are per 1,000 residents in the neighborhood to allow for comparison across areas. Source: Baltimore Police Department Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

The study examined the situational and contextual influences on violence in bars and apartment complexes in Cincinnati, Ohio. Interviews of managers and observations of sites were made for 199 bars (Part 1). Data were collected on 1,451 apartment complexes (Part 2). For apartment complexes owners were interviewed for 307 and observations were made at 994. Crime data were obtained from the Cincinnati Police Department records of calls for service and reported crimes.

The major objective of this study was to examine how physical characteristics of commercial centers and demographic characteristics of residential areas contribute to crime and how these characteristics affect reactions to crime in mixed commercial-residential settings. Information on physical characteristics includes type of business, store hours, arrangement of buildings, and defensive modifications in the area. Demographic variables cover racial composition, average household size and income, and percent change of occupancy. The crime data describe six types of crime: robbery, burglary, assault, rape, personal theft, and shoplifting.

All BPD data on Open Baltimore is preliminary data and subject to change. The information presented through Open Baltimore represents Part I victim based crime data. The data do not represent statistics submitted to the FBI's Uniform Crime Report (UCR); therefore any comparisons are strictly prohibited. For further clarification of UCR data, please visit http://www.fbi.gov/about-us/cjis/ucr/ucr. Please note that this data is preliminary and subject to change. Prior month data is likely to show changes when it is refreshed on a monthly basis. All data is geocoded to the approximate latitude/longitude location of the incident and excludes those records for which an address could not be geocoded. Any attempt to match the approximate location of the incident to an exact address is strictly prohibited.

This is the most current information as of the date of upload. This provides the user the ability to view the most current crime information within Kansas City, Missouri. The displayed information is the most current information from the data source as of the date of upload. The data source is dynamic and therefore constantly changing. Changes to the information may occur, as incident information is refined. While the Board of Police Commissioners of Kansas City, Missouri (Board) makes every effort to maintain and distribute accurate information, no warranties and/or representations of any kind are made regarding information, data or services provided. The Board is not responsible for misinterpretation of this information and makes no inference or judgment as to the relative safety to any particular area or neighborhood. In no event shall the Board be liable in any way to the users of this data. Users of this data shall hold the Board harmless in all matters and accounts arising from the use and/or accuracy of this data.

All BPD data on Open Baltimore is preliminary data and subject to change. The information presented through Open Baltimore represents Part I victim based crime data. The data do not represent statistics submitted to the FBI's Uniform Crime Report (UCR); therefore any comparisons are strictly prohibited. For further clarification of UCR data, please visit http://www.fbi.gov/about-us/cjis/ucr/ucr. Please note that this data is preliminary and subject to change. Prior month data is likely to show changes when it is refreshed on a monthly basis. All data is geocoded to the approximate latitude/longitude location of the incident and excludes those records for which an address could not be geocoded. Any attempt to match the approximate location of the incident to an exact address is strictly prohibited.

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.

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.