Law Enforcement StructuresThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Geological Survey (USGS), displays police and prison landmarks in the U.S. Per the USGS, "Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations."Police Stations and Prison Correctional FacilitiesData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Law Enforcement) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 135 (USGS National Structures Dataset - USGS National Map Downloadable Data Collection)OGC API Features Link: (Law Enforcement Structures - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: The National MapFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Theme CommunityThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

The Tempe Police Department prides itself in its continued efforts to reduce harm within the community and is providing this dataset on hate crime incidents that occur in Tempe.The Tempe Police Department documents the type of bias that motivated a hate crime according to those categories established by the FBI. These include crimes motivated by biases based on race and ethnicity, religion, sexual orientation, disability, gender and gender identity.The Bias Type categories provided in the data come from the Bias Motivation Categories as defined in the Federal Bureau of Investigation (FBI) National Incident-Based Reporting System (NIBRS) manual, version 2020.1 dated 4/15/2021. The FBI NIBRS manual can be found at https://www.fbi.gov/file-repository/ucr/ucr-2019-1-nibrs-user-manua-093020.pdf with the Bias Motivation Categories found on pages 78-79.Although data is updated monthly, there is a delay by one month to allow for data validation and submission.Information about Tempe Police Department's collection and reporting process for possible hate crimes is included in https://storymaps.arcgis.com/stories/a963e97ca3494bfc8cd66d593eebabaf.Additional InformationSource: Data are from the Law Enforcement Records Management System (RMS)Contact: Angelique BeltranContact E-Mail: angelique_beltran@tempe.govData Source Type: TabularPreparation Method: Data from the Law Enforcement Records Management System (RMS) are entered by the Tempe Police Department into a GIS mapping system, which automatically publishes to open data.Publish Frequency: MonthlyPublish Method: New data entries are automatically published to open data. Data Dictionary

As a first step in understanding law enforcement agencies' use and knowledge of crime mapping, the Crime Mapping Research Center (CMRC) of the National Institute of Justice conducted a nationwide survey to determine which agencies were using geographic information systems (GIS), how they were using them, and, among agencies that were not using GIS, the reasons for that choice. Data were gathered using a survey instrument developed by National Institute of Justice staff, reviewed by practitioners and researchers with crime mapping knowledge, and approved by the Office of Management and Budget. The survey was mailed in March 1997 to a sample of law enforcement agencies in the United States. Surveys were accepted until May 1, 1998. Questions asked of all respondents included type of agency, population of community, number of personnel, types of crimes for which the agency kept incident-based records, types of crime analyses conducted, and whether the agency performed computerized crime mapping. Those agencies that reported using computerized crime mapping were asked which staff conducted the mapping, types of training their staff received in mapping, types of software and computers used, whether the agency used a global positioning system, types of data geocoded and mapped, types of spatial analyses performed and how often, use of hot spot analyses, how mapping results were used, how maps were maintained, whether the department kept an archive of geocoded data, what external data sources were used, whether the agency collaborated with other departments, what types of Department of Justice training would benefit the agency, what problems the agency had encountered in implementing mapping, and which external sources had funded crime mapping at the agency. Departments that reported no use of computerized crime mapping were asked why that was the case, whether they used electronic crime data, what types of software they used, and what types of Department of Justice training would benefit their agencies.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study contains data from a project by the New York City Police Department (NYPD) involving GIS data on environmental risk factors that correlate with criminal behavior. The general goal of this project was to test whether risk terrain modeling (RTM) could accurately and effectively predict different crime types occurring across New York City. The ultimate aim was to build an enforcement prediction model to test strategies for effectiveness before deploying resources. Three separate phases were completed to assess the effectiveness and applicability of RTM to New York City and the NYPD. A total of four boroughs (Manhattan, Brooklyn, the Bronx, Queens), four patrol boroughs (Brooklyn North, Brooklyn South, Queens North, Queens South), and four precincts (24th, 44th, 73rd, 110th) were examined in 6-month time periods between 2014 and 2015. Across each time period, a total of three different crime types were analyzed: street robberies, felony assaults, and shootings. The study includes three shapefiles relating to New York City Boundaries, four shapefiles relating to criminal offenses, and 40 shapefiles relating to risk factors.

Please click here to view the Data Dictionary, a description of the fields in this table.If you would like information that occurred more than a year ago, please submit a request through the Police Records Police Records Request.The police Calls for Service (CFS) report generates from the police department Computer Aided Dispatch (CAD) and includes one rolling year of data. Information automatically updates Sunday night. The most recent data available will begin one week prior from the updated date to allow for report approvals, ensuring the most accurate information available. Incidents included may not directly correlate to information found in other data sets nor should this data be considered for official Uniform Crime Reporting. For all official crime statistics please refer to the FBI and Arizona Department of Public Safety.Some information has been excluded and addresses shortened to the hundred block to protect privacy of victims and juveniles.

Metropolitan Police Department (MPD) Police Districts. The dataset contains polygons representing of MPD Districts, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Police jurisdictions were initially created selecting street arcs from the planimetric street centerlines and street polygons, water polygons, real property boundaries and District of Columbia boundaries.2019 Boundary Changes:Periodically, MPD conducts a comprehensive assessment of our patrol boundaries to ensure optimal operations. This effort considers current workload, anticipated population growth, development, and community needs. The overarching goals for the 2019 realignment effort included: optimal availability of police resources, officer safety and wellness, and efficient delivery of police services. These changes took effect on 01/10/2019.On 03/27/2019, this boundary was modified to adjust dispatching of North Capitol Street’s northwest access roads to be more operationally efficient.

The dataset contains polygons representing of MPD Police Sectors, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. In 2017 the Metropolitan Police Department formed an additional operational geographic layer called Sector. The Sector model brings additional management accountability to districts and allows for faster dispatch, lower response times, and improved service to the community. Sectors are made up of multiple Police Service Areas (PSAs) and are headed by a Captain. Please note that PSA is still an active operational model used by MPD; Sector is an additional layer between the PSA and District levels.2019 Boundary Changes:Periodically, MPD conducts a comprehensive assessment of our patrol boundaries to ensure optimal operations. This effort considers current workload, anticipated population growth, economic development, and community needs. The overarching goals for the 2019 realignment effort included: optimal availability of police resources, officer safety and wellness, and efficient delivery of police services. These changes took effect on 01/10/2019.On 03/27/2019, this boundary was modified to adjust dispatching of North Capitol Street’s northwest access roads to be more operationally efficient.

This dataset contains point locations for all publicly identified sites and office locations including headquarters, station, field office and investigative unit locations. This dataset was created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO), MPD and participating D.C. government agencies. Facilities and offices were obtained from MPD's Office of Corporate Communications, through interviews with MPD's Criminal Intelligence, and Tactical Crime Analysis Unit and through site surveys conducted by DC GIS staff.

Alaska crime data from 2000 to present from the FBI Uniform Crime Reporting (UCR) program. Information includes data on both violent and property crime.The UCR Program's primary objective is to generate reliable information for use in law enforcement administration, operation, and management; over the years, however, the data have become one of the country’s leading social indicators. The program has been the starting place for law enforcement executives, students of criminal justice, researchers, members of the media, and the public at large seeking information on crime in the nation. The program was conceived in 1929 by the International Association of Chiefs of Police to meet the need for reliable uniform crime statistics for the nation. In 1930, the FBI was tasked with collecting, publishing, and archiving those statistics.Source: US Federal Bureau of Investigation (FBI)This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: FBI UCR Program.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This data collection represents an experimental micro-level geospatial crime prevention strategy that attempted to interrupt the near repeat (NR) pattern in residential burglary by creating a NR space-time high risk zone around residential burglaries as they occurred and then using uniformed volunteers to notify residents of their increased risk and provide burglary prevention tips. The research used a randomized controlled trial to test whether high risk zones that received the notification had fewer subsequent burglaries than those that did not. In addition, two surveys were administered to gauge the impact of the program, one of residents of the treatment areas and one of treatment providers. The collection contains 6 Stata datasets: BCo_FinalData_20180118_Archiving.dta(n = 484, 8 variables)Red_FinalData_20180117_Archiving.dta (n = 268, 8 variables)BCo_FinalDatasetOtherCrime_ForArchiving_v2.dta(n = 484, 8 variables)Redlands_FinalDataSecondary_ForArchiving_v2.dta (n = 266, 8 variables)ResidentSurvey_AllResponses_V1.4_ArchiveCleaned.dta (n = 457, 42 variables)VolunteerSurvey_V1.2_ArchiveCleaned.dta (n = 38, 16 variables) The collection also includes 5 sets of geographic information system (GIS) data: BaltimoreCounty_Bnd.zipBC_NR_HRZs.zipBurglaryAreaMinus800_NoApts.zipRedlands_CityBnd.zipRedlandsNR_HRZs.shp.zip

Please click here to view the Data Dictionary, a description of the fields in this table.The police incident report generates from the police department Record Management System (RMS) and includes one rolling year of data. Information automatically updates Sunday night. The most recent data available will begin one week prior from the updated date to allow for report approvals, ensuring the most accurate information available. Incidents included may not directly correlate to information found in other data sets nor should this data be considered for official Uniform Crime Reporting. For all official crime statistics please refer to the FBI and Arizona Department of Public Safety.Some information has been excluded and addresses shortened to the hundred block to protect privacy of victims and juveniles.

The report covers Law Enforcement Software Companies and it is segmented by Solutions (Records Management Systems, Computer Aided Dispatch Systems, GIS/Mapping, Emergency Response, Jail Management, Evidence Management, Video Analytics), Deployment (Cloud, On-Premise), and Geography.

Coronavirus (COVID-19): Resources for Law Enforcement (National Police Foundation)._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This dataset has been published by the Virginia Beach Police Department and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

Police Department locations in Hennepin County. Police Department locations were captured for Emergency Mangement.
* Includes main police station but does not include substations. * Phone numbers are non-emergency contact numbers. * WebUrl is for main city website.

Link to Attribute Table Information: http://gis.hennepin.us/OpenData/Metadata/Police%20Stations.pdf

Use Limitations: This data (i) is furnished "AS IS" with no representation as to completeness or accuracy; (ii) is furnished with no warranty of any kind; and (iii) is not suitable for legal, engineering or surveying purposes. Hennepin County shall not be liable for any damage, injury or loss resulting from this data.

© This dataset is maintained by the Hennepin County Emergency Management Office. This layer is a component of Datasets for Hennepin County AGOL and Hennepin County Open Data.

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models. The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months. A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study. The statistical datasets consist of Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases. The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

Seattle Police Department Micro Community Policing Plan InformationThis layer was published in the WGS 1984 Web Mercator Auxiliary Sphere coordinate system.

CrimeMapTutorial is a step-by-step tutorial for learning

crime mapping using ArcView GIS or MapInfo Professional GIS. It was

designed to give users a thorough introduction to most of the

knowledge and skills needed to produce daily maps and spatial data

queries that uniformed officers and detectives find valuable for crime

prevention and enforcement. The tutorials can be used either for

self-learning or in a laboratory setting. The geographic information

system (GIS) and police data were supplied by the Rochester, New York,

Police Department. For each mapping software package, there are three

PDF tutorial workbooks and one WinZip archive containing sample data

and maps. Workbook 1 was designed for GIS users who want to learn how

to use a crime-mapping GIS and how to generate maps and data queries.

Workbook 2 was created to assist data preparers in processing police

data for use in a GIS. This includes address-matching of police

incidents to place them on pin maps and aggregating crime counts by

areas (like car beats) to produce area or choropleth maps. Workbook 3

was designed for map makers who want to learn how to construct useful

crime maps, given police data that have already been address-matched

and preprocessed by data preparers. It is estimated that the three

tutorials take approximately six hours to complete in total, including

exercises.