The Crime Mapping application is an interactive map that allows users to query and view different types of crimes nation-wide. The application also provides the ability to create reports, charts, and print selected information.

Retirement Notice: This item is in mature support as of June 2023 and will be retired in December 2025. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This map shows the total crime index in the U.S. in 2022 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The layer uses 2020 Census boundaries. The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indices Permitted use of this data is covered in the DATA section of the EsriMaster Agreement (E204CW) and these supplemental terms.

The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

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

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.

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.

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.

This map shows the incidence of seven major felonies -- burglary, felony assault, grand larceny, grand larceny of a motor vehicle, murder, rape, and robbery -- in New York City over the past year. Data can be mapped in aggregate at the precinct level, as a heat map showing concentration of crimes, or as individual incident points.

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

Real-Time Crime Mapping Market Outlook

According to our latest research, the global Real-Time Crime Mapping market size reached USD 8.1 billion in 2024, with a robust growth driven by increasing adoption of advanced analytics and digital mapping technologies. The market is expected to expand at a CAGR of 13.4% from 2025 to 2033, reaching a projected value of USD 25.3 billion by 2033. This growth is propelled by the rising need for efficient crime prevention, rapid incident response, and the integration of AI-driven solutions in public safety infrastructure worldwide.

One of the primary growth factors for the Real-Time Crime Mapping market is the increasing demand for proactive public safety measures. Law enforcement agencies across the globe are prioritizing digital transformation to improve their crime response times and predictive policing capabilities. The integration of real-time data analytics with geographic information systems (GIS) allows agencies to visualize crime hotspots, deploy resources more efficiently, and make data-driven decisions. Furthermore, the surge in urbanization and the complexity of modern cities have necessitated more sophisticated surveillance and incident management systems, driving the adoption of real-time crime mapping solutions.

Another significant driver is the technological advancements in artificial intelligence, machine learning, and big data analytics. These technologies have revolutionized the way crime data is captured, analyzed, and presented. Real-time crime mapping platforms now offer predictive analytics, automated alerts, and seamless integration with other law enforcement databases. This has enabled agencies to not only map current incidents but also anticipate potential criminal activities and prevent them before they occur. The continuous innovation in cloud-based solutions and mobile applications further enhances accessibility and usability, making these tools indispensable for both government and commercial sectors.

The growing emphasis on community engagement and transparency has also contributed to the expansion of the Real-Time Crime Mapping market. Public-facing crime mapping portals empower citizens with timely information about incidents in their neighborhoods, fostering greater trust in law enforcement agencies. Additionally, the rise of smart city initiatives and increased investments in public safety infrastructure by governments worldwide have created a fertile environment for market growth. These initiatives often include the deployment of integrated surveillance, emergency response, and data sharing platforms, all of which rely heavily on real-time crime mapping technologies.

From a regional perspective, North America continues to dominate the Real-Time Crime Mapping market, accounting for the largest share in 2024. This is attributed to the presence of advanced law enforcement infrastructure, high adoption rates of digital technologies, and substantial government funding for public safety projects. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, increasing crime rates, and growing investments in smart city and surveillance projects. Europe also remains a significant market, with countries focusing on modernizing their public safety systems and enhancing cross-border crime data sharing. Latin America and the Middle East & Africa are gradually catching up, supported by rising awareness and international collaborations in crime prevention.

Component Analysis

The component segment of the Real-Time Crime Mapping market is categorized into software, hardware, and services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of real-time crime mapping systems, encompassing GIS platforms, analytics engines, and user interfaces. These solutions enable the collection, processing, and visualization of crime data in real time, allowing law enforcement agencies to generate actionable insights.

The Sheriff's Office provides an online mapping and analysis service that combines the value of law enforcement data with the ease of use of Google-based mapping and an analytics module so that members of the public can view police data in a high-impact map or summary descriptive format.

The online mapping tool allows residents to view information about crimes relevant to their community.

View daily crime reports and significant incident reports.

Abstract (en): This research project was designed to demonstrate the contributions that Geographic Information Systems (GIS) and spatial analysis procedures can make to the study of crime patterns in a largely nonmetropolitan region of the United States. The project examined the extent to which the relationship between various structural factors and crime varied across metropolitan and nonmetropolitan locations in Appalachia over time. To investigate the spatial patterns of crime, a georeferenced dataset was compiled at the county level for each of the 399 counties comprising the Appalachian region. The data came from numerous secondary data sources, including the Federal Bureau of Investigation's Uniform Crime Reports, the Decennial Census of the United States, the Department of Agriculture, and the Appalachian Regional Commission. Data were gathered on the demographic distribution, change, and composition of each county, as well as other socioeconomic indicators. The dependent variables were index crime rates derived from the Uniform Crime Reports, with separate variables for violent and property crimes. These data were integrated into a GIS database in order to enhance the research with respect to: (1) data integration and visualization, (2) exploratory spatial analysis, and (3) confirmatory spatial analysis and statistical modeling. Part 1 contains variables for Appalachian subregions, Beale county codes, distress codes, number of families and households, population size, racial and age composition of population, dependency ratio, population growth, number of births and deaths, net migration, education, household composition, median family income, male and female employment status, and mobility. Part 2 variables include county identifiers plus numbers of total index crimes, violent index crimes, property index crimes, homicides, rapes, robberies, assaults, burglaries, larcenies, and motor vehicle thefts annually from 1977 to 1996. The spatial dynamics of crime in nonmetropolitan locations can be understood as a product of social, economic, and demographic influences that are often unique to those areas. Thus there is a need for research on nonmetropolitan crime that takes location and geographic context seriously. This research project was designed to demonstrate the contributions that Geographic Information Systems (GIS) and spatial analysis procedures can make to the study of crime patterns in a largely nonmetropolitan region of the United States. The project examined the extent to which the relationship between various structural factors and crime varied across metropolitan and nonmetropolitan locations in Appalachia over time. GIS and crime mapping technologies enabled the researcher to look more rigorously at the spatial patterns and ecological contexts of crime. To investigate the spatial patterns of crime for this project, a georeferenced dataset was compiled at the county level for each of the 399 counties comprising the Appalachian region. The data came from numerous secondary data sources, including the Federal Bureau of Investigation's Uniform Crime Reports, the Decennial Census of the United States, the Department of Agriculture, and the Appalachian Regional Commission. Data were gathered on the demographic distribution, change, and composition of each county, as well as other socioeconomic indicators. The dependent variables were index crime rates derived from the Uniform Crime Reports, with separate variables for violent and property crimes. These data were integrated into a GIS database in order to enhance the research with respect to: (1) data integration and visualization, (2) exploratory spatial analysis, and (3) confirmatory spatial analysis and statistical modeling. In order to portray the contextual diversity of crime in Appalachia, three different county classifications, each based on different criteria, were employed: (1) Appalachian subregions, consisting of North, Central, and South Appalachia, (2) Beale county codes based on metro-nonmetro designations, population size, and adjacency to metropolitan counties, and (3) distressed county codes based on measures of poverty, unemployment, and per capita income. Part 1 contains variables for Appalachian subregions, Beale county codes, distress codes, number of families and households, population size, racial and age composition of population, dependency ratio, population growth, number of births and deaths, net migration, education, household ...

Crime Mapping Hotspot Prediction Market Outlook

According to our latest research, the global Crime Mapping Hotspot Prediction market size reached USD 4.2 billion in 2024, with a robust CAGR of 13.7% projected through the forecast period. By 2033, the market is forecasted to attain a valuation of approximately USD 13.1 billion, driven by the increasing integration of advanced analytics and artificial intelligence in law enforcement and public safety initiatives. The primary growth factor remains the escalating need for proactive crime prevention and the adoption of predictive policing technologies by government agencies and private security organizations worldwide.

The growth trajectory of the Crime Mapping Hotspot Prediction market is largely attributed to the rapid digital transformation in the public safety sector. Law enforcement agencies globally are leveraging data-driven solutions to anticipate, identify, and respond to crime hotspots with greater efficacy. The proliferation of IoT devices, surveillance cameras, and sensor networks has resulted in a massive influx of real-time data, which, when analyzed using sophisticated machine learning and AI algorithms, enables the identification of trends and patterns that were previously undetectable. This shift towards predictive analytics is not only enhancing operational efficiency but also reducing crime rates in urban and suburban areas. The demand for such solutions is further fueled by increasing urbanization, which brings about complex security challenges that traditional policing methods struggle to address effectively.

Another significant driver for the market is the growing emphasis on community policing and public safety awareness. Municipalities and city planners are increasingly integrating Crime Mapping Hotspot Prediction tools into their urban management strategies to ensure safer environments for residents and businesses. These solutions help in resource optimization, allowing authorities to allocate patrols and law enforcement personnel more strategically. Furthermore, the integration of Geographic Information Systems (GIS) with advanced analytics platforms is enabling the visualization of crime data over time and space, supporting data-driven decision-making at all levels of governance. The market is also benefitting from rising investments in smart city projects, where public safety remains a cornerstone of sustainable urban development.

The rapid advancements in cloud computing and the growing acceptance of cloud-based deployment models are also contributing to market expansion. Cloud solutions offer scalability, cost-effectiveness, and accessibility, enabling even smaller municipalities and private security organizations to implement sophisticated crime mapping technologies without significant upfront investments in IT infrastructure. Additionally, the increasing collaboration between technology vendors, law enforcement agencies, and academic institutions is fostering innovation, leading to the development of more accurate and user-friendly prediction models. The convergence of AI, big data, and GIS technologies is expected to further revolutionize the market, making predictive policing an indispensable tool for modern law enforcement.

Regionally, North America continues to dominate the Crime Mapping Hotspot Prediction market, accounting for the largest share in 2024, primarily due to the early adoption of advanced policing technologies and substantial government funding for public safety initiatives. Europe follows closely, with significant investments in smart city infrastructure and cross-border security collaborations. The Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, rising crime rates, and increasing government focus on modernizing law enforcement agencies. Latin America and the Middle East & Africa are also emerging as promising markets, with growing awareness and gradual adoption of predictive crime mapping solutions, although challenges such as limited IT infrastructure and budget constraints persist in these regions.

Component Analysis

The Crime Mapping Hotspot Prediction market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment holds the largest share, as it encompasses the core analytical platforms, visualization tools, and dashboard interfaces that empower users to derive actionable insights from crime data. These s

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 City of Ferndale uses the service CrimeMapping.com to provide near-live mapping of local crimes, sorted by category. Our goal in providing this information is to reduce crime through a better-informed citizenry. Crime reports older than 180 days can be accessed in this data set. For near-live crime data, go to crimemapping.com. this is a subset of this historic data that has been geocoded to allow for easy analysis and mapping in a different data set. It contains all easily geocoded addresses. A complete CSV file covering all crime reports from 5/2011 to 5/2017 is also available.

The RMS Crime Incidents dataset consists of crime reports from the Detroit Police Department Records Management System (RMS). This data reflects criminal offenses reported in the City of Detroit that DPD was involved in from December 2016 to present. Note that records are included in the dataset based on when an incident is reported which could result in an occurrence date before December 2016. Incident data is typically entered into mobile devices by the officer in the field when responding to an incident. Incidents that occurred in Detroit but in a location that is under the jurisdiction of the Michigan State Police (MSP) or Wayne State University Police Department (WSUPD), such as on an expressway, Belle Isle, or around Wayne State University, are included only if the incident is handled by DPD. Such records are reviewed in a monthly audit to ensure that the incidents are counted by one and only one agency (MSP or DPD). This data is updated daily. For each crime incident, one or more offense charges are recorded, and each row in the dataset corresponds with one of these charges. An example could be a domestic assault where property was also vandalized. Offense charges that occurred at the same crime incident share a common incident number. For each offense charge record (rows)details include when and where the incident occurred, the nature of the offense, DPD precinct or detail, and the case investigation status. Locations of incidents associated with each call are reported based on the nearest intersection to protect the privacy of individuals.RMS Crime Incident data complies with Michigan Incident Crime Reporting (MICR) standards. More information about MICR standards is available via the MICR Website. The Manual and Arrest Charge Code Card may be especially helpful. There may be small differences between RMS Crime Incident data shared here and data shared through MICR given data presented here is updated here more frequently which results in a difference in a cadence of status updates. Additionally, this dataset includes crime incidents that following an investigation are coded with a case status of ‘Unfounded’. In most cases, this means that the incident occurred outside the jurisdiction of DPD or otherwise was reported in error. The State of Michigan, through the MICR program, reports data to the National Incident-Based Reporting System (NIBRS).Yearly Datasets for RMS Crime Incidents have been added to the ODP. This is to improve the user's experience in handling the large file size of the records in the comprehensive dataset. You may download each year separately, which significantly reduces the size and records for each file. In addition to the past years, we have also included a year-to-date dataset. This captures all RMS Crime Incidents from January 1, 2025, to present.Should you have questions about this dataset, you may contact the Commanding Officer of the Detroit Police Department's Crime Data Analytics at 313-596-2250 or CrimeIntelligenceBureau@detroitmi.gov.

Crime Mapping Hotspot Prediction Market Outlook

According to our latest research, the global Crime Mapping Hotspot Prediction market size reached USD 2.9 billion in 2024, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 18.7% from 2025 to 2033, ultimately reaching an estimated value of USD 13.1 billion by the end of the forecast period. This remarkable growth is primarily driven by the increasing adoption of advanced analytics and artificial intelligence by law enforcement agencies and urban planners worldwide, aiming to enhance public safety and optimize resource allocation.

The surge in demand for crime mapping hotspot prediction solutions is fundamentally linked to the exponential rise in data generation from surveillance systems, social media, and IoT-enabled devices. Law enforcement agencies are increasingly leveraging these technologies to proactively identify crime-prone areas, anticipate criminal activities, and allocate resources more efficiently. The integration of machine learning and artificial intelligence with geographic information systems (GIS) has significantly improved the accuracy and timeliness of crime predictions, enabling authorities to take preemptive measures and reduce crime rates. Furthermore, the proliferation of smart city initiatives across both developed and emerging economies has accelerated the deployment of these systems, as municipalities seek to enhance urban security and foster safer communities.

Another significant growth factor is the rising emphasis on data-driven decision-making in public safety management. Governments and private security firms are recognizing the value of predictive analytics in not only reducing crime but also optimizing operational costs and improving response times. The evolution of cloud computing and scalable software solutions has made advanced crime mapping tools accessible to a broader range of end-users, including smaller municipalities and private organizations. Additionally, increased funding for law enforcement modernization and digital transformation projects, especially in North America and Europe, is fueling investments in next-generation crime prediction platforms. These trends are further complemented by growing public awareness and demand for safer urban environments, pushing stakeholders to adopt innovative solutions for crime prevention.

Moreover, the ongoing advancements in artificial intelligence and machine learning algorithms are enhancing the capabilities of crime mapping hotspot prediction systems. These technologies enable the analysis of vast and complex datasets, uncovering hidden patterns and correlations that traditional methods might overlook. As a result, predictive policing strategies are becoming more effective, leading to higher arrest rates, reduced crime incidents, and improved community trust in law enforcement. The integration of real-time data feeds and mobile applications is also facilitating faster communication and collaboration among agencies, further boosting the market’s growth prospects.

Regionally, North America continues to dominate the crime mapping hotspot prediction market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, has witnessed substantial investments in smart policing and public safety analytics, driven by federal and state-level initiatives. Meanwhile, the Asia Pacific region is expected to experience the fastest CAGR during the forecast period, propelled by rapid urbanization, increasing crime rates, and government focus on smart city development. Latin America and the Middle East & Africa are also emerging as promising markets, as local authorities seek to tackle rising urban crime through technological innovation and international collaboration.

Component Analysis

The component segment of the crime mapping hotspot prediction market is categorized into software, hardware, and services. Software solutions currently ho

Web map. Map for St. Louis County and Municipal Crime app.

Web-Map & App Specifications: Application Name: Crime Mapping - Public ViewerApplication URL: https://arcgis.com/apps/webappviewer/index.html?id=cd5b990f2132430bb2bda1da366f175cWeb-Map Name: Crime Mapping Public Viewer -Web-Map Web Map ID#: id=c5088710b69e40bc89c335e4d4101bbeApp Template: Web-app Builder - Foldable ThemeAudience: External Use - Public ViewersFound on halifax.ca - https://www.halifax.ca/fire-police/police/crime-mappingFind ART, map symbols, logos and splash screen write up at this location. R:\ICT\ICT BIDS\Mapping Services\ArcGIS_Online\Published - Interactive Maps\Crime Mapping

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) for all complete quarters so far this year (2017). For additional details, please see the attached data dictionary in the ‘About’ section.