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.

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.

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.

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 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.

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

The global crime analytics software market is expected to grow at CAGR of 8.2% for the forecast period 2023-2030.

Growing demand for effective crime prevention and reduction techniques due to rising crime rate is expected to drive the growth of the crime analytics software market
North America dominates the crime analytics software market

Key Dynamics of Crime Analytics Software Market.

Key Drivers of Crime Analytics Software Market.

Increasing Urban Crime Rates and Concerns for Public Safety: As urban populations expand and criminal activities become more sophisticated, law enforcement agencies face mounting pressure to take proactive measures. The use of crime analytics software facilitates real-time monitoring, predictive policing, and data-driven decision-making, all aimed at enhancing public safety and optimizing resource allocation.

Government and Law Enforcement Agency Adoption: Across various regions, governments are making significant investments in smart policing infrastructure. Crime analytics tools are being incorporated into national security and policing frameworks to identify patterns, anticipate threats, and enable quicker responses. Such investments are a major driver of market growth.

Advancements in AI, Big Data, and Geospatial Technologies: The advancement of artificial intelligence, machine learning, and GIS technologies significantly boosts the capabilities of crime analytics software. These innovations support real-time crime mapping, recognition of behavioral patterns, and the generation of actionable insights, which contribute to more effective crime prevention and resolution.

Key Restrains for Crime Analytics Software Market.

Concerns Regarding Data Privacy and Ethics: The utilization of personal data for predictive analytics raises critical issues related to surveillance, bias, and civil liberties. Any misuse or lack of transparency in data collection and analysis can result in legal challenges and public discontent.

High Costs of Implementation and Integration: The deployment of crime analytics systems necessitates substantial investment in hardware, software, training, and data infrastructure. For smaller municipalities or agencies with constrained budgets, the significant initial and ongoing expenses may hinder or restrict adoption.

Challenges of Inconsistent Data Sources and System Fragmentation: Crime data is frequently sourced from various entities—law enforcement, public safety, social media, etc.—which may not adhere to standardization or interoperability. This fragmentation can impede data accuracy and the overall effectiveness of crime analysis platforms.

Key Trends in Crime Analytics Software Market.

Increasing Adoption of Predictive Policing Models: Law enforcement agencies are progressively utilizing predictive analytics to identify potential crime hotspots and strategically deploy officers. These models analyze historical crime data, along with factors such as time, location, and environmental conditions, to predict incidents and mitigate crime rates.

Integration with Body Cameras and Surveillance Systems: Crime analytics systems are being combined with live video feeds, CCTV networks, and body-worn cameras. This integration facilitates real-time monitoring, evidence gathering, and automated identification of suspects, thereby improving overall situational awareness.

Expansion of Cloud-Based and Mobile Solutions: Cloud-based and mobile-compatible crime analytics applications provide remote access, enable collaboration across different jurisdictions, and offer real-time data updates. These solutions are becoming increasingly favored due to their scalability, cost-effectiveness, and enhanced operational flexibility for law enforcement agencies.

The COVID-19 impact on the Crime Analytics Software Market.

The COVID-19 pandemic has had a significant impact on the crime analytics software market, resulting in both challenges and opportunities for the industry. The most immediate impact of the pandemic was the widespread imposition of travel restrictions, lockdowns, and quarantines. Due to the lockdowns, social distancing measures, and changes in daily routines, the burglary and street-level crimes have noticed some reduction. Crime analytics software would have been crucial in identifying and analysing these shifts. Remote work became essential during the pandemic, including for law enforcement agencies....

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

Public Safety GIS Software Market Outlook

According to our latest research, the Public Safety GIS Software market size reached USD 2.86 billion in 2024, demonstrating robust adoption across emergency response and law enforcement sectors worldwide. The market is experiencing significant momentum, driven by the imperative need for real-time geospatial intelligence in public safety operations. With a projected compound annual growth rate (CAGR) of 11.2% from 2025 to 2033, the market is forecasted to reach USD 7.36 billion by 2033. This remarkable expansion is fueled by increasing investments in digital transformation of public safety infrastructure, rising incidences of natural disasters, and the growing complexity of urban environments necessitating advanced GIS solutions.

One of the primary growth factors propelling the Public Safety GIS Software market is the escalating frequency and severity of natural and man-made disasters worldwide. As cities become more densely populated and climate change intensifies, the demand for sophisticated geospatial analysis tools to support disaster preparedness and response has surged. Public safety agencies are leveraging GIS software to map hazard zones, optimize evacuation routes, and allocate resources effectively in real time. This technological evolution enables a more proactive and coordinated approach to emergency management, significantly reducing response times and enhancing situational awareness for first responders. The integration of GIS with IoT sensors, drones, and real-time data feeds further amplifies its value, providing a comprehensive operational picture that is critical for life-saving interventions.

Another significant driver is the increasing adoption of cloud-based deployment models, which are transforming the accessibility and scalability of GIS solutions for public safety. Cloud-based GIS platforms offer cost-effective, flexible, and secure options for agencies of all sizes, enabling seamless data sharing and collaboration across departments and jurisdictions. This democratization of geospatial intelligence is particularly beneficial for smaller municipalities and rural areas that previously lacked the resources for advanced on-premises systems. Enhanced interoperability with other public safety technologies, such as computer-aided dispatch (CAD) and records management systems (RMS), is further accelerating market growth. The ongoing digitalization of public safety processes, coupled with supportive government policies and funding initiatives, is expected to sustain the market’s upward trajectory over the forecast period.

The growing emphasis on crime analysis and predictive policing is also contributing to the expansion of the Public Safety GIS Software market. Law enforcement agencies are increasingly utilizing GIS-driven analytics to identify crime hotspots, forecast criminal activity, and allocate patrol resources more efficiently. The ability to visualize and analyze spatial patterns of crime in conjunction with demographic, socioeconomic, and environmental data enhances strategic decision-making and supports community-oriented policing initiatives. Furthermore, advancements in artificial intelligence and machine learning are enabling more sophisticated geospatial modeling, empowering agencies to anticipate threats and deploy preventive measures proactively. This shift toward data-driven policing is fostering greater trust and transparency between law enforcement and the communities they serve.

From a regional perspective, North America continues to lead the global Public Safety GIS Software market in terms of both adoption and innovation. The presence of technologically advanced public safety infrastructure, high levels of government investment, and a strong ecosystem of GIS vendors and solution providers have made the region a pioneer in integrating geospatial intelligence into emergency response workflows. Europe is also witnessing substantial growth, driven by stringent regulatory mandates and cross-border collaboration on disaster management. Meanwhile, Asia Pacific is emerging as a high-growth market, fueled by rapid urbanization, increasing vulnerability to natural disasters, and ambitious smart city initiatives. Latin America and the Middle East & Africa are gradually catching up, with growing recognition of the value of GIS in enhancing public safety outcomes.

Component Analysis

The Component segment of the Pu

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.

Spatial Crime Pattern Analysis Tools Market Outlook

According to our latest research, the Global Spatial Crime Pattern Analysis Tools market size was valued at $1.47 billion in 2024 and is projected to reach $4.23 billion by 2033, expanding at a robust CAGR of 12.8% during the forecast period of 2024–2033. One of the primary factors fueling this remarkable growth is the increasing reliance on geospatial intelligence and advanced analytics by law enforcement and urban planning agencies to enhance public safety, optimize resource allocation, and proactively address crime hotspots. As digital transformation accelerates across public and private sectors, spatial crime pattern analysis tools are becoming indispensable in modern security and planning frameworks, making them a critical investment for governments, private security firms, and research institutions worldwide.

Regional Outlook

North America currently commands the largest share of the global Spatial Crime Pattern Analysis Tools market, accounting for approximately 37% of the total market value in 2024. This dominance is attributed to the region’s mature technology infrastructure, high adoption of advanced analytics, and robust investments in public safety initiatives. The United States, in particular, has witnessed significant deployments of spatial crime analysis software across federal, state, and local law enforcement agencies. Supportive government policies, the presence of leading technology vendors, and a culture of innovation have further propelled market growth in North America. Additionally, collaborative efforts between public agencies and private tech firms have led to the development of cutting-edge solutions tailored specifically for crime prevention and urban management, further solidifying the region’s leadership in this sector.

The Asia Pacific region is anticipated to be the fastest-growing market for spatial crime pattern analysis tools, with a projected CAGR exceeding 15.2% from 2024 to 2033. Rapid urbanization, increasing investments in smart city initiatives, and growing concerns over public safety are major drivers in this region. Countries such as China, India, and Japan are witnessing unprecedented investments in digital infrastructure and surveillance technologies. Government-led projects aimed at integrating GIS and AI-driven crime analytics into urban management systems are gaining traction. Moreover, the region’s burgeoning population and complex urban landscapes necessitate innovative approaches to crime prevention, making spatial analysis tools a strategic priority for both policymakers and private sector stakeholders.

Emerging economies in Latin America, the Middle East, and Africa are also witnessing gradual adoption of spatial crime pattern analysis tools, albeit at a slower pace compared to developed regions. Challenges such as limited digital infrastructure, budget constraints, and varying regulatory frameworks have somewhat hindered widespread deployment. However, localized demand is growing, especially in major urban centers grappling with rising crime rates and the need for more efficient resource allocation. International aid programs, public-private partnerships, and capacity-building initiatives are helping to bridge the technology gap. As governments in these regions increasingly recognize the value of data-driven crime prevention, adoption rates are expected to climb, unlocking new growth opportunities for solution providers.

Report Scope

An interactive public crime mapping application providing DC residents and visitors easy-to-understand data visualizations of crime locations, types and trends across all eight wards. Crime Cards was created by the DC Metropolitan Police Department (MPD) and Office of the Chief Technology Officer (OCTO). Special thanks to the community members who participated in reviews with MPD Officers and IT staff, and those who joined us for the #SaferStrongerSmarterDC roundtable design review. All statistics presented in Crime Cards are based on preliminary DC Index crime data reported from 2009 to midnight of today’s date. They are compiled based on the date the offense was reported (Report Date) to MPD. The application displays two main crime categories: Violent Crime and Property Crime. Violent Crimes include homicide, sex abuse, assault with a dangerous weapon (ADW), and robbery. Violent crimes can be further searched by the weapon used. Property Crimes include burglary, motor vehicle theft, theft from vehicle, theft (other), and arson.CrimeCards collaboration between the Metropolitan Police Department (MPD), the Office of the Chief Technology Officer (OCTO), and community members who participated at the #SafterStrongerSmarterDC roundtable design review.

⚠️Due to the City's transition to a new Record Management System (RMS) , the Crime Incidents data, dashboards and maps on this site currently include records only through 11/18/25. Regular updates are paused to align and unify old and new data models. We aim to restore fully automated daily updates by the end of 2025.This transition does not impact Public Safety systems or operations. Data for the pause period (from 11/18/25 until integration completion) is still available upon request via the City’s public records portal. Thank you for your patience!This mapping application visualizes crime incidents across the City of Cleveland. The data includes details on the type, location, and time of each reported crime, enabling users to analyze crime patterns and trends. The data provided is the latest available information and is updated regularly as statistics change. For access to comprehensive reports, kindly submit a public record request by clicking here. This application uses the following dataset(s):Cleveland Crime Incidents Update FrequencyDaily around 8AM EST ContactsCity of Cleveland Division of Police InstructionsBy default, the map loads with the previous 90 days incidents.Use the filters on the left side of the page to filter the data by date, City of Cleveland Ward, Cleveland Division of Police District, and/or crime type.Use the “Reset Filters” buttons under each filter to reset each filter individually.Use the ”Query" button in the lower right to select points currently on the map by drawing shapes and defining specific locations.Use the “Map Layers” button in the lower right to turn on and off the crime incidents layer, the Cleveland Division of Police Districts, and/or the City of Cleveland Wards.Use the “Tables” to view the data tables for the incidents that are map or queried.Click a point on the map to pop up individual information about the point(s) that were selected.Data from the map can be exported to CSV, JSON, and GeoJSON files directly from the tables which are accessed through the “Tables” button in the lower right corner of the map.

This collection grew out of a prototype case tracking and crime mapping application that was developed for the United States Attorney's Office (USAO), Southern District of New York (SDNY). The purpose of creating the application was to move from the traditionally episodic way of handling cases to a comprehensive and strategic method of collecting case information and linking it to specific geographic locations, and collecting information either not handled at all or not handled with sufficient enough detail by SDNY's existing case management system. The result was an end-user application designed to be run largely by SDNY's nontechnical staff. It consisted of two components, a database to capture case tracking information and a mapping component to link case and geographic data. The case tracking data were contained in a Microsoft Access database and the client application contained all of the forms, queries, reports, macros, table links, and code necessary to enter, navigate through, and query the data. The mapping application was developed using Environmental Systems Research Institute's (ESRI) ArcView 3.0a GIS. This collection shows how the user-interface of the database and the mapping component were customized to allow the staff to perform spatial queries without having to be geographic information systems (GIS) experts. Part 1 of this collection contains the Visual Basic script used to customize the user-interface of the Microsoft Access database. Part 2 contains the Avenue script used to customize ArcView to link the data maintained in the server databases, to automate the office's most common queries, and to run simple analyses.

Global Public Safety Software Market is segmented by Application (Law enforcement_ Firefighting_ Emergency medical services_ Homeland security_ Public health monitoring), Type (Incident management software_ Emergency response software_ Crime mapping software_ Mass notification systems_ GIS-based tools), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Senior Crime Data based on Crime data for the City of Dallas for use in the web application below. Created in collaboration with the City of Dallas and the Senior Affairs Commission. Encompasses several variables (poverty, crime, marital status, etc.): Provide data for the Senior Demographics Applicationhttps://experience.arcgis.com/experience/d0f64d47090940f9aeeafce8cb1d1c4e. Crime Indices Pages:https://experience.arcgis.com/experience/d0f64d47090940f9aeeafce8cb1d1c4e/page/Crime-Indices/This data set is aggregated to the 2010 census tract level over a specific time period for the above application and may not be applicable for other analysis.

Incident Mapping and Geointelligence Market Outlook

According to our latest research, the global Incident Mapping and Geointelligence market size is valued at USD 13.2 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.6% projected through the forecast period. By 2033, the market is anticipated to reach USD 40.5 billion, driven by the increasing adoption of real-time geospatial analytics and the rising need for advanced incident response solutions across public and private sectors. The surge in demand for situational awareness, rapid emergency response, and data-driven decision-making are primary growth factors propelling the market forward.

The growth of the Incident Mapping and Geointelligence market is strongly influenced by the proliferation of smart city initiatives and the integration of geospatial intelligence in urban planning and management. Governments and municipalities worldwide are investing heavily in digital infrastructure to enhance public safety, streamline emergency response, and improve disaster management. The availability of high-resolution satellite imagery, advancements in remote sensing technologies, and the adoption of IoT-enabled sensors are further fueling the market. These innovations enable authorities to monitor incidents in real time, assess risks more effectively, and deploy resources with greater precision, thereby reducing response times and mitigating the impact of emergencies.

Another significant growth driver is the increasing prevalence of natural disasters, climate change-related incidents, and security threats, which have underscored the necessity for comprehensive incident mapping solutions. Organizations across sectors—ranging from law enforcement and defense to utilities and transportation—are leveraging geointelligence platforms to enhance situational awareness, coordinate multi-agency responses, and ensure public safety. The integration of artificial intelligence (AI) and machine learning (ML) in geospatial analytics has further augmented the predictive capabilities of these platforms, enabling proactive risk assessment and efficient resource allocation. As a result, the demand for scalable, interoperable, and user-friendly incident mapping solutions continues to rise.

The rapid digital transformation across industries, coupled with the growing emphasis on data-driven governance, has amplified the adoption of cloud-based geointelligence solutions. Cloud deployment offers unparalleled scalability, real-time collaboration, and cost efficiency, making it increasingly attractive for both large enterprises and small-to-medium organizations. Moreover, the convergence of big data analytics, mobile computing, and GIS technologies has broadened the application scope of incident mapping—from crime analysis and environmental monitoring to traffic management and critical infrastructure protection. This technological synergy is expected to sustain market momentum over the forecast period.

Regionally, North America leads the Incident Mapping and Geointelligence market owing to its advanced technological ecosystem, high public safety expenditure, and early adoption of geospatial analytics. However, Asia Pacific is emerging as a high-growth region, fueled by rapid urbanization, government investments in disaster management, and expansion of smart city projects. Europe and the Middle East & Africa are also witnessing significant traction, particularly in sectors such as defense, transportation, and environmental monitoring. Each region presents unique opportunities and challenges, shaped by regulatory frameworks, infrastructure maturity, and socio-political factors.

Component Analysis

The Component segment of the Incident Mapping and Geointelligence market is categorized into Software, Hardware, and Services, each playing a pivotal role in the overall ecosystem. Software solutions form the backbone of geointelligence platforms, providing end users with data visualization, spatial analyt

Current police beat boundaries in Chicago. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

The purpose of this research was to further understanding of why crime occurs where it does by exploring the spatial etiology of homicides that occurred in Washington, DC, during the 13-year period 1990-2002.

The researchers accessed records from the case management system of the Metropolitan Police, District of Columbia (MPDC) Homicide Division to collect data regarding offenders and victims associated with the homicide cases. Using geographic information systems (GIS) software, the researchers geocoded the addresses of the incident location, the victim's residence, and offender's residence for each homicide case. They then calculated both Euclidean distance and shortest path distance along the streets between each address per case. Upon applying the concept of triad as developed by Block et al. (2004) in order to create a unit of analysis for studying the convergence of victims and offenders in space, the researchers categorized the triads according to the geometry of locations associated with each case. (Dots represented homicides in which the victim and offender both lived in the residence where the homicide occurred; lines represented homicides that occurred in the home of either the victim or the offender; and triangles represented three non-coincident locations: the separate residences of the victim and offender, as well as the location of the homicide incident.) The researchers then classified each triad according to two separate mobility triangle classification schemes: Traditional Mobility, based on shared or disparate social areas, and Distance Mobility, based on relative distance categories between locations. Finally, the researchers classified each triad by the neighborhood associated with the location of the homicide incident, the location of the victim's residence, and the location of the offender's residence.

A total of 3 statistical datasets and 7 geographic information systems (GIS) shapefiles resulted from this study. Note: All datasets exclude open homicide cases. The statistical datasets consist of Offender Characteristics (Dataset 1) with 2,966 cases; Victim Characteristics (Dataset 2) with 2,311 cases; and Triads Data (Dataset 3) with 2,510 cases. The GIS shapefiles have been grouped into a zip file (Dataset 4). Included are point data for homicide locations, offender residences, triads, and victim residences; line data for streets in the District of Columbia, Maryland, and Virginia; and polygon data for neighborhood clusters in the District of Columbia.