Application of Big Data for National Security provides users with state-of-the-art concepts, methods, and technologies for Big Data analytics in the fight against terrorism and crime, including a wide range of case studies and application scenarios. This book combines expertise from an international team of experts in law enforcement, national security, and law, as well as computer sciences, criminology, linguistics, and psychology, creating a unique cross-disciplinary collection of knowledge and insights into this increasingly global issue. The strategic frameworks and critical factors presented in Application of Big Data for National Security consider technical, legal, ethical, and societal impacts, but also practical considerations of Big Data system design and deployment, illustrating how data and security concerns intersect. In identifying current and future technical and operational challenges it supports law enforcement and government agencies in their operational, tactical and strategic decisions when employing Big Data for national security - Contextualizes the Big Data concept and how it relates to national security and crime detection and prevention - Presents strategic approaches for the design, adoption, and deployment of Big Data technologies in preventing terrorism and reducing crime - Includes a series of case studies and scenarios to demonstrate the application of Big Data in a national security context - Indicates future directions for Big Data as an enabler of advanced crime prevention and detection

The events of September 11, 2001 changed perceptions, rearranged national priorities, and produced significant new government entities, including the U.S. Department of Homeland Security (DHS) created in 2003. While the principal mission of DHS is to lead efforts to secure the nation against those forces that wish to do harm, the department also has responsibilities in regard to preparation for and response to other hazards and disasters, such as floods, earthquakes, and other "natural" disasters. Whether in the context of preparedness, response or recovery from terrorism, illegal entry to the country, or natural disasters, DHS is committed to processes and methods that feature risk assessment as a critical component for making better-informed decisions. Review of the Department of Homeland Security's Approach to Risk Analysis explores how DHS is building its capabilities in risk analysis to inform decision making. The department uses risk analysis to inform decisions ranging from high-level policy choices to fine-scale protocols that guide the minute-by-minute actions of DHS employees. Although DHS is responsible for mitigating a range of threats, natural disasters, and pandemics, its risk analysis efforts are weighted heavily toward terrorism. In addition to assessing the capability of DHS risk analysis methods to support decision-making, the book evaluates the quality of the current approach to estimating risk and discusses how to improve current risk analysis procedures. Review of the Department of Homeland Security's Approach to Risk Analysis recommends that DHS continue to build its integrated risk management framework. It also suggests that the department improve the way models are developed and used and follow time-tested scientific practices, among other recommendations.

The growing potential of GIS for supporting policing and crime reduction is now being recognised by a broader community. GIS can be employed at different levels to support operational policing, tactical crime mapping, detection, and wider-ranging strategic analyses. With the use of GIS for crime mapping increasing, this book provides a definitive reference. GIS and Crime Mapping provides essential information and reference material to support readers in developing and implementing crime mapping. Relevant case studies help demonstrate the key principles, concepts and applications of crime mapping. This book combines the topics of theoretical principles, GIS, analytical techniques, data processing solutions, information sharing, problem-solving approaches, map design, and organisational structures for using crime mapping for policing and crime reduction. Delivered in an accessible style, topics are covered in a manner that underpins crime mapping use in the three broad areas of operations, tactics and strategy. Provides a complete start-to-finish coverage of crime mapping, including theory, scientific methodologies, analysis techniques and design principles. Includes a comprehensive presentation of crime mapping applications for operational, tactical and strategic purposes. Includes global case studies and examples to demonstrate good practice. Co-authored by Spencer Chainey, a leading researcher and consultant on GIS and crime mapping, and Jerry Ratcliffe, a renowned professor and former police officer. This book is essential reading for crime analysts and other professionals working in intelligence roles in law enforcement or crime reduction, at the local, regional and national government levels. It is also an excellent reference for undergraduate and Masters students taking courses in GIS, Geomatics, Crime Mapping, Crime Science, Criminal Justice and Criminology.

Are we safer from terrorism today and is our homeland security money well spent? This book offers answers and more.

With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. - Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research - Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations - Demonstrates methods to help sort through data to make better observations and allow you to make better predictions

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