Predictive Policing In Germany Opportunities And Challenges Of Data Analytical Forecasting Technology In Order To Prevent Crime

Master's Thesis from the year 2019 in the subject Computer Science - Theory, grade: A*, Management Center Innsbruck, language: English, abstract: This Master Thesis introduces theoretical fundamentals of Predictive Policing tools used in German police institutions such as Hot-Spot techniques, Near-Repeat approaches, Risk-terrain Analysis and Concentric-Zone Model. In times of Big Data, police work has also changed and the usage of forecasting technologies in order to prevent crime does not only vary state-wide in definitions but also in its application. Therefore, objectives and appliances are described in general. Additionally, a chronological transformation is established in order to compare lineages in Germany with those in the USA. Since Predictive Policing polarises, the research question deals with potential opportunities and challenges police institutions and the society have to deal with, when it comes to leveraging data-analytical forecasting technologies in order to prevent crime. The motivation for writing the Master Thesis about the present topic stems from the fact that it is highly current and has not yet been thoroughly studied. Preventing crime and thus ensuring a safe environment is an important field of research in our society and should be guaranteed with problem-oriented policing. Since there are varying considerations and application measures of PP according to different country side frameworks, the Thesis provides an overview about technical functioning and practical appliance within Germany. Therefore, content provides on the one hand added value for lecturers and students in the field of Public Security Management and related studies or police officers in the upper grade of the civil service. On the other hand, it serves to educate citizens about how far the technologies have progressed in this area and to what extent this will influence the lives of citizens in the future. Many police departments worldwide test software-based forecasting technologies according to their relevance in practice. Forecasting systems work with data sets about already registered crime activities. Those datasets are then complemented with socio-spatial, calendar and meteorological data. Since the amount of collected and analyzed data increases day by day, the question arises as to what extent Machine Learning and Artificial Intelligence will influence the human advice origin to predict and prevent crime.

Drawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance. This title contains one or more Open Access chapters.

This edited volume provides new perspectives on how shame is experienced and transformed within digital worlds and Industry 4.0. The editors and authors discuss how individuals and organisations can constructively transform shame at work, in professional and private contexts, and with regard to socio-cultural lifestyle changes, founded in digitalisation and Industry 4.0. The contributions in this volume enable researchers and practitioners alike to unlock the topic of shame and its specifics in the highly dynamic and rapidly changing times to explore this emotion in depth in connection with remote workplaces, home office, automated realities and smart systems, or digitalised life- and working styles. By employing transdisciplinary and transcultural perspectives, the volume further discusses shame in the context of new lifestyles, religion, gender, sexual suppression, mental illness, and the nature of citizenship. Researchers, practitioners and students in the fields of industrial and organisational psychology, positive psychology, organisational studies, future studies, health and occupational science and therapy, emotion sciences, management, leadership and human resources will find the contributions highly topical, insightful and applicable to practice. Fresh, timely, thought-provoking with each turn of the page, this impressive volume explores shame in today’s world. Moving beyond the simple “guilt is good; shame is bad” perspective, authors from diverse disciplines examine adaptive and maladaptive aspects of shame in the context of contemporary issues (e.g., social media use, COVID-19) via multiple cultural and social lenses. Aptly named, Shame 4.0 is a treasure trove of rich ideas ripe for empirical study – a blueprint for the next generation of research on this complex and ubiquitous emotion. Bravo! --June Tangney, PhD, University Professor and Professor of Psychology, George Mason University, USA Uncovering Shame - To a much greater extent than other emotions like anger, grief, and fear, until recently most shame in modern societies has been hidden from sight. The text you see in this book is one of the steps that is being taken to make it more visible and therefore controllable. -- Thomas Scheff, Prof. Emeritus Department of Sociology, UCSB, Santa Bararbara, Ca.

Gregory M. Fulkerson offers a complete portrait of what communities are, how they work, and how they are embedded in urban–rural systems at regional, national, and global scales. After explaining the concept of urban–rural systems, Fulkerson walks through the central dynamics of environmental demography, political economy, culture, social interaction, the built environment, and community connections. His focus on urban–rural systems ensures that communities are understood as nodes within a network, overcoming the tendency to view them as self-contained. Each chapter in Community in Urban–Rural Systems: Theory, Planning, and Development offers a blend of classical and contemporary theories and concludes with relevant planning considerations. An additional chapter on community development provides strategies for translating planning considerations into action. The conclusion offers insights into long-term principles of community sustainability and justice.

This book explores how predictive policing transforms police work. Police departments around the world have started to use data-driven applications to produce crime forecasts and intervene into the future through targeted prevention measures. Based on three years of field research in Germany and Switzerland, this book provides a theoretically sophisticated and empirically detailed account of how the police produce and act upon criminal futures as part of their everyday work practices. The authors argue that predictive policing must not be analyzed as an isolated technological artifact, but as part of a larger sociotechnical system that is embedded in organizational structures and occupational cultures. The book highlights how, for crime prediction software to come to matter and play a role in more efficient and targeted police work, several translation processes are needed to align human and nonhuman actors across different divisions of police work. Police work is a key function for the production and maintenance of public order, but it can also discriminate, exclude, and violate civil liberties and human rights. When criminal futures come into being in the form of algorithmically produced risk estimates, this can have wide-ranging consequences. Building on empirical findings, the book presents a number of practical recommendations for the prudent use of algorithmic analysis tools in police work that will speak to the protection of civil liberties and human rights as much as they will speak to the professional needs of police organizations. An accessible and compelling read, this book will appeal to students and scholars of criminology, sociology, and cultural studies as well as to police practitioners and civil liberties advocates, in addition to all those who are interested in how to implement reasonable forms of data-driven policing.

Chapter “Predictive Policing in 2025: A Scenario” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This open access collection of AI ethics case studies is the first book to present real-life case studies combined with commentaries and strategies for overcoming ethical challenges. Case studies are one of the best ways to learn about ethical dilemmas and to achieve insights into various complexities and stakeholder perspectives. Given the omnipresence of AI ethics in academic, policy and media debates, the book will be suitable for a wide range of audiences, from scholars of different disciplines (e.g. AI science, ethics, politics, philosophy, economics) to policy-makers, lobbying NGOs, teachers and the educated public.