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.
Inhaltsverzeichnis (Table of Contents)
- 1 INTRODUCTION
- 1.1 Criminal prosecution in times of Big Data
- 1.2 Environmental circumstances and status quo of research area
- 1.3 Motivation driving the research question
- 1.4 Thesis structure
- 2 THEORETICAL BACKGROUND
- 2.1 Terminology of Predictive Policing and related buzzwords
- 2.2 Objectives and appliance of Predictive Policing
- 2.3 Policing nowadays and its chronological transformation
- 2.4 Underlying theories and techniques
- 2.4.1 Hot-Spot techniques as part of crime mapping
- 2.4.2 Near-Repeat approaches
- 2.4.3 Risk-Terrain Analysis
- 2.5 Lineages in Germany compared to the USA
- 3 EMPIRICAL WORK
- 3.1 Guided expert interviews as an instrument of data acquisition
- 3.2 Qualitative implementation and setting
- 3.3 Participants and Recruitment
- 3.4 Hypothesis and evaluation methodology
- 4 DISCUSSION: OPPORTUNITIES AND CHALLENGES
- 4.1 Interpretation of Results
- 4.2 Answer of the Research Question
- 4.2.1 Opportunities of applying Predictive Policing
- 4.2.2 Challenges of applying Predictive Policing
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This Master's Thesis aims to explore the opportunities and challenges of implementing predictive policing technologies in German police institutions. It investigates the theoretical foundations of predictive policing methods, examines their practical application in Germany, and compares German approaches with those in the USA. The empirical part uses expert interviews to analyze the real-world implications of these technologies.
- Theoretical foundations of predictive policing techniques (Hot-Spot, Near-Repeat, Risk-Terrain Analysis).
- Application and implementation of predictive policing in German police forces.
- Comparison of predictive policing approaches in Germany and the USA.
- Opportunities and challenges associated with the use of predictive policing in Germany.
- Analysis of expert opinions on the effectiveness and societal impact of predictive policing.
Zusammenfassung der Kapitel (Chapter Summaries)
1 INTRODUCTION: This introductory chapter sets the stage for the thesis by discussing the changing landscape of criminal prosecution in the age of big data. It establishes the context of predictive policing within German law enforcement, highlighting the current state of research and the motivations behind the research question. The chapter concludes by outlining the structure of the thesis.
2 THEORETICAL BACKGROUND: This chapter delves into the theoretical underpinnings of predictive policing. It defines key terminology, explores the objectives and applications of these technologies, and traces the chronological development of policing strategies, comparing Germany's approach to that of the USA. A significant portion is dedicated to explaining the core techniques used in predictive policing: Hot-Spot techniques, Near-Repeat approaches, and Risk-Terrain Analysis. The chapter provides a comprehensive overview of the theoretical framework upon which the empirical research is based.
3 EMPIRICAL WORK: This chapter details the empirical methodology employed in the thesis. It outlines the use of guided expert interviews as the primary data collection method, describing the qualitative implementation, participant selection, and the hypothesis-testing framework. The chapter explains in detail the process of data collection and the methodological choices made to ensure the rigor and reliability of the research findings. The adapted category scheme of Meuser and Nagel for evaluating the interview results is also discussed.
4 DISCUSSION: OPPORTUNITIES AND CHALLENGES: This chapter presents the interpretation of the results obtained from the expert interviews. It addresses the research question directly, outlining both the opportunities and challenges related to the implementation of predictive policing in Germany. The analysis focuses on the practical implications and societal impact of these technologies, considering potential benefits and drawbacks.
Schlüsselwörter (Keywords)
Predictive Policing, crime prevention, data analysis, forecasting technology, Hot-Spot policing, Near-Repeat analysis, Risk-Terrain Modeling, qualitative research, expert interviews, Germany, USA, opportunities, challenges, Big Data, policing strategies, ethical considerations.
Master's Thesis: Predictive Policing in Germany - Frequently Asked Questions
What is the main topic of this Master's Thesis?
This Master's Thesis explores the opportunities and challenges of implementing predictive policing technologies within German police institutions. It investigates the theoretical foundations, practical application in Germany, compares German and US approaches, and analyzes real-world implications using expert interviews.
What are the key themes explored in the thesis?
Key themes include the theoretical foundations of predictive policing techniques (Hot-Spot, Near-Repeat, Risk-Terrain Analysis), the application and implementation of predictive policing in German police forces, a comparison of approaches in Germany and the USA, and an analysis of the opportunities and challenges associated with its use in Germany, including expert opinions on effectiveness and societal impact.
What is the structure of the thesis?
The thesis is structured into four chapters: 1. Introduction (setting the context and research question), 2. Theoretical Background (defining terminology, exploring objectives and applications, and comparing German and US approaches), 3. Empirical Work (detailing the methodology of guided expert interviews and data analysis), and 4. Discussion: Opportunities and Challenges (interpreting results, answering the research question, and outlining opportunities and challenges).
What methodology was used in the empirical research?
The empirical research utilized guided expert interviews as the primary data collection method. The chapter details the qualitative implementation, participant selection, hypothesis-testing framework, and methodological choices to ensure rigor and reliability. The adapted category scheme of Meuser and Nagel for evaluating interview results is also discussed.
What are the key findings or arguments presented in the thesis?
The thesis presents an interpretation of results from expert interviews, directly addressing the research question by outlining both the opportunities and challenges related to implementing predictive policing in Germany. The analysis focuses on practical implications and societal impact, considering potential benefits and drawbacks.
What are the key terms and concepts discussed in this thesis?
Key terms include Predictive Policing, crime prevention, data analysis, forecasting technology, Hot-Spot policing, Near-Repeat analysis, Risk-Terrain Modeling, qualitative research, expert interviews, Germany, USA, opportunities, challenges, Big Data, policing strategies, and ethical considerations.
What is the overall contribution of this thesis?
The thesis contributes to the understanding of predictive policing implementation in a specific context (Germany), comparing it to a well-established system (USA), and providing a nuanced perspective on the opportunities and challenges of such technologies, incorporating real-world expert opinions.
Where can I find more information on this research?
Further details on this research can be found within the full text of the Master's Thesis itself (access details would need to be provided separately).
- Citation du texte
- Vanessa Bauer (Auteur), 2019, Predictive Policing in Germany. Opportunities and challenges of data-analytical forecasting technology in order to prevent crime, Munich, GRIN Verlag, https://www.grin.com/document/513184
