Predictive Policing in Germany. Opportunities and challenges of data-analytical forecasting technology in order to prevent crime

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 TEORECTICAL 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

5 FINAL REMARKS

5.1 Conclusion

5.2 Limitations and further research

Research Objectives and Key Topics

This master thesis investigates the potential opportunities and challenges for German police institutions and society when leveraging data-analytical forecasting technologies to prevent crime. It aims to determine how these technologies influence police practice and the public understanding of crime and safety.

• The theoretical landscape of Predictive Policing and related methodologies such as Hot-Spot techniques and Risk-Terrain Analysis.
• A comparison of the development and application of Predictive Policing in Germany versus the USA.
• Empirical evaluation through guideline-based expert interviews with police and societal stakeholders.
• Assessment of ethical considerations, including potential discrimination and the balance between public safety and individual privacy.

Excerpt from the Book

Summary of Chapters

1 INTRODUCTION: Provides an overview of the topic, describing practical examples of law enforcement in the era of Big Data and outlining the research question.

2 TEORECTICAL BACKGROUND: Details the conceptual and theoretical foundations, including terminology, objectives, and specific techniques like Hot-Spot and Risk-Terrain analysis.

3 EMPIRICAL WORK: Explains the methodology behind the qualitative study, focusing on the selection of experts and the application of guided interviews.

4 DISCUSSION: OPPORTUNITIES AND CHALLENGES: Interprets the findings from the expert interviews, specifically addressing the pros and cons of implementing forecasting tools.

5 FINAL REMARKS: Offers a conclusion and addresses the limitations of the current study while suggesting areas for future research.

Keywords

Predictive Policing, Big Data, Crime Mapping, Hot-Spot techniques, Risk-Terrain Analysis, German Police, Data-analytical forecasting, Qualitative research, Expert interviews, Crime prevention, Law enforcement, Ethical challenges, Digital forensics, Surveillance, Predictive analytics.

Frequently Asked Questions

What is the core subject of this thesis?

The thesis explores the integration of Predictive Policing tools into German police work, focusing on how data-analytical forecasting technologies can be used to prevent crime.

What are the primary thematic areas covered?

Key areas include the theoretical definitions of Predictive Policing, specific methodologies like Near-Repeat and Risk-Terrain Analysis, and the empirical examination of opportunities and challenges in the German context.

What is the main research question?

The research asks what the potential opportunities and dangers are for German police institutions and society when leveraging data-analytical forecasting technologies to prevent crime.

Which methodology does the author apply?

The author conducts a qualitative study based on 15 guideline-based expert interviews with members of police institutions and societal stakeholders, evaluated using an adapted category scheme by Meuser and Nagel.

What does the main body address?

The main body examines the theoretical background, the comparison between German and American policing approaches, and an empirical analysis of expert opinions regarding the efficacy and risks of Predictive Policing.

Which keywords characterize this work?

Core keywords include Predictive Policing, Big Data, Crime Mapping, Hot-Spot techniques, Risk-Terrain Analysis, and German police strategies.

How does the author address the risk of discrimination?

The author discusses concerns regarding biased algorithms, emphasizing the importance of functional transparency and the debate surrounding personal data usage in Germany compared to the USA.

What conclusions are drawn regarding the future of these technologies?

The thesis suggests that Predictive Policing will likely merge with other technologies and become a standard supportive tool, though it is constrained by strict data protection regulations and the need for human oversight.