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.
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
2.4.1 Hot-Spot techniques as part of crime mapping
Crime mapping is a generic term used in criminology to describe the compilation, visualization of spatial crime patterns. Based on this, crime cartographies can be drawn according to the respective city (Paulsen et al., 2009). In order to calculate such crime maps, geo-information systems are employed which do not indicate plotting of crimes, but serve as a tool for processing collected spatial data. Collecting data refers to the assumption ‘that crime will likely occur, where crime has already occurred. Thereby, the past is prologue’ (Perry et al., 2013, p. 19). Crime mapping mainly refers to linking crime scenes and perpetrators on a map by means of geographical information and spatial-temporal coordinates (Hadamitzky, 2015, pp. 9-13). In this context, attempts are made to trace past crimes in order to find the perpetrator or the victim.
Since 2015, crime mapping in Germany has also been used to predict potential crime scenes and areas with a high crime density. Crime mapping is most frequently used in the areas of street robbery, burglary, vehicle crime or community borders. These predictive crime mapping methods are known in police jargon as Hot-Spot techniques. For Eck et al. (2005, p.3), ‘hot spot is an area that has a greater than average number of criminal or disorder events, or an area where people have a higher than average risk of victimization’. Hot-Spot Analysis helps the police to identify areas of high criminality, to predict the types of crime, which might be committed and suggest prevention tactics (Eck et al, 2005, p. iii). Recent developments indicate, that approaches differ on the level, the hot spot size and the geographic area of crime (Levergood et al., 2000, p. 2).
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.
- Arbeit zitieren
- Vanessa Bauer (Autor:in), 2019, Predictive Policing in Germany. Opportunities and challenges of data-analytical forecasting technology in order to prevent crime, München, GRIN Verlag, https://www.grin.com/document/513184
