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

Table of contents

LIST OF FIGURES

LIST OF TABLES

LIST OF ABBREVIATION

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

ACADEMIC REFERENCES

NON-ACADEMIC REFERNCES

TABLE OF APPENDIX

APPENDIX

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 varies 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. For the empirical part, the guideline-based expert interview is key and conducted on the basis of hypotheses revealed in literature. Results of the expert interviews (N= 15) were evaluated with an adapted category scheme of Meuser and Nagel. Resulting, that Predictive Policing is more than a buzzword. Approaches will definitely manifest itself not only as a supportive tool but also as a ground base for German police investigations. However, underlying technologies do not include neutralization software to mitigate erroneous results. As a consequence, the term is in the public eye with political and ethnological debates. For further results, a long-term survey would be necessary; collecting and analysing data from similar urban areas under the same complex conditions, which would require immense financial investments as well as extensions of fundamental rights.

Acknowledgement

Personal thanks applies to the scientific co-worker and criminologist Simon Egbert, who on the one hand has taken time for a personal interview to present the current state of art and his published projects, which also concern the topic Predictive Policing. He has already accompanied various scientific projects in the field of Predictive Policing especially in German authorities and taught future policing at the Institute for Criminological Social Research in Hamburg. Thanks, are also owed to Andreas Vachenauer and Stefan Heisinger. Both have been working for the police for many years in senior service level and also shared their experience during a personal interview, discussing aspects of preventive police work and current methodologies. Furthermore, I would also like to thank Ulrich Mayr who work as Commissioner for Domestic Violence in the Swabia police district and shared professional insights as well as associated technical changes from a different perspective.

List of Figures

Figure 1: Percentage of total criminal offences in Germany in 2018

Figure 2: Criminal activities in Germany from 2015 to 2018

Figure 3: The Prediction-Led Policing Business Process

Figure 4: Predictive Policing process from a methodological perspective

Figure 5: Hot-spot and heat-maps coordinated by grids

Figure 6: Concentric-Zone model by Ernest Burgess

Figure 7: Arranged excel file and QGIS settings

Figure 8: Final Near-Repeat result

Figure 9: Predictive Policing in Germany

Figure 10: Interview evaluation process

Figure 11: Conclusive facts and figures

List of Tables

Table 1: Law enforcement use of predictive technologies

Table 2: Hypothesis and assigned subcategories

Table 3: Examples of the second evaluation process step

Table 4: Examples of the third evaluation process step

Table 5: Examples of the fourth evaluation process step

List of Abbreviations

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1 Introduction

Since Predictive Policing is a highly topical issue especially in Germany, the introduction provides a basic understanding of criminal prosecution in times of Big Data. Furthermore, the purpose of thesis and the status quo of research areas as well as clear problem definitions are explicitly addressed.

1.1 Criminal prosecution in times of Big Data

At least after the successful British science fiction series Black Mirror by Charlie Brooker, the immense influence of Big Data and Artificial Intelligence on the media society and its digital future has become visibly tangible and got shaken in many areas. It can be seen, that some episodes have terrifying prophetic character (Hand, 2018), especially when it comes to gathering and evaluating data in order to predict offences and to automate decision-making processes. Those datasets administer social-media behavior of citizens but also supplement information, which are collected in public space via video cameras. Due to the enormous data growth and its variety, new possibilities and methods in range of data science and thus also in the security sector arises. While the principle of statistically evaluating offenders’ data in order to leverage preventive approaches is not entirely unprecedented; but predictive approaches in order to forecast offences, pose a new challenge. Nevertheless, the potential of merging different technologies such as Big Data Analytics or Machine Learning has not been fully exploited in every sector. Especially in the area of crime or crisis prevention, the application of such systems could be used even more efficiently. Although the topic of pre-crime analysis has attracted more attention in the last six years, police forces or other police organizations still hardly use intelligent prognose tools (Gorr & Harries, 2003) to utilize limited resources. In view of this challenge, companies identified new ways and opportunities to adapt their already developed applications according to recent use cases. Also, tech-giant International Business Machines (IBM) as a global Information Technology (IT) service provider (Gongla & Rizzuto, 2001) announced 2008, that campaigns like 'IBM: we build a smarter planet' will also address issues such as fighting against criminal operations. In IBM's matching commercial, a policeman analyzes data on an electronical pad while sitting in a car meanwhile a burglar prepares during the same time his robbery. In the next scene the policeman waits relaxed with a cafe in front of the supermarket, which the burglar has chosen as his victim. When the burglar is about to put on his mask to rob the supermarket, he sees the police waiting for him and greeting him. Frustrated, offender walks away, as he was again to late. With this scenario, it becomes apparent how IT companies like IBM imagine the future of law prosecution and the protection of citizens and assets. IBM Smarter Public Safety solutions are designed to make optimum use of personnel and resources while minimizing misses (Anand et al., 2011). Since already accumulated data and reports exist in different police authorities worldwide, IBM's vision is to link them with each other. Thereby both structured data such as prosecution or judicial data can be integrated into the analysis as well as unstructured data such as officer notes or social media activities (Anand et al., 2011). Hence, the term Predictive Policing (PP), which is one of the keywords making policing easier and less prone to errors in the future, stands as a synonym for prosecution in times of Big Data.

The conceptional changes and mission of PP is driven by the fact that civil servants and responsible ones are confronted with an ever-increasing number of different types of problems (Joh, 2014). These are based on the one hand on the rapidly changing demographic and ethnological environment, in which officers have to cope with a wider range of tasks as the rise of urbanization increases the crime rate in certain categories in both developed and undeveloped countries (Ahmad Malik, 2016). In addition, budget cuts due to holes in exchequer make it more difficult to work in a targeted manner. Especially, less staff is provided for dealing with daily tasks. Moreover, a trend in the way how crime is done in the digital and real world can be observed (Farwell & Rohozinski, 2011). For instance, nowadays wars can be waged online, since hackers collect data about an organization such as state institutions in order to hack into the network and then slowing down operations or even turning off the power of an entire nation. Because more and more criminal organizations use highly technical means to commit illegal acts (Arquilla & Ronfeldt, 1993) it is also important for the state to revolutionize police investigation methods.

By collecting data about a countries’ citizen and geographical occurrences, the focus also relies on minimizing disadvantageous decision making. Algorithm-based decision­making should provide system advice and reinforce human advice (Joh, 2015). One of the applied methods is Crime Mapping in combination with Data Mining techniques. Hence, predictions can be made at which place, at what time, which perpetrator commits which crime. A possible scenario could be, that an individual got arrested for a crime that has not yet been committed. Because, computational criminal forecasts confirmed after analyzing the individual data he or she will execute a criminal activity. Thus, system advice origin could strongly influence the human advice origin and the instinctive behavior of civil servants. According to the Max-Planck-Institute and its evaluation results, Predictive Policing is particularly successful in Germany in the area of domestic burglaries (Gerstner, 2017). However, having a closer look at the matter, it quickly becomes apparent that Predictive Policing is not only a question of how to obtain and evaluate data in a technical and ethical manner, but also a question of weighing the consequences for innocent citizen who respect rights and moral claims. In addition, on the one hand misgivings arise to what extent police forces currently have the funds to integrate such technologies into their daily work and on the other hand how environmental circumstances endorse these advancements. Since it is not unequivocal how society envisages the prospective inclusion of Big Data as a part of police law enforcement processes.

1.2 Environmental circumstances and status quo of research area

The goal of every police organization is to reduce the crime rate gradually in different categories by using assistive technologies such as Predictive Policing (PP) software or digital forensics which is mainly deployed for handling frauds in the range of Information technologies or computer science (Garfinkel, 2010). But before employing various tools to achieve the obj ectives, it is necessary to analyse crime groups and their annual statistics in order to be able to assess whether the respective resources are deployed successfully. Since the Master's Thesis focuses geographically on the development and use of forecasting systems in Germany, only crime statistics of this area are relevant. On the one hand, this decision stems from the fact that the global examination would go beyond the scope of the study. Secondly, the limitation of one country allows a more detailed elaboration as well as a greater similarity and thus comparability of knowledge. The following graph shows crime shares of different crime categories out of a total of 5,555,520 cases which were recorded in Germany in 2018 (PKS, 2019).

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4 Figure 1: Percentage of total criminal offences in Germany in 2018 (PKS, 2019, p. 7)

Admittedly, with 1,936,315 cases recorded, 34.9 percentage of the total number of crimes committed in 2018 were thefts. Therefore, it is not surprising that PP is increasingly applied in German federal states to prevent grand and petit larceny. In general, the overall number of total criminal offences has declined. In detail, a total of 5,555,520 cases were registered 2018, representing a decrease of -3.6% (PKS, 2019). It is difficult to judge whether this is due to forecasting technologies such as Predpol or Precobs since different environmental factors have to be taken into account. The measurement and evaluation of these statistics depends, on the one hand, on the number of a population in the respective country, which in Germany grew from 82,501,000 in 2005 to 82,792,351 in 2018. In addition, the crime detection rate has risen to 57.7 per cent in 2018, which is a new peak that may act as a deterrent for future ferrymen. On the other hand, Kuo et al. (2001) argues that the vegetation, architectural style and ecosystem of a city can also influence the criminal behaviour of its citizens. Meanwhile, Cohn et al. (2000) analysed that weekday times as well as weather data are crucial predictors of property crimes.

In addition, the level of poverty, the severity of tourism, the presence of the police, the unemployment rate as well as sociological aspects have an impact on the number of crimes in a country (Howsen et al., 1987). It becomes evident, that the performance measurements, which serves as a basis for the evaluation of opportunities and challenges of data-analytical forecasting technology, depends on a myriad of environmental circumstances. Therefore, research and analysis encompass four correlating players operating in the immediate ecosystem of Predictive Policing (PP) and influencing the human advice origin. This involves, on the one hand, society in general, which should be willing to accept these new technologies and feed systems with data. Furthermore, police institutions need to devote time and resources in order to leverage from individual forecasting innovations. In addition, police officers who are working with those applications have to act responsibly with sensible data. Moreover, external tech- companies as major stakeholders, provide the technical framework to integrate such systems into existing structures and deliver the best possible outcome. Last but not least, the government also play a decisive role within the ecosystem since they provide on the one hand the legal framework, allowing police institutions to link and analyse collected dat. On the other hand, the government guarantees citizens a certain transparency regarding usage and collection of data.

Although the ecosystem, as mentioned at the beginning of the section, has experienced positive developments, there are still critical categories, which can be defanged with foreseeable police work. The following diagram shows the increase in crime between 2015 and 2018 in the categories of murder, weapons misuse and illegal heroin trafficking.

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Figure 2: Criminal activities in Germany from 2015 to 2018 (Own illustration based on PKS 2015 - 2018)

It can be seen that criminal offences against the Weapons Act increased by +5.5% in 2018 compared to the previous years and that murder, manslaughter and killing on demand also increased by +3.9% (PKS, 2019). An exponential increase could also be observed in drug- related offences. In addition, an increase in the number of offences of at least +6.0% could be calculated in the following categories: Resistance to and assault on state authority, distribution of pornographic literature and drug offences in general (PKS, 2019).

The figures are based on current output statistics of the respective individual data records available in the State Criminal Investigation Offices (LKA) and the Federal Criminal Investigation Office (BKA). However, available figures and reports only reflect the so- called ‘bright field’, which represents the criminality recorded by the police (PKS, 2019). The number of unreported cases is estimated to be many times higher, especially with regard to economic offences or corruption (PKS, 2019). These figures are intended to prove that experts have to consider environmental and external influences when it comes to Predictive Policing performance measurements. In this respect, positive evidence is difficult to substitute (Santos, 2019). But still, the rise of criminality in various types of crime indicate the necessity to invest in new technologies.

1.3 Motivation driving the research question

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. The political and judicial expectation regarding this automated data processing, promises to be capable of preventing potential dangers and risks (Egbert & Krasmann, 2019). However, precise analysis of the consequences for social impacts has not yet been systematically researched.

PP, as described above, is applied in Germany to identify urban areas where burglaries or vehicle thefts are most likely to occur. Predictive Policing, as it is currently used in Germany, therefore does not constitute an encroachment on civil rights (Knobloch, 2018, pp. 3-5). The various limited systems in action mainly process location-based information, not personal data. However, this has led to the deduction of the research question, as more and more countries are also focusing on personal analysis (Kaufmann et al., 2018). The concrete research question of the project is therefore: ‘What are potential opportunities and dangers for German police institutions and the society to leverage data- analytical forecasting technologies in order to prevent crime?’ The aim of the Master's Thesis is to determine how the introduction of technologies for predicting criminal offences affects police practice (Egbert & Krasmann, 2019). In addition, it will be researched to what extent PP and the underlying technologies change both the police's and society's understanding of crime and danger. In a nutshell, the Master’s Thesis is driven by the motivation, to provide replies to the following problem statements. The problem statement base on the fact that with increasing population, criminal activities in certain areas may rise. Therefore, it is indispensable to evaluate opportunities and challenges of new prediction methods in order to be able to implement them in a purposeful way. Furthermore, it is necessary to elaborate PP which is often perceived as a black box in both police precincts and society.

1.4 Thesis structure

First of all, the introduction provides an overview of the topic by describing practical examples from law enforcement in times of Big Data. After that, the environmental conditions and focus as well as the research question and problem statement are explained in more detail. Before specific opportunities and risks of PP are considered, chapter two elaborates a conceptual and theoretical delimitation. In order to bring all readers to the same level, the theoretical background with its definitions guarantee a basic knowledge. This part of the Thesis will mainly reflect the informative purpose. In particular, the terminologies such as Hot-Spot techniques or Risk-Terrain Analysis. In addition, not only the goals of Predictive Policing are explained, but also other definitional refinements such as Big Data are given. In addition, the current police work and its chronological changes will be presented by taking reference to international application. Regarding this, an outline of the international and national status quo is given for subsequent analysis. Afterwards, the Master Thesis will illuminate the crime forecasting process and the respective data generation methods according to its technical background (Ahishakiye et al, 2017). Thereby the different categories in which data can be collected, such as predicting time versus predicting the offender, should be differentiated in order to guarantee a holistic picture.

In particular, the social embedding and evaluation of the criminological findings plays a decisive role for the acceptance of further models. On the one hand it is necessary to discuss already existing data collection and processing systems and possibly modify them. On the other hand, the appropriate employee training in order to be able to leverage an active external communication, should be eroded (Knobloch, 2018). To substantiate statements, a qualitative study and expert interviews are carried out. The empirical data collection first examines content of the expert interviews (Gobbens, 2010), whereas the section ‘discussion’ interprets the results with regard to chances and dangers of described technologies. Thereby, questions also deal with the practical appliance of Predictive Policing methods, to what extent they are already used and how further developments might decrease crime or might change natural behavior (Moses & Chan, 2018). This section also provides explicit answers to the research question. At the end some future scenarios and concluding remarks are mentioned.

2 Theoretical Background

The theory provides basis for generating qualitative research surveys with appropriate hypotheses. On the one hand, the terminology, transformation, goals and applications of PP are explained, but also basic theories and concepts such as Near-Repeat approaches are elucidated. Afterwards it was necessary to compare current methods used in Germany with those of the USA. Since Predictive Policing has its’ origins there.

2.1 Terminology of Predictive Policing and related buzzwords

Buzzwords such as Cloud Computing, Predictive Analytics, Information Centric Net­working, Internet of Things and Big Data, have already impacted the futuristic paradigms as well as the human horizons in the range of health, demographic change, sustainable agriculture and mobility but also in the range of societal security (Atzori & Morabito, 2017, p. 1). Looking at the definitions of those buzzwords, it becomes apparent, that all have a common denominator. These technologies are based on the exchange, analysis or storage of data and information, which are not only acquired from man-to-machine but also from machine-to-machine. For instance, Lupton (2014, p. 7) defines Internet of Things as a ‘tool in which digitized everyday objects or smart things are able to connect to the internet and with each other and exchange information without human intervention, allowing for joined-up networks across a wide range of objects, databases and digital platforms.’ Meanwhile predictive analytics deals with empirical methods to generate data predictions based on analysed information as well as to assist in creating practically useful models by leveraging explanatory modelling (Shmueli & Koppius, 2011, p. 1). As PP is an interface of these various technologies and has developed from the fusion of different approaches, it is difficult to assign the term to a topic.

However, Big Data and predictive analytics methods are particularly relevant in this context. Since the data sets, which are collected for example via social and information driven networking represents, similar to the already mentioned buzzwords, the genesis of PP. The increased performance and complexity of computer systems and hardware (Kümmerle, 1970, p. 342) as well as user-friendly software and exponentially growing data volumes have intensified the human consciousness about predictive modelling. These exponentially growing amounts of data, also known as data floods, are summarized under the umbrella term Big Data and built ground for PP. But the term has become ubiquitous (Ward & Baker, 2013, p. 1). The usage of the term varies according to a wide range of settings, thereby its meanings are still blurred (Mauro et al., 2015, p. 1). Mauro et al. have reviewed both, the existing definitions of Big Data as well as related research priorities. They concluded that the core characteristics of Big Data can be summarized by the following definition: ‘Big data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value’ (Mauro et al., 2015, p. 7). This abundance of information assets provides ground for various initiatives, but especially for forecasting future behavior and occurrence. According to Weiser (1991, p. 66-75) the most profound technologies are those that disappear as they weave themselves into the fabrics of everyday life until they are indistinguishable from it (Weiser, 1991, p. 66-75). Nowadays, the term Big Data has proven itself in the conjunction with predictive analyses and is used on a daily basis in commercial and private companies as well as in state institutions. Therefore, it is likely that in times of Big Data the future of police work is envisioned in PP. Predictive policing in general, implies forecasting technologies, which are able to predict the places and times of future criminal activities and preventing them ad-hoc. Perry et al. (2013, p. 16) defines PP as an application of analytical techniques - particularly quantitative techniques - to identify likely targets for police intervention and prevent crime or solve past crimes by making statistical predictions.’ Thereby the expression ‘prediction’ signifies a probability proposition and is therefore not defined as a fact (Belina, 2016, p. 4). According to Perry et al. (2013) linguistic differences might exist between the terms Predictive Policing and crime forecasting systems, but in practice the same technical process is meant. Based on this, the Master Thesis would also use these terms interchangeably. The generic term of both systems is Event Forecasting, which is according to Armstrong (2001), defined as follows: ‘forecasting is often confused with planning. Planning concerns what the world should look like, while forecasting is about what it will look like.’ In detail, forecasting systems utilize past data as a source to provide reasoned estimates that are predictable to identify the direction of prospective events.

All terms, focus on similar outcomes - to reduce the risk of a certain event, activity or reaction (Armstrong, 2001). However, for Egbert (2019, p.1) keywords such as ‘Big Data Policing’ or ‘ Algorithmic Policing’ are misleading in regards to PP. The idea, that police institutions use digital technologies and sophisticated data mining systems in order to combat crime seems to be a futuristic approach for society. However, practical approaches indicate that these forecasting tools leverage with a more conventional appliance than scientific references and narrative paradigms states. Ultimately, these systems build on existing concepts and criminological insights, such as Rational-Choice- Theory, Problem-Oriented Policing, Location-Based Policing or environmental criminology (Egbert, 2019). Innovation is guaranteed on the one hand by the provision of societal records and on the other hand by technically combining these datasets. For instance, making sense of scaled crime-based data by linking databases to open up new analytical approaches (Kitchin, 2014, p. 100) such as algorithmic-mediated analysis, seems especially ground-breaking for German police institution. From Egbert's (2019) point of view, the term PP can be divided into two layers since it is a cross-sectional strategy with multidimensional processes. The first layer collects data and leverages input techniques. It is important to consider the extent to which these techniques can really be used in daily police tasks in terms of resources and personnel. The second level refers to the generation of crime predictions by algorithmically mediated data analysis. Thus, PP is an application of data analysis technologies used by the police to generate usable predictions about scenarios and spatiotemporal conditions (Egbert, 2019, p. 3). Concluding, the definition of Morgan in Craig D. Uchida's National Institute Justice report (2009, p. 2) provides ground for an overarching terminology by covering discussed characteristics. Predictive Policing refers to any policing strategy or tactic that makes usage of Big Data. Those datasets are processed via analytical and quantitative techniques. The aim is on the one hand to inform forward-thinking and crime prevention in the range of scenarios and spatiotemporal conditions. On the other hand to predict criminal offences throughout probability and criminal patterns in order to use existing resources to an optimum.

2.2 Objectives and appliance of Predictive Policing

To ensure that these new approaches gain acceptance, evolve over time and are implemented by policemen in different levels, results must be tangible (Perry et al., 2019, p. 3). According to Zavrsnik (2017, p. 1) the objective of PP is ‘to issue crime forecasts in the same way as the Weather Service issues storm alerts’ and thereby to disrupt the ‘production cycle’ of crime. The opportunity of automated justice is to vaporize biases, heuristics and to confine fundamentally value-based decisions to ‘clean and pure’ mathematical reason (Zavrsnik, 2017, p. 1). The use of intelligent prediction tools, if implemented and trained wise can provide various benefits. The analytical function develops a variety of intelligent products to assist investigators in detecting, predicting and solving criminal investigation. Thereby, prosecution is based on collected data, which is displayed in well-arranged tables, charts, maps or other visuals. Aiming to support on the one hand adjudication of trials and on the other hand to support decision-management of chief executives or agency’s mission (Ioimo, 2018, p. 6). Law enforcement officers can benefit from tactical and strategic recommendations. Such reports include crime hot­spots, crime bulletins and summaries, view crime trends, possible threats, vulnerability or provides risk assessment analysis (Ioimo, 2018, p. 7). Computerized databases as a ground for PP, organize information and fosters meaningful relationships with other law enforcement staffs by allowing them to quickly obtain information and assisting in multijurisdictional cases. Since Predictive Policing tools are adapted to respective legal conditions, results are compliant with local, state, tribal and federal laws and regulations (Ioimo, 2018, p. 7). In addition, by leveraging underlaying software, existing resources can be utilized in a more efficient way and resources can be saved in the long run. This does not necessarily mean personnel savings, but rather the appropriate employment of police officers. For instance, PP systems can process large amounts of data in a short period of time. In the meantime, police officers have more capacity for other activities such as street patrol. In this way, the use of new forecasting technologies can simplify investigative efforts and, in the best case, crime statistics can be reduced through appropriate prevention tactics. Therefore, an added value can be achieved in the following sectors: police personnel management such as professional deployment and recruitment, police budgets management such as measuring the costs of overtime and other expenditures, offender monitoring, city or neighborhood planning i.e. design of spaces, police security resource allocation or infrastructure protection (Craig D. Uchida's National, 2009, p. 6). For instance, the LKA of the federal state North Rhine-Westphalia, identified during the implementation phase of the PP project called SKALA, following objectives for German police institutions: ‘its purpose is a strategic and target-oriented police work, which detects emerging hotspots at an early stage on the basis of known, crime-relevant determinants. The aim is to achieve a resource-conscious deployment of police forces and a reduction in the frequency of crime’ (Landeskriminalamt NRW, 2018, p. 10). This motive is predominant in the German federal states, although the total crime rate in Germany is declining (Knobloch, 2018, p. 10). In practise, different types of prediction are distinguished. On a superordinate level prediction between space and time or persons can be differentiated (Krasmann & Egbert, 2019, pp. 12) and combined in appliance. The data collection thus is determined in space and time related data or person related data. Location and time related forecasts include methods for predicting crimes, these approaches are used for predicting places and times with an increased risk of a specific criminal activity (Perry et al., 2013, p. xiv).

The most common forecasting technology in the range of criminal activities are Hot-Spot methods since police departments mainly work with location-based data. Here, ‘crime analysts prepare maps of crimes that have already occurred and those maps are used to deploy officers and to identify areas in need of intervention’ (Groff & La Vigne, 2002, pp. 34). Methods for predicting offenders, perpetrators’ identities and potential crime victims are approaches, which are more based on person related datasets. In detail, methods for predicting offenders aims to identify individuals, who commit a crime in the near future. Meanwhile, predicting perpetrators’ identities focus on profiles that accurately match likely offenders with specific past crimes. Tools which forecast potential crime victims are able to identify groups or individuals who are more likely to become victims of offender (Perry et al., 2013, p. xiv). Although the forecasting capabilities can be classified into different categories, further implementation and appliance processes are similar. Perry et al. (2013) summarizes that the process of PP can be presented in a classic four-step comprehensive business cycle (Perry et al., 2013). As can be seen in figure three. First two steps deal with the collection and analysis of crime, incident, and offender data, which requires data fusion. For detailed prediction, the data will be analysed according to the individual police operations and departments.

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Figure 3: The Prediction-Led Policing Business Process (Perry et al. in corporation with RAND, 2013, p. xviii)

The third step implies performances of police institutions to act against the predicted criminal event or even to solve old crimes. Possible interventions such as generic, crime- specific and problem specific aspects are classified according to their complexity. According to Perry et al. (2013, p. xviii), complicated interventions require more resources such as personnel but achieve better, more goal-oriented results. In order to successfully carry out missions, managers should not only discuss the critical part of preventive analysis but also provide information that fills the need for situational awareness among officers and staff (Perry et al., 2013, p. xviii). Building on this, the fourth step of the cycle can be completed. Each intervention leads to a criminal response which in the best case minimizes the risk or prevents the crime. Here, a short-term feedback and assessment is considered by guaranteeing, that the interventions are being carried out correctly and there are no apparent issues. In order to benefit from Predictive Policing in long-term, it is necessary to reprocess the newly generated data after each operation, which in turn leads to changing environmental conditions. Even though the Prediction-Led Policing Business Process by Perry et al. is intended to constitute a holistic approach, Bode et al. (2017, pp. 1-2) argue that this illustration does not meet the methodological requirements as it is applied in Germany. The following figure depicts the process from a police perspective (Bode et al., 2017, p.2) and is adapted by scientists (Seidensticker, 2017, p.296).

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Figure 4: Predictive Policing process from a methodological perspective (Bode et al. 2017, p. 2; Seidensticker, 2017, p. 296)

Whether the usage of Predictive Policing tools is beneficial in the respective context or not, depends on the resources. Therefore, it is necessary to start with a delinquency analysis and to distinguish what application makes sense (Seidensticker, 2017, p. 296). At the end, the executors give feedback whether the use of the Predictive Policing tool was meaningful in this case. Thereby, deviations within different federal states are always possible. The first step initiates the review and selection of data records as well as the collection and processing of datasets. Thereby, spatial and temporal consolidation are central. Already recorded police-related data can be combined with non-police-related data such as weather or temperature changes. For this purpose, it is important to geographically reference data in order to guarantee a uniform, machine-processable dataset, which is ground for further analysis. During the second step, a concrete statistical model, such as a regression or decision tree (Box et al. 2015, p. 305), is created depending on the available data. The third phase involves analysing the data based on the selected probability model. For example, the forecast calculation points out, which offence with an increased risk will take place in which area. Within the fourth step results of the prognosis are presented graphically for police officers and investigators. This can be done with proper dashboards on smartphones or tablets in order to utilize them ad-hoc. Depending on the current scenario, the data can be used as a basis for decision management. Thereby policemen can implement accurate prevention measures. The last step describes the performance measurement of the applied modelling, memorizes lessons-learned and verifies the plausibility of intermediate results (Bode et al., 2017, p. 3). Compared to the Prediction-Led Policing process by Perry et al., the adaption of Bode et al. includes a continuous evaluation after each step and leverage the feedback-culture.

2.3 Policing nowadays and its chronological transformation

Due to business consolidation and marketplace fragmentation the nature of police organization has fundamentally changed. Even though changes can be seen in stuffing or digitalization, physical approaches and objectives have remained steady. Preventive policing and the assurance of social security by tailored interventions according to the context, has always been a domain for police institutions. Between 1990 and nowadays there have been various policy changes within German police prevention programmes. The transformation is triggered by the anticipable adaptation phenomenon of social transition but also by devastating happenings such as assassinations (Behr, 2016, p. 1). Particularly in the 1990s, attempts were made to improve the relationship between police as an organisation and the population. Thereby citizens were considered as customers and social skills were decisive (Behr, 2016, p. 2). Especially, terms such as smooth policing or community policing are coined. Aiming for a holistic, service ecosystem where police acts as a buffer between citizens, stakeholders and the government.

In the early 2000s, the term smart policing was used, which not only links police operations from a technical perspective but also leverage knowledge from other countries. Smart policing initiatives and evidence-based, data-driven criminal-prosecution-tactics built ground for Predictive Policing (Coldren, 2013, p. 275). However, such systems have gained popularity for the first time in 2010. The first accompanying scientific evaluation of PP as a test operation was conducted in Germany between 2015 and 2017 (Knobloch, 2018, p. 5). Thereby, new opportunities and challenges for stakeholders and police organisations emerged in order use existing systems more effectively and benefit from exponentially surging data volumes. In particular, there is a shift from post-crime to pre­crime analysis (Wall, 2010, p. 22). These significant shifts ‘have occurred including major reforms in public policing, and a substantial expansion of the private security industry’ (Jones, 2002, p.1). These revolutions are not only due to the technical circumstances but also the society demands change. Already, US-American psychologist Abraham Maslow analysed that the need for security is the second most important value for human being (Stum, 2001). To meet these societal needs, the police on the one hand strengthened internal employee skills by implementing advanced study possibilities and on the other hand increasingly cooperate with external IT enterprises. Since experts operating in the free market economy are driven by different motivational factors and benefit from the competitive business environment. As a result, talents in the free market economy are used to work with current software and hardware systems, which makes potential employers even more attractive. Since the police work is generally accused of being too static and outdated (Marks, 2000, p. 1) the technical know-how of external organizations has to be implicated. In turn, police processes are becoming more transparent for society, which at the same time corresponds to the approaches of community policing. In order to avoid the danger of being dependent on external companies, internal personnel should be trained together with external staff. In conclusion, Predictive Policing is not an entirely novel approach of policing, it is rather a combination of prior police practices such as Crime Mapping and Geo-Policing, Quality- of-Life Enforcement methods or Intelligence-Led Policing (Egbert, 2018, p. 10).

2.4 Underlying theories and techniques

For various use cases, different techniques can be used to facilitate criminal investigation proceedings. These various approaches are not mutually exclusive, and can be combined for a more detailed outcome. Before processing the data on the basis of the selected theory, they must be available in systemic order. Since approaches are differentiated into spatial and person predictions as well as into predicting crimes, predicting offenders, predicting perpetrator identities and predicting crime victims, the following table provides an overview of individual instructions.

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Table 1: Law enforcement use of predictive technologies (Own illustration based on Perry et al, 2013, pp. 10-14; Egbert & Krasmann, 2019, pp. 12-15)

In detail, the table reviews, which approaches are already in use in order to statistically predict potential offenders, victims and to forecast in which area, when and what criminal activity could happen. These software solutions and tools are based on different psychological theories. Particularly relevant in this branch are Routine-Activity Theory, Rational-Choice Theory and Broken-Window Theory. The Rational-Choi ce approach states that all human actions are driven by desires and needs. In order to achieve this state of affairs, the perpetrator has to weigh up the costs and benefits. Which means that the higher the personal benefit, the more the perpetrator is willing to dare and sacrifice (Hill, 2002, pp. 29). Meanwhile, the Broken-Window Theory states that there is a causal link between physical and social disorder functions. Therefore, it is more likely that criminal activities occur in decayed and disordered areas then in neat neighborhoods (Ren et al., 2019, p. 1). The Routine-Activity Theory is based on the assumption that offenders act rationally and deliberately and thus follows a pattern. The precondition is, on the one hand, a motivated offender and an appropriate, unprotected target or victim (Santos, 2015, pp. 108-109). In order to determine the identity of possible offenders, both place and time as well as personal data are required and thus specialists combine methods. Even though person-related proceedings are becoming more and more attractive, civil servants in Germany concentrate on spatial proceedings (Egbert & Krasmann, 2019, p. 12) and mainly employ them in the area of burglary. The main justification behind their decision is, that these procedures are promising as they not only process individual data sets but also search for patterns. This indicates that crime follows a pattern in certain areas such as planned burglary. Since Near-Repeat, Hot-Spot Analysis and Risk-Terrain Analysis are the most frequently used techniques in Germany, the consideration is limited to these methods. In addition, both person-related and spatial predictions can be realized with Near-Repeat approaches. The explanation of more theories such as Heat-List techniques, Strategic-Subject-List, computer-assisted queries or statistical modeling would go beyond the scope of the master thesis and are therefore skipped.

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). In detail, place predications, which forecast a crime at a specific coordinate, differ in visualization from street, area or repeat victim prognoses (Eck et al, 2005, p. iii). Experts use different methods according to the questions and objectives. For instance, the question 'where are drugs sold?' refers to the identification of specific drug trafficking locations or street segments where drug traffickers and customers routinely meet. While 'what is the market for drugs' as second query, sounds similar, but focus on the origin of the customers (Eck et al, 2005, p. 1). Then, these results are visualized on city maps. In particular, streets with high crime probabilities are marked with lines. While a whole residential area behaves as a polygon and the forecast of a certain event is marked as a point. In addition, hazardous areas can also be displayed using heat maps. In practice, policemen can use up-to-date heat maps in order to decide in which areas patrolling is more efficient. The following image on the left shows a red polygon, which encircle a German neighborhood, where burglaries are very likely to happen in the near future. While the graph on the right represents a 'heat-map' of Portland, subdivided into grids. Red dots prove that there has been a high density of crimes and therefore individual measures have to be taken.

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Figure 5: Hot-spot and heat-maps coordinated by grids (Landeskriminalamt NRW, 2018, pp. 4; Dantec, 2016)

Particularly relevant when using crime mapping is the inclusion of the Concentrated-Zone Model, which visualizes the distribution of social groups in urban areas (Seidensticker, 2017, p. 293). This model states that a town can be scaled in rings starting with its center.

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