Security Technologies and Social Implications E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Geisteswissenschaft
- Sprache: Englisch
SECURITY TECHNOLOGIES AND SOCIAL IMPLICATIONS Explains how the latest technologies can advance policing and security, identify threats, and defend citizens from crime and terrorism Security Technologies and Social Implications focuses on the development and application of new technologies that police and homeland security officers can leverage as a tool for both predictive and intelligence-led investigations. The book recommends the best practices for incorporation of these technologies into day-to-day activities by law enforcement agencies and counter-terrorism units. Practically, it addresses legal, technological, and organizational challenges (e.g. resource limitation and privacy concerns) combined with challenges related to the adoption of innovative technologies. In contrast to classic tools, modern policing and security requires the development and implementation of new technologies using AI, machine learning, social media tracking, drones, robots, GIS, computer vision, and more. As crime (and cybercrime in particular) becomes more and more sophisticated, security requires a complex mix of social measures, including prevention, detection, investigation, and prosecution. Key topics related to these developments and their implementations covered in Security Technologies and Social Implications include: * New security technologies and how these technologies can be implemented in practice, plus associated social, ethical or policy issues * Expertise and commentary from individuals developing and testing new technologies and individuals using the technologies within their everyday roles * The latest advancements in commercial and professional law enforcement technologies and platforms * Commentary on how technologies can advance humanity by making policing and security more efficient and keeping citizens safe Security Technologies and Social Implications serves as a comprehensive resource for defense personnel and law enforcement staff, practical security engineers, and trainee staff in security and police colleges to understand the latest security technologies, with a critical look at their uses and limitations regarding potential ethical, regulatory, or legal issues.
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
List of Contributors
Preface
Introduction
1 The Circle of Change
1.1 Introduction
1.2 Study Aims and Objectives
1.3 Methodology
1.4 Results
1.5 Discussion
1.6 Instead of Conclusion
1.A Appendix
References
2 Data Protection Impact Assessments in Law Enforcement
2.1 Introduction
2.2 Legal Framework and Guidance
2.3 Importance and Role of DPIAs in Law Enforcement
2.4 Key Legal and Ethical Risks in Algorithmic Policing
2.5 Best Practices: Mitigation Measures and Safeguards
2.6 Conclusion
Acknowledgments
References
3 Methods of Stakeholder Engagement for the Co‐Design of Security Technologies
3.1 Toward a Holistic Approach for Technology Assessment
3.2 Methods of Stakeholder Engagement
3.3 Conclusions
3.4 Recommendations
Acknowledgments
References
4 Performance Assessment of Soft Biometrics Technologies for Border Crossing
4.1 Introduction
4.2 Literature Review
4.3 Human Body Anthropometrics
4.4 Working on Dataset for Soft Biometrics
4.5 Some Influential Factors for Soft Biometrics
4.6 Working with Limited Data Using Transfer Learning
4.7 Experimental Result
4.8 Discussion
4.9 Conclusion
References
5 Counter‐Unmanned Aerial Vehicle Systems
5.1 Introduction
5.2 Drone Terror Threat Landscape
5.3 UAV Configurations and Categories of UAVs
5.4 Counter‐Drone Technology
5.5 Programming Rogue Drone Countermeasures
5.6 Training End Users of C‐UAV Systems
5.7 Conclusions
References
6 Critical Infrastructure Security Using Computer Vision Technologies
6.1 Introduction
6.2 Literature Review
6.3 Critical Infrastructure Security Using Computer Vision Technologies
6.4 Intelligent Situational Awareness Framework for Intruder Detection
6.5 Experimental Result
6.6 Distance Estimation
6.7 Conclusion
References
7 Evaluation of Content Fusion Algorithms for Large and Heterogeneous Datasets
7.1 Introduction
7.2 Data Preprocessing and Similarity Calculation Techniques
7.3 Description of the Algorithms Used
7.4 Proposed Methodology and Data Used
7.5 Results
7.6 Person Fusion Toolset Design for Future Development
7.7 Discussion
References
8 Stakeholder Engagement Model to Facilitate the Uptake by End Users of Crisis Communication Systems
8.1 Introduction
8.2 Risk and Crisis Communication Challenges for CBRNe
8.3 CBRNe Disaster Crisis Communication Systems, Especially Disaster Apps
8.4 The PROACTIVE Stakeholder Engagement Model
8.5 Lessons Learnt About the Stakeholder Engagement Model
8.6 Going Forward: Ensuring the Crisis Communication System’s Market Uptake
Acknowledgments
References
9 Crime Mapping in Crime Analysis
9.1 Introduction
9.2 Introducing Crime Mapping to the Slovenian Police
9.3 Crime Mapping Studies
9.4 Geographic Information Systems Laboratory – “GIS Lab” – at the Faculty of Criminal Justice and Security, University of Maribor and Cooperation with the Slovenian Police
9.5 First Steps and Inclusion of Crime Analysis to Research and Teaching at the Faculty of Criminal Justice and Security, University of Maribor
9.6 Discussion and Conclusion
References
10 The Threat of Behavioral Radicalization Online
10.1 The Growing Threat of Online Radicalization
10.2 The Implications of Online Radicalization
10.3 Delineating Essential Radicalization‐Related Online Activities
10.4 The Root Causes of Behavioral Radicalization Online: Identifying the Proper Vulnerability Indicators
10.5 PROPHETS Tools: Preventing, Detecting, Investigating, and Studying Behavioral Radicalization Online
10.6 Monitoring and Situational Awareness Toolkit
10.7 Policymaking Toolkit
10.8 Expert Notification Portal
10.9 Conclusion: Combining Social Science and Technological Insights
References
11 Blockchain Technologies for Chain of Custody Authentication
11.1 Introduction
11.2 MAGNETO Architecture
11.3 Literature Review
11.4 Semantic Framework for Recording Evidence Transactions
11.5 Evidence Lifecycle Management
11.6 IPFS Storage
11.7 Accessibility and Evidence Traceability
11.8 MAGNETO Features Against Cognitive Biases
11.9 Conclusions
References
12 Chances and Challenges of Predictive Policing for Law Enforcement Agencies
12.1 Next Generation Policing by Prediction of Crime
12.2 Lessons Learned from Previous Mistakes
12.3 A Question of Methodology
12.4 Intuitive Method
12.5 Statistical‐Nomothetical Prognosis
12.6 Clinical‐Idiographic Prognosis
12.7 Methodology of Criminal Forecasting
12.8 Rational Choice Theory
12.9 Learning Theories
12.10 Routine Activity Approach
12.11 The Ecological Approach
12.12 The Technological Dimension and Data‐Protection Challenges
12.13 Predictive Policing in the Field of Radicalization and Terrorism
12.14 Personal Risk Assessment in Context of Radicalization – Findings from the PREVISION Project
12.15 Personal Risk Assessment
12.16 Text Analysis
12.17 Identification of Problematic Content
12.18 Methodological Design
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.A.1 Basic information about analyzed studies, sorted alphabetically...
Chapter 4
Table 4.1 Length measurement across frames of the same individual.
Table 4.2 Information about a subset of the dataset used from FVG.
Table 4.3 Predicting outcome of pretrained models on input image from MMV p...
Table 4.4 The outcome of whole body human silhouette experiments.
Table 4.5 The outcome of upper‐half human silhouette experiments.
Table 4.6 Annotation methods and types.
Chapter 5
Table 5.1 Terrorist attack using unmanned aerial vehicles.
Table 5.2 Classification of UAVs by weight and flight range.
Table 5.3 UAS classification according to the US DoD.
Table 5.4 Generic classification of C‐UAV systems.
Table 5.5 Types of sensors used in C‐UAV systems.
Table 5.6 Comparison table for various detection sensors.
Table 5.7 Countermeasures used in C‐UAV systems.
Table 5.8 DroneWISE counter‐UAV training programme framework.
Chapter 6
Table 6.1 Mapping of intruder detection size to the zoom level.
Table 6.2 The mapping of FoV against camera focal length.
Table 6.3 Drone detection accuracy.
Table 6.4 The capability regards the method.
Chapter 7
Table 7.1 Similarity metrics categorization.
Table 7.2 Number of comparisons needed and similar person instances per dat...
Table 7.3 Execution time in seconds and threshold of each algorithm for the...
Chapter 8
Table 8.1 Validation of initial Crisis Communication System requirements du...
Table 8.2 Functionality requirements for the field exercises.
Table 8.3 Feedback on the existing features collected during the first Mobi...
Table 8.4 Additional features collected during the first Mobile App worksho...
Table 8.5 Requirements related to the prevention and mitigation of data bre...
Table 8.6 Accessibility requirements collected during the three focus group...
Chapter 11
Table 11.1 Solidity smart contract for accessibility and evidence traceabil...
Table 11.2 The binary format of contract processed within Ethereum.
List of Illustrations
Chapter 1
Figure 1.1 Study selection flow chart.
Chapter 3
Figure 3.1 Stakeholders’ mapping matrix: interest vs. power.
Figure 3.2 Stakeholder engagement assessment matrix.
Figure 3.3 PERSONA stakeholders’ identification and mapping.
Figure 3.4 Iterative process of co‐designing with stakeholders.
Figure 3.5 Global Google Trends lines for artificial intelligence (left) and...
Figure 3.6 Sentiments of people behind 1000 tweets away from airports mentio...
Figure 3.7 Means–end chain.
Chapter 4
Figure 4.1 A conceptual framework for seamless recognition.
Figure 4.2 Soft Biometrics taxonomy.
Figure 4.3 OpenPose framework for anthropometric features estimation.
Algorithm 4.1 Customizing OpenPose output – accessing and storing required l...
Algorithm 4.2 Feature estimation – arm's length.
Figure 4.4 Images used from FVG. (a) Normal walking – 2017. (b) Fast walking...
Figure 4.5 Images from MMV pedestrian dataset – frames at four different dis...
Figure 4.6 Pearson correlation by Gonzalez et al.
Figure 4.7 Impact of distance on Soft Biometrics.
Figure 4.8 Computing permanence score and stability.
Figure 4.9 Comparing feature and modality‐level fusion.
Figure 4.10 Transfer learning framework for feature extraction and classific...
Figure 4.11 Input image for pretrained convolution networks.
Figure 4.12 Our proposed framework for person verification.
Figure 4.13 The outcome of the segmentation process.
Figure 4.14 Minimum area silhouette segments.
Figure 4.15 Overview of One‐Vs‐Rest classification schema.
Figure 4.16 ROC curve for the whole‐body human silhouette experiments. (a) 2...
Figure 4.17 ROC curve for the upper‐half human silhouette experiments. (a) 2...
Chapter 5
Figure 5.1 Typical single UAV configuration.
Figure 5.2 Typical swarm configuration.
Figure 5.3 Categories of UAVs.
Figure 5.4 A schematic diagram of comprehensive C‐UAV system with AI and ML....
Figure 5.5 C‐UAV neutralization chain.
Figure 5.6 Snapshot of DroneWISE self‐assessment page.
Chapter 6
Figure 6.1 The critical infrastructure security perimeter.
Figure 6.2 Proposed dual‐camera system.
Figure 6.3 Operational states of the proposed system.
Figure 6.4 Detector configuration of the background objects.
Figure 6.5 Intruder object localization using the edge detection and dilatio...
Figure 6.6 The impact of focal length variation on the camera FoV.
Figure 6.7 The result of the drone detection following the PTZ operation usi...
Algorithm 6.1 Pseudocode for the video analytics algorithm for drone detecti...
Figure 6.8 Framework.
Figure 6.9 Encrypted media repository.
Figure 6.10 Intrusion detection.
Figure 6.11 Detector installation for intruder detection.
Figure 6.12 Person reidentification.
Figure 6.13 Central pixel distance curve of different situations.
Figure 6.14 Performance calculation.
Figure 6.15 The trajectory of a drone flight with tracking by PTZ camera.
Figure 6.16 Protection area.
Figure 6.17 The relation between the frame and the real world.
Chapter 7
Figure 7.1 Execution times for 100 random names.
Figure 7.2 Execution times for 1000 random names.
Figure 7.3 Execution times for 10 000 random names.
Figure 7.4 Execution times for all datasets and algorithms.
Figure 7.5 Similarity threshold results for all datasets and algorithms.
Figure 7.6 Person fusion tool flow diagram.
Chapter 8
Figure 8.1 Pie chart with the PSAB member category breakdown.
Figure 8.2 Pie chart with the CSAB member category breakdown.
Figure 8.3 The PROACTIVE Stakeholder engagement process leading to iterative...
Chapter 9
Figure 9.1 Crime in Slovenia in the period 1948–1950.
Figure 9.2 Accident black spots on the main road network in Slovenia in the ...
Figure 9.3 Crime mapping in Ljubljana using the Isopleth technique.
Figure 9.4 Crime distribution and hotspots in Ljubljana and Maribor.
Figure 9.5 Krimistat.si.
Chapter 10
Figure 10.1 Screenshot of the MST’s content level analytics visualizations. ...
Figure 10.2 PMT Comparator Page: Choose to view paragraphs side‐by‐side from...
Figure 10.3 Users can post questions to the ENP. Setting the visibility of t...
Chapter 11
Figure 11.1 Data management for evidence lifecycle.
Figure 11.2 Evidence data flow management.
Figure 11.3 MAGNETO platform architecture.
Figure 11.4 ANPR analysis carried out on CCTV footage collected as evidence ...
Figure 11.5 Ontology instantiation of ANPR records within MAGNETO common rep...
Figure 11.6 PROV‐ontology expanded terms build upon the starting terms.
Figure 11.7 MAGNETO IPFS immutable document store.
Figure 11.8 MAGNETO IPFS setup.
Figure 11.9 IPFS explores document repository among peers.
Figure 11.10 Preconfigured MAGNETO immutable document repository shared with...
Figure 11.11 MAGNETO IPFS distributed data repository settings to secure the...
Figure 11.12 Ethereum network configuration and operation with blocks mined....
Figure 11.13 Organizational framework for case evaluation studies, adopted f...
Figure 11.14 Face recognition component with a confidence level associated w...
Figure 11.15 Person identity presented with 82.5%.
Figure 11.16 Person identity presented with 85.5%.
Figure 11.17 Confidence of the person identity presented as 98%.
Chapter 12
Figure 12.1 The system design of PREVISION risk assessment tools.
Guide
Cover Page
Series Page
Title Page
Copyright Page
List of Contributors
Preface
Introduction
Table of Contents
Begin Reading
Index
Wiley End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
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Diomidis Spinellis
Anjan Bose
Saeid Nahavandi
Ahmet Murat Tekalp
Adam Drobot
Jeffrey Reed
Peter (Yong) Lian
Thomas Robertazzi
Security Technologies and Social Implications
Edited by
Garik Markarian
Emeritius Professor, School of Computing and Communications
University of Lancaster
Lancaster, UK
Ruža Karlović
Police College
Police Academy, Ministry of Interior
Zagreb, Croatia
Holger Nitsch
Department of Policing of Bavarian Police
Fürstenfeldbruck, Germany
Krishna Chandramouli
Venaka Media Limited
London, UK
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Library of Congress Cataloging‐in‐Publication DataNames: Markarian, Garik, editor. | Karlović, Ruža, editor. | Nitsch, Holger, editor. | Chandramouli, Krishna, editor.Title: Security technologies and social implications / edited by Garik Markarian, Ruža Karlović, Holger Nitsch, Krishna Chandramouli.Description: Hoboken, New Jersey : Wiley, [2022] | Includes bibliographical references and index.Identifiers: LCCN 2022029632 (print) | LCCN 2022029633 (ebook) | ISBN 9781119834144 (hardback) | ISBN 9781119834151 (adobe pdf) | ISBN 9781119834168 (epub)Subjects: LCSH: Law enforcement–Technological innovations.Classification: LCC HV7936.A8 S39 2022 (print) | LCC HV7936.A8 (ebook) | DDC 363.2/3–dc23/eng/20220805LC record available at https://lccn.loc.gov/2022029632LC ebook record available at https://lccn.loc.gov/2022029633
Cover Design: WileyCover Image: © Light/Getty Images
List of Contributors
Evgenia AdamopoulouInstitute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
Theodoros AlexakisInstitute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
Sebastian AllertsederDepartment Police, University of Applied Sciences for Public Services in Bavaria, Fuerstenfeldbruck, Germany
Luigi BriguglioR&D Department, CyberEthics Lab., Rome, Italy
Valeria CesaroniR&D Department, CyberEthics Lab., Rome, Italy
Krishna ChandramouliMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
and
Venaka Media Limited, London, UK
Konstantinos DemestichasInstitute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
Katja EmanFaculty of Criminal Justice and Security, University of Maribor, Maribor, Slovenia
David FaureThales, Courbevoie, France
Sven‐Eric FikenscherDepartment of Policing (CEPOLIS), University of Applied Sciences for Public Service in Bavaria, Fürstenfeldbruck, Germany
David FortuneSAHER (Europe), Harju maakond, Estonia
Rok HacinFaculty of Criminal Justice and Security, University of Maribor, Maribor, Slovenia
Bilal HassanMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
and
Faculty of Engineering & Environment, Northumbria University London Campus, London, UK
Grigore M. HavârneanuSecurity Division, International Union of Railways (UIC), Paris, France
Roxana HorincarThales, Courbevoie, France
Andrea IannoneR&D Department, CyberEthics Lab., Rome, Italy
Ebroul IzquierdoMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
Ruža KarlovićPolice Academy, Police University College, Zagreb, Croatia
Ioannis KompatsiarisInformation Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
Garik MarkarianEmeritus Professor University of Lancaster and CEO of Rinicom Intelligent Solutions Riverway House, Morecambe Road Lancaster LA1 2RX, UK
Thomas MarquenieKU Leuven Centre for IT & IP Law, Leuven, Belgium
Natasha McCroneRiniSoft Ltd, Sliven, Bulgaria
Sotirios MenexisInformation Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
Gorazd MeškoFaculty of Criminal Justice and Security, University of Maribor, Maribor, Slovenia
Wilmuth MüllerFraunhofer IOSB, Karlsruhe, Germany
Charlotte Jacobe de NauroisThales, Courbevoie, France
Holger NitschDepartment of Policing (CEPOLIS), University of Applied Sciences for Public Service in Bavaria, Fürstenfeldbruck, Germany
Carmela OcchipintiR&D Department, CyberEthics Lab., Rome, Italy
Guenter OkonInstitut für musterbasierte Prognosetechnik, ImfPt., Oberhausen, Germany
Damir OstermanMinistry of Interior Research and Innovation, Zagreb, Croatia
Dirk PallmerFraunhofer IOSB, Karlsruhe, Germany
Nikolaos PeppesInstitute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
Laura PetersenSecurity Division, International Union of Railways (UIC), Paris, France
Katherine Quezada‐TavárezKU Leuven Centre for IT & IP Law, Leuven, Belgium
Konstantina RemoundouInstitute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
Arif SaharCENTRIC, Sheffield Hallam University, Sheffield, UK
Thomas SchweerInstitut für musterbasierte Prognosetechnik, ImfPt., Oberhausen, Germany
Andrew StaniforthSAHER (Europe), Harju maakond, Estonia
Ines SučićInstitute of Social Sciences Ivo Pilar, Zagreb, Croatia
Theodora TsikrikaInformation Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
Stefanos VrochidisInformation Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
Xindi ZhangMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
Preface
While working in a law enforcement environment for a long time, I realized that societies are changing very fast. To protect citizens as the main objective for law enforcement, it is important to take all changes and developments in the society and also of deviant behavior into consideration. For police education, that means a constant observation of the developments concerning societal, technological, tactical, economical, and legal changes. The technological innovation within the last decades made vast steps and also changed all the other aspects mentioned earlier. Especially, dark web, deep web, Web 2.0, and drones pose threats that have not been known before.
Policing has to keep up with the challenges of the threat endeavoring by new technologies, for sure, but societal implications have also to be taken into account. Every innovation brings not only its positive aspects but also possible new threats to the security of the society. Therefore, it is of utmost importance that the education of law enforcement has to be on the spot for the newest developments of crime in all aspects.
Threats of the current times to the safety and security of citizens of free and democratic societies come from radicalization, terrorism, cybercrime, drone threats, threats to the misuse of personal data, digital identity theft, disinformation, fake news, and many more. Law enforcement has the duty and obligation due to its original design to counter these threats, to imagine possible future threats and by doing so, to protect the democratic and civil society from these threats by countering them with appropriate measures. The education sector plays a major role in this. As in my long‐time experience with policing, there is always a connection to social sciences in deviant behavior, also within the use of new technologies, I see it as important to connect these two major sciences to effectively fight threats to the civil society. Furthermore, to be up to date, it is important to take current research on technological and societal developments into account.
I am glad to see that in this book, the nexus of social science and technology for law enforcement agencies is perfectly represented. As an experienced lecturer and head of a policing education organization, I emphasize the combination of different disciplines in the way it is presented here. The given range is very broad and it is an eye‐opener for law enforcement to see all the innovations done by research. Technological and social science research do not exclude themselves. The combination of both provides a solution for the education of law enforcement and successful future policing to ensure the safety and security of citizens in a democratic and open society.
Ingbert Hoffmann
Head of the University of the Bavarian Police
Introduction
Scientific innovation in the area of security solutions for citizen safety has leapt ahead following the exponential increase in the capability to collect, store, and process information from various sources. The enhancements in the field of device connectivity through networking and their onboard computational power have enabled algorithmic intelligence to be deployed both at the cloud and also at edge devices. Such computing systems have also extended beyond traditional modalities of data processing to include new forms of data analytics, resulting from recent innovations in artificial intelligence (AI) and machine learning (ML) solutions. The ever‐increasing diverse data modalities and the ability to extract hidden patterns and relevant information through advanced data correlation have simultaneously enhanced the investigative capabilities of the law enforcement agencies (LEAs) in securing European society and citizens against foreseeable threats and terrorist attacks. The technological advancements in the field of information technology (IT) offer a sense of encouragement to equip LEAs to counteract criminal activities carried out by perpetrators.
In contrast to the classic tools available for LEAs (such as guns, handcuffs, and other less harmful weapons), the new approach to terrorism and crime relies on the emergence of technological tools such as mobile devices, AI, social media, drones, and GIS to name a few. As crime, in general, and cybercrime, in particular, become more and more sophisticated, a combination of complex social measures are required, which include prevention, detection, investigation, and prosecution. An effective solution to this problem requires continuous synergy and innovation from interdisciplinary scientific experts and the adoption of technologies into the operational practice of LEAs. Emerging new technologies change the landscape for LEAs, providing new opportunities for improving the effectiveness of problem‐solving and partnership initiatives and assisting in the implementation of organizational changes designed to institutionalize these processes. This book focuses on the development and application of new technologies that police officers could leverage as a tool for both predictive and intelligence‐led investigations and recommends the best practice for incorporation of these technologies into day‐to‐day activities by LEAs.
The use of technologies by LEAs is mandated in two different ways: the use of legacy technologies and novel platforms specifically dedicated for LEA applications (which we will refer to as professional LEA technology platforms) and technologies introduced for other (e.g. consumer) applications but which can be utilized by LEAs enabling new activities (which we will refer to as consumer technologies). Although significant progress has been achieved in developing innovative technologies, incorporation of such technologies into decision making by LEAs is still slow due to several objective and subjective reasons. For example, the development of professional technology platforms is associated with government decisions, requires public funding, is limited to LEAs only, and is expensive and conservative (and often slow) due to its nature. On the other hand, consumer technology platforms have seen rapid development in recent years. They are driven by commercial organizations often representing the private sector and have wider acceptance by the general public who are using such solutions on a day‐to‐day basis. In addition, the ethical discussions on the use of pervasive technologies that are complemented with the introduction of new European regulations such as GDPR have compounded the organizational reluctance in adopting some of the scientific advancements into day‐to‐day operational activities of LEAs.
In this book, we discuss both professional and consumer technology platforms, which were introduced recently for LEA applications. We demonstrate the drawbacks of the existing solutions and provide a blueprint for improving the overall adaptability of innovative technologies for enhanced policing and security. We also analyze emerging consumer technology platforms and show how these could be incorporated within the existing professional technology platform and improve LEA operations.
As the breadth of research outcomes to be analyzed is too vast to be addressed in a single book, we focus on technologies developed within the HORIZON 2020 EU Security Programme, which have either recently completed or have entered the final stages of completion, in which all authors played an active role. More specifically, we describe new technologies developed by numerous HORIZON 2020 projects, such as UNITY, NEXES, PROPHETS, TENSOR, CUPS, DRONEWISE, MAGNETO, DEFENDER, and PROPHETS (just to name a few) and provide both engineering and social perspectives on these technologies.
In addition, we also analyze the speed of acceptance of these technologies considering numerous factors, such as LEA subculture, training, and recent ethical and GDPR regulations, and provide recommendations for faster convergence of technologies into the LEA decision process. In addition, as new technologies have become available to everyone, including various criminal organizations and individuals with a criminal mind, the dual role of new technologies is discussed and evaluated – on the one hand, the ability to facilitate LEAs work, and, on the other hand, providing new opportunities for criminals. This is an interesting and challenging topic, which requires a multidimensional approach, covering technological advances, legislation, regulations, and licensing, just to name a few. Typical examples include satellite navigation systems and drones, which are used by both the LEAs and criminals. However, through certain technological advances, legislation, and state control, criminals (theoretically) cannot get access to higher levels of navigation accuracy. Similarly, drones are widely used by LEAs for search and rescue operations, however, they are also used by criminals for delivering contraband to prisons. Developing complex solutions, which will allow legitimate use of drones by the general public and the LEAs while preventing criminals from using drones, is one of the open problems, which is analyzed and described in the book.
The book is organized as follows:
The aim of the first chapter is to review the current state of research on policing technology to assess the police perspective on technology role and usage. It also encountered obstacles and observed needs. The review provides an in‐depth understanding of how technology is adopted in the LEAs, which factors moderate association between technology implementation and police efficiencies, and in what way integration of innovative technology in the LEAs could generate the most benefits for both police and community.
The second chapter outlines the interim legal and ethical impact assessment of the security solutions, technology, and tools by providing a preliminary evaluation of the ethical and legal concerns raised by the security practitioners in the usage of the system as well as templates and solutions to address these issues. The chapter also reviews the risks posed by the new systems, presents mitigation techniques aimed at addressing them, and provides an update on the implementation of legal and ethical safeguards in the system.
The third chapter is dedicated to improving the quality and accuracy of identity recognition and the impact of such technologies upon society. The chapter shows how research in technology can and, in some respect, must include collaboration with social sciences and social practice. More specifically, the authors of this chapter look at the challenges associated with biometrics‐based solutions in no‐gate border crossing point scenarios, including the procedures needed for the assessment of their social, ethical, privacy, and regulatory acceptance, particularly in view of the impact on both the passengers and border control authorities as well as the potential pitfalls of biometric technology due to fraudulent activities. In consultation with the collaborating border control authorities, the chapter reports on the formal assessment of biometric technologies for real‐world acceptance to cope with the increasing demand of global travelers crossing state borders.
Chapter 4 is dedicated to soft biometrics, which is emerging as one of the promising technologies for enabling faster border crossing. It is based on the fusion of multiple modalities in a soft biometrics’ framework. To showcase proof of concept, authors developed a taxonomy of soft biometrics features specific to verification at public places, including the context‐aware bag of soft biometrics. More importantly, quantitative features‐based verification is the main agenda along with extracting significant information from clothing and auxiliary attachments of the human body. The chapter is completed with experimental results showing a verification rate of more than 90% during multiple different experiments, confirming the great potential of soft biometrics in security research.
Chapter 5 recognizes that the illegal use of UAVs is now a serious security concern across the world as terrorists, activists, and criminals are adopting drone technology and developing new, creative, and sophisticated ways in which to commit crime, terrorism and invade the privacy of citizens. To address these current vulnerabilities, the chapter provides a detailed description of the counter‐UAV systems and proposes a holistic first‐responder agency command, control, and coordination strategy, underpinned by evidence‐based training for the counter‐terrorism protection of public spaces.
Chapter 6 is closely linked to Chapter 5 as it describes novel AI‐based machine vision solutions for detection, tracking and classification of UAVs and other objects which are of interest to LEAs. More specifically, the chapter presents a framework that integrates three main computer vision technologies, namely (i) object detection; (ii) person reidentification; and (iii) face recognition to enhance the operational security of critical infrastructure perimeters. The novelty of the proposed framework relies on the integration of key technical innovations that satisfy the operational requirements of critical infrastructure in using computer vision technologies. One such requirement relates to data privacy and citizen rights, following the implementation of the General Data Protection Regulation across Europe for the successful adoption of video surveillance for infrastructure security.
Chapter 7 describes a novel tool, which aims to unify different evidence data sources, such as video, audio, text/documents, social media and Web data, telecom data, surveillance systems data, and police databases, providing a common representation model for internal data representation. Data fusion combines the collected information in order to enable actions and decisions that would be more accurate than those that were produced by a single data source.
Chapter 8 describes technical and societal issues associated with the development and implementation of communication tools and platforms which will enable secure, reliable, and ethical communication between the first responders and the general public during the CBRNE attacks or events. The chapter also describes several recommendations for next‐generation Emergency Services that harmonize the use of mobile applications for purposes of emergency communications.
Chapter 9 describes the application of the geographic information system (GIS) by the LEAs in general and provides specific examples of its use by the Slovenian police. Importantly, it summarizes interviews with police chiefs on a local level describing their experiences as end users of GIS in solving antisocial problems and planning preventive activities.
Chapter 10 presents the result of a study that aims to counter the causes of online radicalization, cybercrime, and cyberterrorism. It also provides clear explanations of the definitions of four key areas: terrorism‐generated content, terrorist financing, terrorist recruitment, and training and online hate speech. The results are rooted in the use of different methodologies from a variety of disciplines and the focus is on the micro, meso, and macro level. The focus of this chapter is on the vulnerability indicators on the different levels. The chapter emphasizes the importance of a multidimensional approach in the prevention of these negative security phenomena for the general public good of society.
The eleventh chapter reports on the scientific activities carried out toward the development of tools and software components that complies with the European legal and judicial regulations for authenticating and authorizing the digital evidence using advanced and complex algorithms. The chapter outlines the implementation of three technologies, namely (i) a semantic framework for tracking and recording the processing of information; (ii) the use of distributed immutable storage that protects against external malicious attacks; and (iii) the creation and storage of digital hash within a blockchain environment to enable data audit logs.
The twelfth chapter is dedicated to predictive policing, which is still a rather young but very dynamic part of criminological research and police work. In addition to the fight against ordinary crime (e.g. domestic burglary), predictive policing is becoming increasingly important in the fight against terrorism, not only in predicting terrorist attacks but also in predicting the radicalization tendencies in biographies. The chapter describes innovative instruments for analyzing the risk potential of supporters of extremist groups or individual radicalization patterns, in order to be able to forecast terrorist activities and initiate suitable operational measures in a timely manner. In addition to the undeniable advantages of such personal prognosis methods, like the increase of internal security and the prevention of politically motivated acts of violence, the chapter also considers data protection and ethical questions focusing on the opportunities and challenges of predictive policing for the LEAs of the European Union.
Finally, in the Conclusion, we summarize the findings of individual chapters, show the connection between the topics and propose some new topics for future research in the area.
1The Circle of Change: Technology Impact on LEAs
Ines Sučić
Institute of Social Sciences Ivo Pilar, Zagreb, Croatia
1.1 Introduction
Infiltration of innovative technology into law enforcement agencies (LEAs) could be traced to the late 1990s when started the replacement process of previous forms of “intuition‐led policing” by “intelligence‐led policing” (Ratcliffe 2016) and then by “technologically enabled prediction‐led policing” (Sandhu and Fussey 2021). These processes parallel a shift in policing priorities “from crime‐fighting to public protection of ever‐widening scope” and harm prevention (Heaton et al. 2019, p. 2). As policing broadens its scope, so is the concept of crime becoming more tied to different external threats under the “wider umbrella of security” (Degenhardt and Bourne 2020). As a result, LEAs are increasingly turning to new technologies to address fast‐transforming settings of threats and harms more safely and efficiently, parallelly facing many transformations and challenges – from practical (Vrăbiescu 2020) over regulatory (Allen et al. 2016) to organizational (Vepřek et al. 2020).
Advances and changes in technology generated opportunities and transformations – across the criminal sphere and its impact on victims, communities, and policing. Technology provides new promising tools for LEAs in hardware – surveillance devices (e.g. CCTV, body‐worn cameras, and drones); mobile devices (e.g. navigation systems and mobile phones); “Internet of Things” (e.g. wearables and smart devices), as well as in software – statistics, database coupling, data mining, and profiling (e.g. “predictive policing” and “big data” algorithms); Automatic Number Plate Recognition (ANPR); mapping (e.g. GPS and heatmaps); biometrics (e.g. fingerprints, DNA, and face recognition); social media and open‐source intelligence (OSINT) (e.g. Ariel et al. 2018; Bradford et al. 2020; Clavell 2018; Custers and Vergouw 2015). Thus, LEAs, for some time now, are functioning in an intense information environment (Lorenz et al. 2021; Sørensen and Pica 2005). Technologies’ utilization and policing routines, strategies, and decision making turned into “complex socio‐technical mediations” (Fussey et al. 2021) because technologies not only alter users’ behavior (e.g. Ariel et al. 2015) but users also actively identify new needs as well as problems imposed by the technology implementation (Fielding 2021; Sandhu and Fussey 2021). Thus, the impact of technology on the transformation of police practices, increase in effectiveness and efficiency seems more complex (e.g. issues of occupational tensions and trust) (Miranda 2015; Wilson‐Kovacs 2021) and often not so explicit and/or not so prompt as expected (e.g. Sandhu and Fussey 2021).
One of the important reasons is also the primary purpose of a specific technology (commercial or professional), and the other is concurrent criminals’ access to and usage of technology. LEAs have rationally used various commercial and private sector technological solutions, only some of which are specifically designed for them (Rajamäki et al. 2018). It is important to distinguish between technology developed for commercial purposes or/and in a commercial setting (e.g. drones, mobile phones, and cameras) and specialized technology designed exclusively for LEAs (e.g. TETRA, hot‐spot maps, and databases) since each has its advantages and disadvantages. The legal and policy framework and the structural decisions made around commercial and professional technologies differ significantly, leading to many unresolved issues; for example, high costs and lengthy implementation and adoption of professionally in‐house developed technology for LEAs, problems arising from private companies taking care of the data collection and analysis for LEAs, and commercial software remaining proprietary knowledge usually unavailable for external scrutiny (“black box,” Sandhu and Fussey 2021).
Technology provides resourceful platforms for standard and “new” types of offenses, making crimes more sophisticated, organized, and less traceable. Digital tools now play a role in almost all crimes, and it remains debatable whether it is now beyond the capability of the current LEA to police it effectively (Horsman 2017). Since criminals usually have access to technology ahead of LEAs, they are in a constant race of trying to catch up with criminals’ plans for implementing attacks against individuals and organizations, especially if the LEAs are using the same commercial technology as criminals.
Moreover, those digital tools are defined in a sociopolitical context and with certain objectives that must be acknowledged (Miró‐Llinares 2020); for example, digital technologies “act as an important legal instrument and a vital part of the transnational harmonization mechanism that enhances efficiency in protecting EU external borders” (European Commission 2016; Vrăbiescu 2020, p. 11). By employing them, LEAs are balancing between obtaining international security while respecting (inter)national‐(inter)state legislations on data protection, human rights, and privacy (Fielding 2021). Security concerns and predicted increase in border‐crossing trends empower further border security technologization (Lehtonen and Aalto 2017) advocated by European Commission (2016). This proactive use of innovative technologies resulted in associating such technologies with securitization (in which public opinion is neglected) (Lodge 2004; Müller 2011; Skleparis 2016) and the militarization of police (technology that can have both civilian and military usage) (Martins and Ahmad 2020). Thus, besides challenges imposed on LEAs by its technical aspects, innovative technology also ruffles legal and ethical dilemmas surrounding legitimacy, accountability, and governance. Having in mind the severe and far‐reaching consequences of LEAs’ decisions and consequences of public mistrust and lack of confidence in police (Ariel 2016; Kounadi et al. 2015), the broader societal implications of technology implementation should be taken into consideration.
In Europe, the effects of technological developments for police organizations and citizens have not yet been comprehensively or systematically studied (see for exception: Buckingham et al. 2019; Edwards et al. 2015; Maskaly et al. 2017) and dissemination outputs resulting from Horizon 2020 projects are just emerging. Therefore, to better understand and value technology and its implications, the assessment requires a more comprehensive overview not only of empirical data on perceptions of technological solutions’ endorsement by both POs and the public capturing benefits but also limitations and drawbacks, and future directions (Miró‐Llinares 2020).
1.2 Study Aims and Objectives
There is a knowledge gap regarding how different police systems around Europe have adapted to digital challenges and how they cope with innovative technology demands. Thus, this chapter aims to review the results of empirical research on innovative technology adoption by different LEAs’ jurisdictions in Europe in the period 2014–2021 by representing both LEAs and public perspectives on technology role and usage as well as experienced challenges and barriers.
1.3 Methodology
Studies for this rapid review were selected based on the following inclusion criteria: primary studies conducted in Europe, journal‐article‐type publications, written in English, and published from 2014 onward. The year 2014 was chosen as the baseline year because it is the start year of the Horizon 2020 EU Research and Innovation program. The search was limited to peer‐reviewed publications that contained empirical assessments and/or evaluations of technology implementation into LEAs, using quantitative and/or qualitative research design, conducted on humans, and presenting LEAs and/or public perspectives.
Relatively broad concepts for the research topic were chosen: “technology,” “police,” “LEA,” “study,” and “survey,” and all possible combinations of these keywords were used in publication search. To narrow the search, the presence of those keywords was required in the published abstract.
Three academic databases were searched for combinations of the keywords in English‐language journals: Web of Science Core Collection – excluding Chemical Indexes, Scopus, and EBSCO. The initial search process resulted in 1697 publications. Additional 32 publications were selected for inclusion based on cross‐referencing, resulting in 1729 publications.
Publications revealed in the initial search were the first subject to screening for duplicates and then followed by “title and abstract” screening. Then, the full text of all potentially relevant papers was retrieved for closer examination. The inclusion criteria were applied first against the manuscripts’ abstracts and then against the full‐text version of the selected papers. Based on the screening and extraction process, 28 research articles were selected for analysis (Figure 1.1). Information extracted from studies included the authors’ names, year of study publication, country of study, technology researched, objectives, the study design/data collection, and sample size (Table 1.A.1). Finally, findings from the included publications were synthesized using a narrative summary.
1.4 Results
1.4.1 Study Characteristics
Regarding the temporal and geographical distribution, most of the analyzed studies were published in 2020 (32%), and the majority is conducted in the United Kingdom (50%), while 20% was conducted in more than one jurisdiction. Respondents were mainly POs (75%), while public and POs were captured in two research areas. All studies were cross‐sectional, and the majority of them were entirely or partially qualitative (70%). In qualitative studies, the dominant method of data collection was interviewing (70%). More than one‐fifth of the research used mixed‐method design (21%), and four endorsed experimental paradigms. Consequently, data were mainly collected on convenient, small samples. In two researches, data were collected from the (nationally) representative samples. In most analyzed studies, researchers were analyzing the implementation of social media (18%), smart borders (17%), diverse technologies (17%), followed by crime mapping (10%), and predictive policing (10%) (Appendix 1.A).
Figure 1.1 Study selection flow chart.
The following section will first summarize the results of the studies that capture technology implementation into LEAs more broadly, followed by the summary of the adoption of specific digital tools.
1.4.2 Diverse Technologies Adoption
Generally, there are optimistic views among European LEAs regarding technology implementation and adoption. However, the majority also observe obstacles in this process and would prefer to have a more comprehensive overview of the available technologies, together with more grounded information on whether and how those technologies are working before actual frontline adoption (Allen et al. 2016; Custers and Vergouw 2015; Miranda 2015).
Findings from Portugal (Miranda 2015) show that inspectors’ beliefs about the positive contribution of technology to the criminal investigations’ effectiveness are grounded in associating actions based on scientific evidence with objectivity, credibility, and legitimacy. Investigators expect that technology will help them resolve more sophisticated crimes, primarily those criminals commit, by using innovative techniques and strategies. The capacity of technology to (automatedly) obtain, collect, record, sort, store, analyze, and compare information through databases and online (e.g. through social networks) faster, easier, and more error‐free was the most significant assistance to their work.
When it comes to technological consequences for changes in their work, some investigators predict the disappearance of traditional police work in the future and its complete replacement by innovative technological solutions (Miranda 2015). Others think that criminal investigation will not change so much and that traditional and basic police methods will remain the same. They argue that traditional detective work remains necessary even if combined with science and new technologies since there is still a need to manually ascertain the results at the end of an “automated” investigation (Miranda 2015). As one of the negativities of technology implementation, they ascertain how this trust in technology infallibility contributes to eliminating human responsibility and reducing POs’ effort (Miranda 2015).
As obstacles to technology implementation in police work, inspectors mentioned: lack of technological and human resources, restrictive legislation, and the lack of collaboration in the access and sharing of information (between the scientific organization and police, and between different national police bodies, services of scientific and technical policy) due to lack of confidence from some authorities toward others, lack of knowledge about the capability of some units, the mismatch between criminal investigation and science and technology (not being aware or capable of applying some things that already exist), resistance to innovation, especially among older POs, certain conservatism and nonacceptance of new technology by legal bodies, resistance and distrust toward new (genetic) profiles (Miranda 2015).
Wilson‐Kovacs’ (2021) explorational study of the part‐time digital media investigators’ embeddedness in police investigations confirms how POs’ unwillingness to use digital technologies, and for their fear of digital processes, as well as lack of seniors’ support and prefer using traditional methods over innovative one, represents obstacles in technology implementation in the UK police. Custers and Vergouw (2015) internationally collected data confirmed that most respondents experienced obstacles in applying technology in policing. Especially in the domain of organization (insufficient support, guidance, and management, insight and overview, connection with international developments, financing, and technology availability), followed by legal (lack or insufficient clarity of a legal basis, especially on how to deal with personal data) and then technical obstacles (insufficient technology availability and overview of available technologies). In Wilson‐Kovacs (2021) study, part‐time digital media investigators also expressed their concern about the innovative technology data reliability outside a controlled (educational) environment. They complained about their workloads during specializations accompanied by big expectations, confusion about their duties, and their roles and responsibilities, as well as lack of guidance and post‐training. Consequently, the support they provide to POs with technology implementation is weakened and not fully comprehended.
According to Abbas and Policek’s (2021) analysis of the post‐adoptive stage of mobile technology POs found several digital features (e.g. photographs, the e‐signature feature, and Google Maps) as efficiency promotors, but overall do not consider that mobile devices either made their work more exciting or increase their job satisfaction. Moreover, most POs believe that organization adopts technologies that are not useful, not being convinced of the new systems’ benefits. Also, especially officers of long service were dissatisfied with how new technologies are implemented (ill preparation for device usage, insufficient help, and support to officers who are experiencing problems). Additional reasons for officers’ resistance and responses/adaptations during the post‐adoptive stage of mobile technology were considering the devices frustrating, accessing the low quality of information, facing poor signal and connectivity, limited data storage capacity, etc. The usefulness of the device feature contributed to observed benefits. In contrast, limited IT skills, the uneasiness of getting used to new work methods, older age, inadequate reliability, and the uselessness of these features contributed to the perception of the mobile device as a threat. Generally, Abbas and Policek (2021) recommended that organizations invest in (continuous) training programs, especially for older POs, to enhance their technical skills and create a positive user experience through which the perceived usefulness of technology will be promoted.
Sumuer and Yildirim (2015) assessed factors related to acceptance of general electronic performance support systems in Turkey (EPSS – integrated into application software and provide POs with task structuring, knowledge, data, tools, and communication components to facilitate their work processes). Behavioral intentions, perception of usefulness, and positive attitudes toward using EPSS were strongly interrelated, and perceived ease of use had a significant direct influence on both perceived usefulness and attitude toward using the EPSS. Furthermore, estimation on system usefulness and easiness to use were, similarly to Abbas and Policek (2021) findings’, related to (i) user personal characteristics – better computer knowledge and skills, more experience, less anxiety about using the system, enjoyment with the system, motivation to use (ii) system characteristics – simplicity and clarity of the system interface, user‐friendliness, simplified data entry, performance support facilities such as access to data and information, step‐by‐step guidance, and automating job‐related tasks; importance of relevance of the system to their job; less part of the system that produce overload; usability of the mobile personal computers – the learnability of the system, regular updates to fix problems or bugs, improve existing tools and resources, and add new functions and functionalities to the system and (iii) organizational characteristics – improvement of information technology infrastructure in terms of network and hardware in the organization – training offered for using information, for using technology infrastructure (e.g. help systems and peer and technical support), and adequate personnel management.
Custers and Vergouw (2015) international findings showed that technologies used the most by police forces are also assessed as those having the highest potential (DNA, camera surveillance/CCTV, face recognition, wiretapping, network analyses, GPS/tracking systems, biometrics, and fingerprints) and were producing the highest satisfaction and preference for its use. ANPR, virtual reality, weapon technologies, social media, and bodycams are lesser used technologies that are hardly mentioned as most satisfactory. LEAs’ representatives are most interested in the future implementation of face recognition, virtual reality, drones, and GPS/tracking system, but the potential of those technologies is not estimated as high (apart from GPS/tracking systems). Allen et al. (2016) findings related to the condition of IT infrastructure in UK police forces showed that overall, most technology areas are seen as up‐to‐date or old‐but‐serviceable, in most cases, in better condition than before. As expected, the areas seen as being up to date tend to have a lower expectation of transformational change. The results highlight call handling, dispatch, custody management, crime analysis, and mapping as areas where technology was significantly described as not being up to date. As key priority areas in police forces are listed incident management, mobility/portable technologies (as opposed to fixed ones), and information governance. Reliance on collaboration in systems development with other police forces is high and assessed as a high priority, while reliance on cloud computing and outsourcing to deliver and support IT capability is low and assessed as a low priority for most police forces. These findings go against governmental efforts to strengthen collaboration between different police services and between police services and other agencies, and against considering the lack of collaboration in the access and sharing of information as an obstacle to technology implementation (Custers and Vergouw 2015; Miranda 2015). International findings (Custers and Vergouw 2015) also indicated how within and between different jurisdictions prevails an entirely different conception of what is comprised by the same type of technology (e.g. biometrics) and that there is a lack of overview of available technologies in police forces.
1.4.3 Real‐Time Data Providers
It was assumed that body‐worn cameras (BWC) facilitate new opportunities to examine (document, inform, and assess) police decisions and reduce improper decision‐making enabled by subjective policing (Sandhu and Fussey 2021).
The BWC randomized‐controlled trial (Henstock and Ariel 2017) showed a reduction in the prevalence and severity of police use of force used when wearing BWC but only in the cases with less aggressive force response (e.g. open‐hand tactics including physical restraints and non‐compliant handcuffing). The study pointed to the need to test BWC efficacy regarding the level of force response. Later, BWC study by Miranda (2021) showed that POs consistently compare the use of BWCs to other visual surveillance technologies (namely CCTV) regarding their administration and data management, showing special concern about data gathering, storing, and accessing (especially access of unauthorized third parties). POs pointed out that BWC design (e.g. bulk, dimensions, and weight), body placement (e.g. chest and head), and situational characteristics substantially impact their operations and practice and reliability in use. POs are concerned with where to place the camera, (un)intentionally moving of camera, losing it, quality of video footage (i.e. in the night economy), etc. BWC showed to be more useful in more remote (rural) areas, but these settings especially present problems of connectivity and, consequently, video‐information quality. In order to ensure the suitability of BWC equipment to their operational needs, the perspectives and experience of frontline POs should be considered when designing them.
Similar to BWC, other real‐time data providers (e.g. POI application/systems) are expected to decrease the number of situations in which POs act without information – and reduce improper decision making enabled by subjective policing.
In Lukosch et al.’s (2018) study, frontline POs assessed a “mobile location‐based real‐time notification system” as pragmatic and straightforward to use, valuable and usable, especially for POs new to the area. App usage also contributed to a reduction in emergency response calls. Furthermore, to keep using the application daily, POs pointed to the need for continuously updating information and the need for co‐driver assistance if the application is used in the vehicle (Engelbrecht et al. 2019). However, providing POs with more information on situations through this app did not result in the expected increase in situation awareness (situation understanding) (Lukosch et al. 2018). Thus, an expected decrease in the number of situations in which POs act without information and a reduction in improper decision making enabled by subjective policing were not gained by this application.
Further testing of the location‐based real‐time notification system that gives POs information on the spot and lets them report incidents on location (Engelbrecht et al. 2019) revealed that system usability varies with the type of front officers’ work (neighborhood patrol, surveillance, and emergency) and type of transportation (foot, car, and bicycle). The app was perceived as the most usable by neighborhood patrol agents on foot since its usage fits the most closely to their daily routine work. Again, app usage did not result in differences in situational awareness, productivity, or task load. Also, there was no difference in the app assessment due to the difference between locations in hot spot density. It was assumed that the lack of effect on situational awareness could be attributed to the higher number of notifications that raise attention demands. That lack of stress reduction could be attributed to a disruption in working roles and division of responsibilities by introducing this new system with high information density. POs liked the possibility of creating reports in their patrol area on location directly but showed concern about database fragmentation, long‐term maintenance, etc. The system enabled them to visit substantially more hot spots during their shift, but some POs expressed concern over constantly tracking their location, monitoring them, and logging activity. POs preferred the mobile app over the smartwatch version. It was recommended to use additional filters to decrease the number of notifications received to lower the attention demand and that new digital tools should be implemented to improve work performance while still allowing officers to keep their preferable work routine (Engelbrecht et al. 2019).
Saunders et al. (2019), in an experimental comparison of VR training and full‐live training exercise, prove that performance increased significantly within all training groups and that knowledge scores did not differ significantly across groups after the training. These results supported using VR training as a backup solution for training POs.
Bradford et al. (2020
