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Smart Street Lights: Using an Emergency Communication Device to Support Situational Crime Prevention

Charlotte

Universiteit van Amsterdam (11117958)

Jermaine Dol

Universiteit van Amsterdam (11060441)

Michael Hadiono

Universiteit van Amsterdam (11104902)

ABSTRACT

Will be defined later.

Keywords

Internet of Things, Smart Streetlights, Emergency Communication Device, Situational Crime Prevention.

1. INTRODUCTION

With approximately 90 million streetlights installed worldwide, streetlights can easily be found in most urban areas (International Energy Agency, 2006). As the source of lightning during the night, street lighting does not only encourage socio-economic activities at night, but it could also reduce crime and traffic collisions (Pease, 1999 & Wanvik, 2009). This statement is also supported by several studies about the notion of the installation of street lightning improves the perception of personal safety and security (Atkins, Husain, & Storey, 1991 &  Ramsay, 1991). Farrington and Welsh (2002) suggest two main theories of why improved street lighting may cause a reduction in crime. The first theory suggests that improved lightning leads to the prevention of crimes by improving the visibility of potential offenders and by increasing the number of people on the street. The second theory emphasizes the increasing community pride, community cohesiveness, and informal social control because of the people’s awareness regarding community investment in the area. Deriving from the characteristics of streetlights mentioned earlier, the functionality of streetlights could be enhanced to support the perception of safety and security in the neighborhood.

When it comes to the effort of improving the functionality of streetlights, the current phenomena in the development of technology can be considered as a way to fight with crimes. Ten “critical” trends and technologies impacting IT for the next five years were laid out by Gartner in 2012 and among them is the Internet of Things (IoT). The Internet of Things is a concept in which many things around us are connected and enabled such as streetlights being networked and things like embedded sensors, augmented reality, and near field communication are integrated into situational decision support, asset management and new services (Vermesan & Fries, 2013). Even though Ruoklainen and Kutvonen (2005) suggest the IoT add the complexity of IT, many business opportunities can be emerged because of IoT’s trend. Vermesan and Fries (2013) mention controlled alarm systems as one of the examples of the human users’ needs for the IoT applications. What makes an object with IoT distinguished with a normal object is that IoT will make an object autonomously manage its connection with the internet or based upon request of someone or something remotely.

1.1 Problem Statement

Crime has been a problem that is heredity from time to time along with the human civilization. While the society has always struggled to define the necessary action on facing criminal activity, Virginia Crime Prevention Association (1993) suggests the best alternative to illegal behavior is prevention. Even though examples of punishments for certain crimes can be easily found in the history, the evolution of crime prevention is still hard to be defined. Several reasons behind this circumstance are perhaps the militaristic viewpoint that tends to prevail in history and the less excitement of prevention compared with investigations, arrest, and sanction.

In Cohen and Felson’s (1979) terminology, the commission of a crime requires the availability of a suitable target and a supportive situation for committing the crime. Thus, crime cannot be explained simply by explaining criminal dispositions, but how such dispositions interact with certain factors favoring crime to produce a criminal act is also needed to take into account (Ekblom, 1994). Therefore, as Garland (1996) has argued, Britain and the Netherlands have been aware that crime control should not merely be the government’s responsibility, but all sectors of society should be contributed to maintain more secure surroundings.

Locking doors, securing valuables, and guarding purses may be seen as the effort of individual to reduce the risk of crime. Likewise, schools, factories, offices, shops and many other organizations and agencies invest various devices as their precaution to safeguard themselves from crime. Nevertheless, it has been assumed that those precaution activities will merely displace criminal’s attention elsewhere, without giving a clear outcome in reducing crimes Clarke, 1997). Thus, efforts in making public places, environments, and other shared facilities more secure should be done so that a more conducive situation to prevent crime could be created.

Many efforts have been undertaken to support situational crime prevention, but crime remains a major concern of the society in general. On the other hand, the trend of IoT is emerging and we argue that the use of IoT could be one of the solutions to increase deterrence of crimes or, how it is also called, to design out crime (Clarke, 1980). We found that there is still lack of research and IoT appliances to prevent crime. Therefore, in this research, the effect of using the emergency communication device in streetlights to prevent crimes is discussed further and people’s views on using such device are analysed as well as the view of the Dutch police department on the topic. We also investigate the role of that community has in preventing crime in relation to our main research topic. The key requirements of the emergency communication device are also collected.

2. LITERATURE REVIEW

Technology changes everything, including crime. Traditional crimes, like fraud, identity theft and child pornography, can take new forms and become a new kind of problem that needs to be handled accordingly. On the other side, technology also introduced new ways to control crime, for example by facial recognition, GPS applications, radio frequency identity tags and many more. The new forms of crime and new ways to control crime changes criminology. With the introduction of new technologies, new challenges arise on how to process evidence, coming from the new technologies. Advancing technology like Internet of things (IoT), urges criminology to change in a direction in which IoT applications are being recognized in that they may contribute to crime prevention and crime investigation (Hegarty, Lamb & Attwood, 2014; Clarke, 2004).

IoT was first mentioned as a term more than 15 years ago in 1999 by Kevin Ashitton, an MIT RFID specialist, who said “The IoT will be constructed because RFID and other sensors are mounted on things in daily life” (Lee, 2014; Wortmann & Flutcher, 2015) While the term Internet of Things is now more and more broadly used, there is no common definition or understanding today of what the IoT actually encompasses (Wortmann & Fluchter, 2015). In the Dictionary of Information Science and Technology (2013) the Internet of Things is described as “a global architecture in which things (objects) are able to process, store and communicate information about themselves and other things” (Kunz et al. 2012)

The fact that IoT applications make it possible to process, store and communicate information about themselves and other things, make them objects of forensic interest (Hegarty, Lamb & Attwood, 2014). On the other hand the perceived effect of just having an application of IoT installed in an object in a certain environment, may scare of potential perpetrators (Haans & de Kort, 2012), but, in fact, may also lead to both spatial and temporal avoidance of an area (Neslon 1998).  

One application of IoT comes in the form of smart street lights. The concept of smart street lights is still very new and for this reason not many studies, with smart street lights as a topic and which define smart street lights as a concept on its own, have been performed. In a broader context, smart street lights can be seen as part of the concept of smart cities (Merlino et al. 2015; Naphade et al. 2011). A city is smart “when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and high quality of life, with a wise management of natural resources, through participatory governance” (Caragliu, Del Bo & Nijkamp, 2011). In this manner, smart street lights become part of a ‘system of systems’, all interconnected in an integrated (ICT) communication infrastructure to provide innovation in handling common city problems, like extensive energy use, (light) pollution, (the prevention of) crime, and economic and convenience issues (Merlino et al. 2015; Naphade et al. 2011). In this respect smart street lights are plain street lights with smart functionalities and features common in the Internet of Things applications. Depending on how the smart street light is exactly designed, the ability to process, store and communicate information may differ. The design of the smart street light could be modular, in which case new elements and functionalities could be added over time.

Although not much research has been done on defining the smart street lights, many (case) studies do include the effect of lighting on preventing crimes from happening (Farrington & Welsh, 2002; Haans & de Kort, 2012) and the introduction of CCTV systems to prevent crimes from happening (Lee, 2014; Germain, Douillet, & Dumoulin, 2012). Therefore there may also be a link between the application of (functionalities installed in) smart street lights and perceived safety and therefore on the use of smart street lights to (situational) crime prevention. For instance Haans & De Kort (2012) suggest there are three safety-related cues in perceived safety, which are: prospect (having an overview), escape (perceived escape possibilities), and refuge/concealment (perceived hiding places for offenders). Do smart street lights make use of one of these cues might also be interesting to investigate. That all depends on the design of the smart street light and which functionalities are implemented.

One functionality may be the implementation of an emergency communication device. This device provides a citizen, walking by, with a way to communicate with the emergency call centre might there be any trouble or unsafe feelings. A constant blue light that changes to strobe when a panic button is depressed may attract the attention of people in the surrounding and cause the criminal to flee (Smith, 1996). (Reference?????). Another functionality, that can be thought of to be related to the perception of safety, is a microphone, which can analyse the data for loud screaming noises and with this trigger turns on the implemented camera and alerts the emergency call centre. (Reference???)

Perceived safety opposite to absolute crime rates is worth investigating because of the paradox in which visible signs of security hardware may make people more fearful, sensing that high security must indicate high risk. The fact that this opposite effect may result in both spatial and temporal avoidance, which in itself may lead to the desertion of an area, rendering it more vulnerable to crime (Whattam, 2011), gives reason to investigate perceived safety more in depth.

To summarize the above: New technologies, like IoT applications, can be interesting to investigate in relation to perceived safety. One of these applications, e.d. the concept of smart street lights, is worth investigating further, because of the potential link between perceived safety and smart functionalities, installed in plain street lights, to prevent crime from happening. Which functionalities might be interesting to investigate further, are emergency communication devices and noise analyzing microphones. All of the above is in the light of preventing crime from happening in a certain situation. Therefore in the next part, situational crime prevention will be elaborated upon and all of the above will be accumulated into a theoretical framework that will be used to investigate specific cases to provide evidence and argumentation for the statements made here.

Situational crime prevention was introduced as a concept as early as 1983 by Robert Clarke. He defined situational crime prevention as “comprising measures directed at highly specific forms of crime that involve the management, design, or manipulation of the immediate environment in as systematic and permanent a way as possible so as to reduce the opportunities for crime and increase its risks as perceived by a wide range of offenders”.

From a broad perspective situational crime prevention includes designing out crime (Clarke, 1980), crime prevention through environmental design (CPTED) (Jeffrey 1971 in Clarke, 1995), Defensible space (Newman, 1972 in Clarke 1995) and problem-oriented policing (Clarke, 2015). These concepts and theories differ on details, but are all based on the same basic idea (Clarke, 2015). In this paper situational crime prevention is used.

Furthermore, Clarke defines the concept of situational crime prevention as based on a broad spectrum of theoretical traditional and modern theories of which environmental criminology, the rational choice perspective, and routine activities and lifestyle theories, are used to produce a model to tackle situational crime problems (Clarke, 1995):

 

The model helps to evaluate a certain situation in order to reduce the opportunities for potential crimes to happen. The model can be used to provide a basis on which a specific case can be researched, for instance the case of implementing smart street lights in high crime areas. In order to provide a sound analysis on smart street lights in relation to crime prevention, the functionalities must be analysed on a sound theoretical basis, which is provided below in the form of twelve techniques of situational crime prevention (Clarke 1995):

Technique: Description:

Target Hardening Obstruct the vandal or thief by physical barriers through the use of locks, safes, screens or reinforced materials

Access Control Refers to measures intended to exclude potential offenders from places such as offices, factories, and apartment buildings.

Deflecting offenders Provisioning of object that channels people’s behaviour in more acceptable directions, like public urinals and litter bins.

Controlling facilitators Take away a facilitator of crime, like guns or other weapons, but also identity-based control mechanisms.

Entry/Exit screening Differs from access control in that the purpose is less to exclude potential offenders than to increase the risk of detecting those who are not in conformity with entry requirements.

Formal surveillance Is provided by police, security guards, and store detectives, whose main function is to furnish a deterrent threat to potential offenders.

Surveillance by employees In addition to their role as employees, they can also perform a surveillance role

Natural surveillance Is being applied by people who just going about their own business and may or may not notice something occurring.

Target removal Replace the target of the crime with an alternative that is much less prone to be victimized, for instance replace glass with Plexiglas.

Identifying property Give an identity to a certain object to make it harder to steal.

Removing inducements For instance weapons have been found to induce aggressive responses in people. To remove them, lowers the change of a crime.

Rule setting Setting rules, to reduce crimes happening.

As mentioned above it is interesting to investigate the effect of smart street lights on perceived safety. For this part the three cues of perceived safety, described by Fisher & Nasar (1992) and used in the study of Haans & de Kort (2012) on the effects of street lights on perceived safety, is used: prospect (having an overview), escape (perceived escape possibilities), and refuge/concealment (perceived hiding places for offenders).

To summarize the above in a theoretical framework:

 

3. RESEARCH METHODOLOGY

In this project, the researcher are questioning the use of Internet of Things in police investigations in crime scenes. The police department needs answer to questions like the following ones. Would the management of information in crime scenes be improved thanks to the Internet of Things? According to Hegarty et al (2014), the Internet of Things is useful in the sense that they allow data and information to be stored, monitored and processed. They are everyday objects with sensors or other technology inside them (Lee, 2014; Wortmann & Flutcher, 2015). The use of these objects is fairly new as it was first mentioned in 1999 by Kevin Ashitton. The fact that this is a new concept will be beneficial for the authors because it will allow them to have more room to research the possible uses of it in the field of police investigation. The case that the authors received is thus based on the use of the Internet of Things within police investigation and prevention of crime. The case of smart streetlights has also been chosen to support the prevention of crime. As light has been proven to be useful in reducing crimes by Farigton & welshe (2002), the authors have chosen to work on smart streetlights that could detect future crimes and protect communities. The literature review done on crime prevention reveals that it was mentioned bu Robert Clarke in 1983 where he defined it as “measures directed at highly specific forms of crime […] to reduce opportunities for crime”. The authors decided to have the prevention of crime to be the focus of their research paired with the use of smart streetlights.

In order to obtain more insight on the work that policemen do and the advantages that technology could add to this work, this research paper will be done with the use of qualitative methodologies. Interviews will be conducted with professionals and an extensive literature review on the subjects related to our research will be done.

The authors had the chance to meet Ron Boelsma, the contact within the police department, who was helpful in giving some ideas to focus the research on a more specific topic. The interviews of our research will be conducted with him and some of his co-workers that work almost exclusively on the subject of the Internet of things. The interviews will be lead in a semi-structured manner; this will allow the authors to have answers to their questions without blocking the speech flow and thus gain information that had not been thought about in the first place.

The general themes of the interview will be as followed. First of all, the authors will need to have more information about the general practice of crime scene investigations. Then it will be crucial to have a broader knowledge on the technology used in the police. As the research focuses on the prevention of crimes, the questions will be guided toward how the Police uses technology to prevent crimes. Then the authors will present the idea that they had about a smart streetlight and its use. The questions related to that subject will be towards knowing if it has already been in use and its general goals. It will be interesting to know the point of view of the interviewee, as he or she is a specialist in Internet of Things within the police department. This interview will guide the researchers in the end of the their research and will help them draw conclusions and/or recommendations.

As stated above and in order to prepare those interviews, an extensive literature review will be done. This literature review will allow the team to have a clear view of what has already been written on the subject as well as find a gap in the theories.

4. REFERENCES

Atkins, S., Husain, S., & Storey, A. (1991). The Influence of Street Lighting on Crime and Fear of Crime. United Kingdom: Home Office Crime Prevention Unit.

Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of urban technology, 18(2), 65-82.

Clarke, R. V. (1995). Situational crime prevention. Crime and justice, 91-150.

Clarke, R. V. (2004). Technology, criminology and crime science. European Journal on Criminal Policy and Research, 10(1), 55-63.

Farrington, D., & Welsh, B. (2002). Improved street lighting and crime prevention. Justice Quarterly, 19, 313-342.

Fisher, B. S., & Nasar, J. L. (1992). Fear of crime in relation to three exterior site features prospect, refuge, and escape. Environment and Behavior, 24(1), 35-65.

Germain, S., Douillet, A. C., & Dumoulin, L. (2012). The legitimization of CCTV as a policy tool genesis and stabilization of a socio-technical device in three French cities. British journal of criminology, 52(2), 294-308.

Haans, A., & de Kort, Y. A. (2012). Light distribution in dynamic street lighting: Two experimental studies on its effects on perceived safety, prospect, concealment, and escape. Journal of Environmental Psychology, 32(4), 342-352.

Hegarty, R. C., Lamb, D. J., & Attwood, A. (2014). Digital Evidence Challenges in the Internet of Things. In Proceedings of the Tenth International Network Conference (INC 2014) (p. 163). Lulu. com.

International Energy Agency. (2006). Light’s Labour’s Lost: Policies for Energy-efficient Lighting. France: IEA.

Lee, H. J. (2015). A Study on Social Issue Solutions Using the “Internet of Things” (Focusing on a Crime Prevention Camera System). International Journal of Distributed Sensor Networks, 2015, 1-8.

Merlino, G., Bruneo, D., Distefano, S., Longo, F., Puliafito, A., & Al-Anbuky, A. (2015). A smart city lighting case study on an openstack-powered infrastructure. Sensors, 15(7), 16314-16335.

Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., & Morris, R. (2011). Smarter cities and their innovation challenges. Computer, 44(6), 32-39.

Natarajan, M., Clarke, R., Carcach, C., Ponce, C., de Sanfeliú, M. B., Polanco, D. E., ... & Shi, M. (2015). Situational prevention and public transport crime in El Salvador. Crime Science, 4(1), 1-15.

Pease, K. (1999). A Review of Street Lighting Evaluations: Crime Reduction Effects,. Surveillance of Public Space: CCTV, Street Lighting and Crime Prevention, 10, 47-76.

Ramsay, M. (1991). The Effect of Better Street Lighting on Crime and Fear: A Review. United Kingdom: Home Office Crime Prevention Unit.

Ruoklainen, T., & Kutvonen, L. (2005). Interoperability in Service-Based Communities. (C. Bussler, & A. Haller, Eds.) Business Process Management Workshops: BPM 2005, 3812, 317–328.

Smith, M. S. (1996). Crime prevention through environmental design in parking facilities. US Department of Justice, Office of Justice Programs, National Institute of Justice.

Vermesan, O., & Fries, P. (2013). Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. Aalborg, Denmark: River Publishers.

Virginia Crime Prevention Association. (1993). Crime Prevention Standards.

Wanvik, P. O. (2009, January). Effects of Road Lighting: An Analysis Based on Dutch Accident Statistics 1987–2006. Accident Analysis & Prevention, 41, 123-128.

Whattam, S. (2011). Situational crime prevention: Modern society’s trojan horse. Internet Journal of Criminology, 1-52.

Wortmann, A. P. D. F., & Flüchter, K. (2015). Internet of Things. Business & Information Systems Engineering, 57(3), 221-224.

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