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Smart City Development: ICT Innovation for Urban Sustainability

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Accepted manuscript. Mora, L., Deakin, M., Aina, Y. A., & Appio, F. P. (2019). Smart City Development: ICT
Innovation for Urban Sustainability. In W. Leal Filho, A. M. Azul, L. Brandli, P. G. Özuyar, & T. Wall (Eds.),
Encyclopedia of the UN Sustainable Development Goals: Sustainable Cities and Communities. Cham: Springer.
Smart City Development: ICT Innovation for Urban Sustainability
Luca Moraa*, Mark Deakinb, Yusuf A. Ainac and Francesco P. Appiod
a Edinburgh Napier University, The Business School, Edinburgh, UK
b Edinburgh Napier University, School of Engineering and the Built Environment, Edinburgh, UK
c Yanbu Industrial College, Department of Geomatics Engineering Technology, Yanbu, Saudi Arabia
d Léonard de Vinci Pôle Universitaire, School of Management, Paris, France
* Corresponding author: L.Mora@napier.ac.uk
1. Definition
Smart city: a city
1
in which issues limiting sustainable urban development are tackled by
means of ICT-related solutions.
2. Introduction
Boosting sustainable urban development by helping cities across the world to adopt a smart
city approach is one of the main commitments of the United Nations (UN). This commitment
is made explicit in the New Urban Agenda, which gives particular attention to the potential
contribution of smart city development to urban sustainability and suggests making
determined efforts to meet urban challenges by deploying sustainability-oriented ICT
innovations is key to: (1) attain environmentally friendly, resource efficient, safe, inclusive and
accessible urban environments; (2) sustain an economic growth based on the principles of
environmental sustainability and inclusive prosperity; and (3) provide equal access for all to
public goods and high-quality services (United Nations 2017a).
The number of cities working on strategies to enable smart city development and take
advantage of the available technological advancements in the field of ICTs has been growing
over the years, along with the production of literature on smart cities (Mora et al. 2017; 2018b).
This literature offers a large number of practical examples to look at. Angelidou (2017), Bolici
and Mora (2015), Mora and Bolici (206; 2017), Leydesdorff and Deakin (2011) and Mora et
al. (2018d; 2018c), for example, investigate how smart city development is approached in
Europe by reporting on the cases of Amsterdam, Barcelona, Edinburgh, London, PlanIT
Valley, Stockholm, Thessaloniki, Vienna and Helsinki. Vanolo (2014) and Cowley et al.
(2018)’s multiple case study analyses further extend this overview by focusing attention on
groups of cities located in Italy and the United Kingdom respectively.
Significant efforts for activating smart city development have also been found in Asia and
America, more precisely in the cities of San Francisco, Seoul, Dholera, Songdo, Philadelphia,
Rio de Janeiro, Seattle, Quebec City, Mexico City, Tokyo, Yokohama, Osaka, Kyoto and Kuala
Lumpur (Alawadhi et al. 2012; Alvin Yau et al. 2016; Lee et al. 2014; Fietkiewicz and Stock
2015; Schreiner 2016; Shwayri 2013; Wiig 2015a; 2015b). Insights into Asia’s approach to
smart city development are also provided by both Aina (2017) and Praharaj et al. (2017). The
former compares eight Saudi Arabian cities, while the latter addresses the planning and
governance mechanisms that the national government of India has put in place to meet its
expectation of transforming 100 urban areas across the country into smart cities.
On the one hand, this literature provides evidence of cities’ strong interest in smart city
development. On the other hand, it serves to expose the limitations of smart city research,
which is still trying to: (1) make it clear what needs to be done in order to enable smart city
development; and (2) explain what benefits and potential contribution a smart-city approach
to sustainable urban development can produce. This chapter contributes to fill this knowledge
gap by reporting on a series of smart city projects which are instrumental in showcasing how
ICT solutions can be deployed to address some of the most pressing urban challenges.
These smart city initiatives are deployed as descriptive case studies and selected for their
ability to draw upon ICT solutions to support the United Nations in meeting the transformative
commitments recently set out in the New Urban Agenda. These commitments include the
followings: (1) Facilitating the sustainable management of natural resources; (2) Ensuring that
all citizens have equal access to basic services and infrastructures; (3) Improving food
security; and (4) Promoting environmentally sound waste management and reducing waste
1
In this definition and this chapter, the term ‘city’ is used to refer to any type of urban areas, irrespective
of their population size, and the acronym ICT stands for Information and Communication Technology.
generation (United Nations 2017a).
The third section of this chapter gives a comprehensive account of the smart city initiatives
which have been selected, the urban challenges they tackle and the benefits they have
produced. This section is anticipated by an overview of the emerging technological trends
which have been paving the way for a smart-city approach to sustainable urban development.
The chapter concludes with a critical reflection upon the current debate over smart city
development, which aims to draw attention to some of the key knowledge gaps and questions
that smart city research is asked to focus attention on.
3. Cities in the Digital Era: Emerging Technological Trends
Thanks to the opportunities for professional and personal growth they offer, as well as
economic, cultural and social stimuli, cities have become the primary life environment. This
preference now results in a situation whereby the majority of the world’s population has
progressively moved from rural sites to urban areas. This global trend has been captured by
the United Nations (2014) demographic change estimates, which show how the powerful
attraction exhibited by urban areas has been progressively growing since 1950, leading to a
mass relocation.
As a result of this demographic transition, in 2007, the population living in urban areas
outnumbered the rural areasinhabitants. This is an epoch-making change for the history of
human civilization, which is confirmed by the statistics published in the UN World Population
Prospects: (1) in 1950, more than two-thirds of people lived in rural settlements and less than
one-third in urban environments; (2) in 2007, for the first time in history, more than 50% of the
human population was living in urban settlements; and (3) in 2015, the growth trend continued,
as 54% of the world’s population was urban (United Nations 2014; 2017b).
However, this figure is likely to change significantly over the coming years. The data suggests
the world population is projected to grow from 7.4 billion in 2015, to a minimum of 9.6 and a
maximum of 13.2 billion in 2100, increasing therefore between 30% and 78%, and cities of the
developing world are expected to make up 80% of this growth (United Nations 2017b). In
Europe, for example, about 75% of the population belonging to the European Union’s Member
States is already living in urban areas (European Commission 2010; United Nations 2014).
The dramatic growth in the urban share of world population appears to be unstoppable and
has placed considerable pressure on urban environments, highlighting their inability to support
a sustainable urban development. This situation has generated inefficiency levels which are
increasing hand-in-hand with the urban population and calls for new approaches to urban
sustainability.
The evolutionary process of cities and society has always been strongly influenced by the
continuous advancement of technology. The technological innovations of the Neolithic era
favored the initial transition from the living places of hunter-gatherers to the first permanent
settlements in agricultural villages. During the 19th and 20th centuries, urbanized areas around
the world were profoundly changed by the new transport and communication technologies
introduced by the industrial revolution. This led to a radical transformation of the urban
landscape, which was greatly influenced by technological innovation, with the belief that it
would lead to growth, development, well-being and a better quality of life (Benevolo 1993).
As in the past, a new period of transition is now in process. New ICT devices and
infrastructures have been introduced by the digital revolution and have entered the daily life
of billions of people. These technologies are pervading and absorbing a wide range of
functions in urbanized areas and have triggered radical transformations in the urban
dynamics. According to Mitchell (1995), the modern society is witnessing a rapid and silent
revolution, which has opened up a new opportunity for supporting urban development: using
information technology to solve the spatial, economic, environmental and social issues
affecting urban environments. Mitchell (1995) suggests the main challenge this revolution
poses is to learn how to take advantage of the emerging technological trends and exploit the
possibilities which they offer to support an urban development that is sustainable. Some of
these trends and their connection are discussed in the following paragraphs.
3.1. Faster, cheaper, smaller
The fast-paced advancements in the field of information and communication technology have
greatly accelerated progress and innovation and are facilitating the development of new
products and services. Yet, it is also supporting the improvement of existing ones. The
evolution of the auto-mobility industry, whose analysis has been implemented by the New
York and Copenhagen-based architecture company Bjarke Ingels Group (BIG), clearly
exemplifies this trend. In developing their proposal for the annual Audi Urban Future Award
competition, BIG has focused attention on personal transport solutions and mapped the major
technological breakthroughs which have let the manufacturing process of cars progress (see
Figure 1). This mapping exercise runs across a period of 11,500 years, from the invention of
the wheel to now, and shows that: (1) technological advances in the car industry have been
occurring continuously and within exponentially shorter intervals; (2) these advances tend to
cluster in the last decade; and (3) the evolutionary process has been favored by the digital
revolution and the fast-moving technological development in the field of ICT.
Figure 1. The technological evolution of the car. Data source: Jordana (2010)
The ICT sector has been growing continuously since the production of the first planar transistor
and integrated circuit, which has revealed the potential for extending the cost and operating
benefits of transistors to every mass-produced electronic circuit, including the
microprocessors that control computer operations” (Schaller 1997: 53). The transistor is the
driving force of this dynamic sector, which produces technological devices making it possible
for humans to manage complex tasks and boost their reasoning abilities. Devices which are
subject to constant evolutionary change, because their computing power doubles
approximately every 18 months. Gordon Moore, the inventor of the integrated circuit and co-
founder of Intel Corporation, was the first to pick up on this trend in the 1960s. With his
research, Moore estimated that manufacturers would have doubled the number of transistors
per integrated circuit every 18 months. As a result of this upgrading process, the circuits would
have run twice as fast at regular intervals, providing an augmented computational power. This
theory is known as Moore’s Law and has marked a turning point in the semiconductor industry,
leading to the widespread diffusion of information technology around the world. Also, this law
is a now a shorthand explanation for the rapid technological change underlying the digital
revolution (Moore 1965; Schaller 1997).
However, while the processing ability of digital devices with computational power has been
dramatically increasing, their relative costs have been progressively decreasing. This trend
has made electronic devices more affordable and pervasive and has been further investigated
by Kurzweil, Director of Engineering at Google. Comparing the speed in instructions per
second per unit cost of 49 famous calculators and computers spanning the entire 20th century,
Kurzweil (2004: 389) demonstrates that the “computer speed (per unit cost) doubled every
three years between 1910 and 1950, doubled every two years between 1950 and 1966, and
is now doubling every year”.
Technological development is making information-processing devices faster, due to an
increasing computational power, and cheaper to produce. In addition, it is speeding up the
reduction of both the size of such devices and their selling prices. These last two trends are
discussed in research by Eyre and Bier (1998), Mitchell (1995; 1999) and Mack (2011), and
mobility report published by Ericsson in June 2017, which suggests the possibility to produce
smaller, faster and cheaper electronic devices has enabled new markets to grow and pushed
the mass diffusion of modern computing technology. For example, greater device affordability
is driving the sharp increase in smartphone adoptions. The data released by Ericsson shows
that the global number of smartphone subscriptions has been growing significantly since 2010,
moving from 0.5 billion devices to the 3.9 of 2016, and is expected to reach around 6.8 by the
end of 2022 (Ericsson 2013; 2014; 2015; 2016; 2017).
3.2. Intangible
Kevin Lynch, American urban planner and Emeritus Professor of City Planning of the
Massachusetts Institute of Technology (MIT), offered a significant contribution to large-scale
urban design theory. “The Image of the City” is by far one of the most greatly admired and
impactful publications that he produced. Despite being published half a century ago, the
contents of this book are still relevant because they provide a valid theory able to explain: (1)
how large groups of individuals formulate the environmental image of the city in which they
live; (2) what are the main elements of the urban environment which this common mental
picture is composed of; and (3) what city planners can do in order to make such environmental
images more legible, more distinctive and able to offer emotional security.
Such a theory resulted from a five-year multi case-study analysis of three US cities, i.e.
Boston, Jersey City and Los Angeles. Groups of inhabitants were selected for each case study
and interviewed to capture how the cities’ appearance and visual form were perceived. The
individual mental images collected during the interview process were then correlated in order
to examine their structure and identify common trends. As Lynch explains, this comparative
analysis made it possible to reveal a common mental framework suggesting that the image of
a city can be broken down into five type-element classes of physical objects which make up
the complexity of the urban landscape. These elements are: (1) Paths: channels organizing
the urban mobility and along which people move throughout the city, such as streets,
walkways and railroads; (2) Edges: elements that close one part of the city off from others,
like walls; (3) Districts: medium-to-large sections of the city in which common physically or
culturally-related features are perceived; (4) Nodes: polarizing spots which can be either
junctions of paths or concentrations gathering people together, like squares; and (5)
Landmarks: physical objects which are significant and easily identifiable and help observers
to orient. Obviously, same elements can take different roles, depending on the observer’s
point of view (Lynch 1960).
The deductive analysis conducted by Lynch made it possible to explain what basic elements
compose the images of cities in their usersmind. The theory resulting from such an analysis
has offered innovative insights into the collective perception of the urban landscape and
suggests cities can be read as a series of interrelated physical elements. However, when
considering cities in the information age, the attention should not be focused only on their
physical assets. The digital revolution has augmented the complexity of cities by leading to
the growth of their intangible counterparts, which relates to the immateriality of urban
environments and cannot be perceived visually, but nevertheless affect how they function and
contribute to the transformation of the physical components.
Figure 2. The image of the virtual and physical cities
As Mitchell (1995; 2003) and Castells (1996) explain, each urban environment has a virtual
counterpart, which is: (1) made of data streams; (2) located in the immateriality of the Internet;
and (3) populated by a fast-growing network society composed of people who are able to enter
any virtual environments by connecting to the Internet with their technological devices.
Physical cities and their virtual counterparts are completely interrelated and influence each
other functioning: “at one end of the spectrum are completely traditional, place-based
communities composed entirely of physical spaces, organized around traditional types of
public spaces, and held together by physical circulation through streets and transportation
networks. At the other end of the spectrum are fully virtual communities” (Mitchell 2004: 127).
The Internet is the global network connecting physical and virtual cities and represents one of
the most influential inventions in human history. Its origins date back to the 1990s and its
usage was extended to a mass audience through the creation of the World Wide Web, an
Internet-based hypermedia initiative for global information sharing which made it easier for
everybody to access the online network (Ryan 2010). The data collected by the International
Telecommunication Union (ITU) suggests the numbers of Internet users have increased over
the years in both developed and developing countries. Over a time period of 12 years, with an
average annual growth of about 11%, the 1.02 billion individual users of 2005 have become
almost 3.4 billion in 2016. According to ITU’s estimates, this means that nearly 47% of the
world population is now connected to the Internet and is spending time in the intangible
counterparts of physical cities (ITU 2014; 2016; ITU and UNESCO 2016). This global trend
demonstrates that the network society is a fast-growing entity.
3.3. Everywhere
When Guglielmo Marconi invented the telegraph, he marked the beginning of the wireless
world. His invention, for the first time in history, made it possible to send signals between two
different locations without having any physical connections. In discussing Marconi’s wireless
telegraphy system and the effects this invention has produced, Mitchell (2003) introduces
three additional global trends which have been radically changing urban environments and
society. These changes are captured in the following developments: (1) mobility; (2) ubiquity;
and (3) Internet of Things.
The development of wireless technologies, such as Bluetooth, Wi-Fi, cellular data networks
and Radio-Frequency Identification (RFID) technology, has made it possible for billions of
electronic devices to connect to the Internet while on the move. These objects have been
freed from the constraints of a fixed location and their diffusion has continued to grow over
time. While the urban population was outnumbering the population living in rural areas, the
number of electronic devices connected to the Internet was exceeding the number of
inhabitants on the planet.
The Internet Business Solutions Group (IBSG) of Cisco Systems captured this second change
in 2011. Their statistics suggest this event took place between 2003 and 2010 and forecast
the gap between the number of people and Internet-connected objects will continue to rise.
Looking to the next future, Cisco IBSG predicts there will be 50 billion devices connected to
the Internet by 2020 and the ratio of people to devices will be 1 to 7 (Evans 2011). This data
describes an exponential increase, which is also forecasted by Gartner, ABI Research and
Ericsson. These three companies expect the same growth trend: according to ABI Research
(2013), by 2020 there will be 30 billion objects connected to the Internet, while Gartner (2013)
and Ericsson (2015) predicts an increase to 26 billion.
This growth trend has been facilitated by the constant reduction of technological devices’ sizes
and their improved affordability, as well as the preference expressed by Internet users for
mobile networks and devices. Evidence of such a preference for the mobile option is provided
by ITU (see Figure 3). During the last 13 years, the mobile-broadband subscriptions have
increased much faster than fixed-broadband subscriptions and 81% of the active broadband
subscriptions were mobile in 2016.
Figure 3. World’s number of broadband subscriptions by type
An ecosystem of increasingly evolved technological tools is expanding and the growth in the
number of Internet-connected devices is accelerating. Mobile broadband users are expected
to connect 15 billion smartphones, tablets, laptops and PCs to the Internet by the end of 2020.
However, considering an expected growth scenario between of 26 and 30 billion connected
objects, this only accounts for about 50%. The remaining share relates to networks of sensors
which are embedded in the built environment and populate the Internet with a huge amount
of real-time data (Ericsson 2015). As Mitchell (2003: 3) pointed out, with the advent of wireless
technologies, the thing-to-thing relationships have changed. Many things that might benefit
from network connections never got them because it was just too difficult to run the wires. By
the dawn of the twenty-first century, though, inexpensive, ubiquitous wireless connections
were linking whole new classes of things into networks. Very tiny things, very numerous things,
very isolated things, highly mobile things, things deeply embedded in other things, and things
that were jammed into tight and inaccessible places”. As a result, the Internet begins to be
populated by both human beings and a large number of networks composed of physical
objects, which are able to communicate with and sense the external environment.
This wave of Internet-connected devices has enabled the Internet of Things (IoT), which has
become the paradigm at the interplay between ICT and sustainable urban development. The
Internet of Things is activated by the pervasive presence of a large variety of sensor-type
technologies able to link the physical environment, in which they are embedded, to the virtual
world. These objects can automatically communicate to computers and each other, and
provide services and applications characterized by a high degree of autonomous data capture,
data transfer, network connectivity and interoperability. The Internet of Things is expected to
generate the most disruptive technological revolution since the advent of the World Wide Web
and generate positive technical, socio-economic and political consequences. The wide-scale
adoption and deployment of IoT solutions have already enabled a new generation of advanced
digital services which can benefit any policy domain, such as agriculture, transport, health,
utilities, industry and education (Atzori et al. 2010; Holler et al. 2014; Ts el en t is et al . 2009).
3.4. Volume, velocity and variety
The increasingly widespread deployment of electronic devices with Internet-connection
capability is one of the major factors behind the expansion of the network society. With these
tools, anyone can access the wide range of information items and services stored into the
Internet and contribute to the creation and diffusion of new ones, fueling the continued growth
of data streams and digital applications that the global network has to offer.
The Internet has become an extraordinarily data-rich environment, which is rapidly expanding.
The estimates released by the International Data Corporation (IDC) show that the amount of
digital information giving form to the Internet was 281 Exabytes in 2007, approximately 10
times bigger than 2006. But also 7 times smaller than 2011, when the amount of information
surpassed 1,843 Exabytes (Gantz et al. 2008; Gantz and Reinsel 2011). By considering that
an Exabyte is equivalent to 4,803 billion books of 200 pages each, the 2011 amount of digital
information stored in the Internet’s digital universe was about 9,000 trillion books (Shark 2015).
However, this growth trend seems to be exponential and unstoppable because, as of 2012,
“about 2.5 Exabytes of data are created each day, and that number is doubling every 40
months or so. More data is produced every second than were stored in the entire Internet just
20 years ago” (McAfee and Brynjolfsson 2012: 62).
IDC has also estimated that the amount of information that Internet users create themselves
is far less than the amount of information being created about them by an increasing number
of objects with Internet-connection capacity (Gantz and Reinsel 2011). Physical objects and
people are therefore members of a single, large community and are both actively involved in
the production process of the huge stream of data shaping the digital universe. GPS signals,
data describing either traffic conditions or the environmental quality of buildings, digital
images, audio and video files, purchase transaction records from online shopping and posts
on social networks are just some of the types of data produced every day throughout the world.
For example, four billion hours of video are viewed on YouTube each month, 400 million tweets
are sent on a daily basis by around 200 million Twitter users, and 30 billion posts are shared
on Facebook every month by over 600 million users (IBM Corporation 2012; Taylor 2013).
This explosive increase in data availability has led to the big data era, a term which is mainly
used to describe enormous datasets composed of large masses of unstructured and
heterogeneous data. In the big data era, the volume of data is growing exponentially and
originates from a huge variety of sources. In addition, the production is vastly greater than the
past, as the speed required for the analysis. Therefore, it is possible to state that volume,
variety and velocity are the main factors characterizing contemporary knowledge production
processes.
These big data-related trends open up the challenge of designing and deploying new
methodological approaches and technologies which make it possible to transform large
volumes of raw data into knowledge by enabling high-speed capture, aggregation, processing
and discovery processes (Boyd and Crawford 2012; Chen et al. 2014; McAfee and
Brynjolfsson 2012).
4. Smart Stories: Catalyzing Sustainable Urban Development with ICT solutions
This chapter’s section showcases how cities can take advantage of these emerging
technological trends for sustainable urban development purposes and use ICT solutions to
address the most pressing societal issues. This aim is achieved by reporting on a series of
smart city initiatives which are deployed as descriptive case studies. The initiatives have been
grouped by looking at the main United Nations’ urban transformative commitment that they
help achieve.
4.1. Facilitating the sustainable management of natural resources
A world of digital contents is growing in an ever-reduced period of time and the question that
researchers are trying to answer is: how do we make sense of all that data and deploy it to
improve the functioning of cities’ infrastructures? One of the most innovative way cities are
employing big data analytics is to understand collective behaviors and use the knowledge
produced for raising collective awareness in relation to specific issues affecting the quality of
urban environments and support informed policy-making.
The HubCab project is emblematic of the benefits that big data analytics can produce,
especially in facilitating the sustainable management of natural resources. The aim of this
project was to reduce air pollution in New York City by: (1) revealing the positive impact of
large-scale taxi sharing on environmental sustainability and service cost reduction; (2)
stimulating behavioral changes in personal mobility patterns related which involve taxi trips;
and (3) informing local policy-makers about the feasibility and effectiveness of activating a
new taxi-sharing system or program.
HubCab (www.hubcab.org) results from the collaboration between the Massachusetts Institute
of Technology’s Senseable City Lab, the German automobile manufacturer Audi and the
multinational conglomerate company General Electric. Together, these three partners mapped
an entire year of taxi trips originating and ending in Manhattan. This mapping exercise pulled
together the 172 million trips that the taxicabs officially registered in New York City completed
in 2011. The data describing each trip was captured by the Global Positioning System (GPS)
trackers installed into the taxicabs. This included nearly 14,000 vehicles. The data obtained
from the GPS trackers was included in a single and massive dataset in which every trip was
connected to: the vehicle ID; the GPS coordinates of both the pick-up and drop-off locations;
and the travel time.
After being collected, the real-time data streams were processed by using a graph-based
mathematical model. The result is an interactive visualization map which groups together a
whole year of taxi movements, along with the taxi pick-ups and drop-offs, which are
represented as yellow and blue dots respectively. In addition, the thickness of street segments
is proportional to the taxi activity. By selecting any destination and pickup address on the
HubCab map, the user can visualize the flows connecting them and is immediately informed
about the collective travel patterns: (1) the number of trips which are made to connect the two
locations at different times; and (2) the potential benefits generated by shared taxi services,
which are expressed in savings for the passengers, number of untraveled miles and quantity
of carbon dioxide not emitted into the atmosphere (Badger 2014; Santi et al. 2014; Stertz
2014; Szell and Groß 2014)
HubCab demonstrates that the ubiquitous integration of wireless sensor networks in the urban
environment, which are driving the big data movement and expanding the Internet of Things,
is making it possible to obtain real-time representations of complex urban dynamics. For
example, sensor networks are generating real-time information flows which are helping to:
identify free parking areas (Jeffrey et al. 2012); monitor and evaluate road condition and
facilitate the planning of maintenance interventions (Collotta et al. 2012); detect emergency
situations and catastrophic events, such as forest fires, floods and earthquakes, and improve
response times (Bouabdellah et al. 2013; Castillo-Effen et al. 2004; Díaz-Ramírez et al. 2012;
Faulkner et al. 2011; Fischer et al. 2013); monitor the structural conditions of buildings and
bridges (Plessi et al. 2007); control important farming operations and make them more efficient
(Pierce and Elliott 2008); optimize residential energy management (Erol-Kantarci and Mouftah
2011); make healthcare more cost-effective and accessible and provide patiantes with
appropriate health recommendations remotely (Kulkarni and Sathe 2014); and measuring air
quality in indoor and outdoor environments (Hejlová and Voženílek 2013), an activity that
citizens can now carry out themselves.
The diffusion of low-cost and easy to use home-based sensing kits is allowing citizens to:
collect local environmental data in real-time; become aware of the quality of the urban
environments in which they live; and share this knowledge by means of community-led
crowdsourced maps, which could be used to update and improve professional sensing
networks (Saunders and Baeck 2015). The Smart Citizen Kit (https://smartcitizen.me) is an
example of these emerging home-based sensing kits supporting participatory collection and
sharing processes of environmental data.
Recognizing the potential benefits that the large-scale diffusion of this sensing device can
produce, Manchester and Amsterdam have already experimented with building communities
of adopters around the Smart Citizen Kit. This tool is the output of a project launched in 2012
by Fab Lab Barcelona, the Institute for Advanced Architecture of Catalonia and Geoteo. The
Smart Citizen Kit is a tiny piece of hardware composed of a set of environmental sensors, a
data-processing board, a battery and a Wi-Fi antenna. Its sensors measure air composition,
temperature, humidity, light intensity, solar radiation, wavelength exposure and sound levels.
This sensing activity generates a stream of real-time environmental data which is sent to the
Smart Citizen online platform, i.e. an interactive map in which all the kits are georeferenced
and all the data they produce is made open. Everybody can access the map and visualize the
environmental data it captures. The map makes it possible to either understand the
environmental quality of a specific location or compare different geographic areas (Balestrini
et al. 2014; Diez and Posada 2013; Saunders and Baeck 2015).
4.2. Ensuring equal access to basic services and infrastructures
Online platforms pooling crowd-generated data are not only helping cities to monitor their
environmental quality but are also making them more accessible. For example, “pervasive
computing technology can enhance quality of life for those with disabilities by providing access
to timely information and helping them to navigate their environment independently” (Rector
2018: 9).
In 2010, the non-profit organization Social Heroes launched Wheelmap
(https://wheelmap.org), a worldwide map for wheelchair accessible places identification.
Wheelmap is generated by means of a Wikipedia-approach, i.e. users provide their local
knowledge to other users in relation to a location’s accessibility. Anyone can visualize the map
using both desktop and mobile devices and contribute to its further development by: marking
public places around the world; rating their level of accessibility for individuals with mobility
impairments; changing details in the information related to any places; and uploading photos
of their entrances. The map currently counts more than 840,000 public places, which are
grouped in 130 different types. Overall, this platform makes it easier to understand the level
of accessibility offered by a location and make private and public owners of wheelchair-
inaccessible public places aware of the issue.
The accessibility application called Project Sidewalk (http://sidewalk.umiacs.umd.edu), which
has been developed by University of Washington and University of Maryland, shares the same
ambition as Wheelmap. Its aim is to transform how accessibility information is produced,
collected and visualized by exploiting volunteer contributions. However, in this case, the
attention is focused on sidewalks. Project Sidewalk makes it possible to virtually explore the
streets of a city by using Google’s Street View images and: (1) identify and label sidewalks’
accessibility issues, such as missing curb ramps, obstacles or damaged surface that needs
to be repaired; and (2) assess the gravity of each issue. The information produced is expected
to improve city planning processes by making governmental authorities aware of the issues
limiting wheelchair users’ mobility within the city. The application was successfully piloted in
Washington D.C. in September 2016 (Saha et al. 2017).
4.3. Improving food security
The global food system is one of the largest consumers of the world’s land, water and energy
and it is perhaps the most wasteful, with around one-third of the food globally produced being
lost or wasted along the food supply chain. All of this suggests the current food system is
being mismanaged and food security is under threat. Recent reports from the United Nations
confirm this threat by focusing attention on the effects the global food system is having on
health and well-being. As they point out, while food is abundant, malnutrition is on the rise all
over the world: 12.5% of the world’s population is undernourished; 26% of the world’s children
are stunted; and 2 billion people suffer from micronutrient deficiencies (FAO 2013; FAO et al.
2015a; 2015b; Pinstrup-Andersen et al. 2014; WHO 2016).
Technology can help end hunger and tackle food waste and FoodCloud (https://food.cloud) is
an interesting example of ICT solution showing how this is happening. FoodCloud is an online
food-sharing service operating in Ireland and the United Kingdom, which let charities and
community organisations know when a retail partner has surplus food available for donation.
Retail partners currently adopting FoodCloud include some of the largest chains of Irish and
British supermarkets, like Tesco, Aldi, Lidl and Waitrose. When the retail partners’ stores have
food that cannot be sold, they can upload a description of the food they want to donate onto
the FoodCloud warehousing system by using either in-store scanners or the FoodCloud
mobile application. As soon as the donation request is completed, it reaches the local charities
and community organisations partnering with FoodCloud, which are instantly delivered a
notification from the app. The notification asks to confirm the acceptance of the food and
proceed with the collection process. Overall, FoodCloud can count on a very large network of
users composed of more than 11,500 organizations, of which about 7,500 are charities and
community groups (Fox 2016; Holmes 2018).
The food-sharing app Olio (https://olioex.com) works similarly to FoodCloud. It provides
private consumers and local stores with the possibility to recover value from perishable food
which is about to be wasted by combining mobile technology, circular economy and collective
efforts. Anyone with surplus food only needs to: (1) upload both a picture and a description of
the food upon Olio; (2) select the time and location for the pick-up; and (3) wait for a notification
reporting on someone’s interest in having the product. According to the statistics published in
Olio’s official website, this digital service is active in 41 countries and has more than 400,000
registered users, who have helped to save an amount of food equivalent to nearly 630,000
food items from being wasted. However, despite being developed to reduce the global food
waste, Olio has also become a powerful tool helping people who cannot afford to feed
themselves to access free food products (Clay 2016; Harris 2017; Lei Win 2017; McEachran
2016).
4.4. Promoting environmentally sound waste management and reducing waste
generation
Information technology and circular economy are also combined by the United Nations and
Baidu to improve China’s electronic waste management systems. China’s landfills are storing
about 70% of the world's annual 500 million tons of e-waste and, to help the country save
resources and reduce the pollution this issue generates, Baidu partnered with the United
Nations to develop a mobile app for supporting e-waste recycling. The result of this
collaboration is the Baidu Recycle smartphone application, which was launched in August
2014.
The United Nations’ estimates show that Baidu Recycle is currently available in 22 cities
across China and has led to the safe disposal of 5,900 electronic items per month on average.
This app seeks to simplify the recycling process and effectively connect consumers, qualified
dismantling companies and manufactures. After selecting the type of appliance to recycle, the
user can upload a picture of the obsolete electronic devices and Baidu Recycle price it, by
estimating its value. The user is then asked to fill a pick-up request with its contact details,
which the application automatically forwards to the nearby legally certified e-waste disposal
companies. Finally, the companies interested in proceeding with the safe disposal of the
device contact the user and arrange a door-to-door pick-up appointment. The Baidu Recycle
app, therefore, help streamline the recycling process and cut down on any informal recycling
stations (UNDP 2015; 2016a; 2016b).
iRecycle (https://earth911.com/irecycle) is an additional example of free mobile application
aiming at improving the recycling process. However, unlike Baidu Recycle, it focuses on any
type of waste, not only on obsolete electronic devices. This application offers easy access to
one of North America's most extensive databases of plant locations in which private
consumers and commercial organizations can recycle their waste. The database is composed
of over 100,000 recycling venues, which are georeferenced on an interactive map. By clicking
on a single venue, the following information is provided: the full address of the location;
opening hours; and which types of materials are accepted. Overall, the venues included in the
database make it possible to recycle more than 350 materials.
5. Conclusions
Despite the presence of a fast-growing body of literature investigating smart city development
(Mora et al. 2017; Mora and Deakin 2019), the knowledge produced to date falls short of
providing the strategic, organizational and technological insights that cities need in order to
embrace an ICT-driven approach to urban sustainability. For the ambiguity that surrounds
smart cities still leaves many gaps in what is understood about these developments and the
way in which they should be managed (Kitchin 2015; Colding and Barthel 2017; Yigitcanlar
and Kamruzzaman 2018).
Evidence of this ambiguity is provided by recent studies showing the existence of several
smart city development paths, which overlap one another and that generate uncertainty on
how to deal with the ICT-driven approach to urban sustainability they should enable. These
paths uncover strategic principles that are divergent in nature and make it difficult to establish
whether smart city development should be approached by means of a: (1) Technology-led or
holistic strategy; (2) Double or triple/quadruple-helix model of collaboration; (3) Top-down or
bottom-up approach; (4) Mono-dimensional or integrated intervention logic. The questions
arising from these dichotomies open up knowledge gaps that current research is unable to
overcome, making it difficult to advise on how to approach smart city development and deploy
ICT solutions in a way that is capable of delivering urban sustainability (Deakin and Reid 2016;
Mora et al. 2018a; 2018b; Komninos and Mora 2018).
Further research is required to overcome these knowledge gaps and answer the questions
they pose. Examples of such questions include:
What key performance indicators should be assembled to evaluate smart city
development and the ICT-driven approach to urban sustainability it promotes?
What metrics should be applied to assess any ICT-related transformational action and
attributes they seek to cultivate in sustaining urban sustainability?
What are the cultural, financial and institutional barriers to smart city development as
an ICT-driven approach to urban sustainability and what should be done in order to
overcome the limitations they generate?
What business models should be adopted to build a platform of ICT solutions for urban
sustainability that is inclusive, safe and resilient?
What are the activities and phases to be considered when designing and implementing
strategies for enabling smart city development? What strategic planning tools can be
deployed to facilitate the design and implementation process of such activities?
How can privacy concerns and controversy arising from the deployment of smart city
technologies and urban analytics be detected and managed?
What are the dynamics of the governance systems regulating the development of
smart cities as ecosystems of ICT-driven innovations for urban sustainability and
wealth creation?
How geographical conditions and structural characteristics of urban systems influence
the design and implementation process of smart city development projects and
strategies?
References
ABI Research (2013). More Than 30 Billion Devices Will Wirelessly Connect to the Internet of
Everything in 2020. Press release. ABI Research. https://www.abiresearch.com/press/more-than-
30-billion-devices-will-wirelessly-conne. Accessed 9 May 2013.
Aina, Y. A. (2017). Achieving Smart Sustainable Cities with GeoICT Support: The Saudi Evolving Smart
Cities. Cities: The International Journal of Urban Policy and Planning, 71, 49-58.
Alawadhi, S., Aldama-Nalda, A., Chourabi, H., Gil-Garcia, R. J., Leung, S., Mellouli, S., Nam, T., Pardo,
T. A., Scholl, H. J., & Walker, S. (2012). Building Understanding of Smart City Initiatives. In H. J.
Scholl, M. Janssen, M. A. Wimmer, C. E. Moe, & L. S. Flak (Eds.), Electronic Government: 11th IFIP
WG 8.5 International Conference, EGOV 2012, Kristiansand, Norway, September 3-6, 2012.
Proceedings (pp. 40-53). Berlin: Springer.
Alvin Yau, K., Lau, S., Chua, H. N., Ling, M. H., Iranmanesh, V., & Charis Kwan, S. C. (2016). Greater
Kuala Lumpur As a Smart City: A Case Study on Technology Opportunities. In 2016 8th International
Conference on Knowledge and Smart Technology (KST), Chiangmai, 3-6 February 2016 (pp. 96-
101). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).
Angelidou, M. (2017). The Role of Smart City Characteristics in the Plans of Fifteen Cities. Journal of
Urban Technology, 24(4), 3-28.
Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A Survey. Computer Networks, 54(15),
2787-2805.
Badger, E. (2014). How a System for Shared Taxi Rides Could Transform New York City. CityLab.
http://www.citylab.com/cityfixer/2014/03/how-system-shared-taxi-rides-could-transform-new-york-
city/8530/. Accessed 10 May 2015.
Balestrini, M., Marshall, P., & Diez, T. (2014). Beyond Boundaries: The Home As City Infrastructure for
Smart Citizens. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and
Ubiquitous Computing, Seattle, WA, 13-17 September 2014 (pp. 987-990). New York, NY: ACM
Press.
Benevolo, L. (1993). The European City. Oxford: Blackwell Publishing Ltd.
Bolici, R., & Mora, L. (2015). Urban Regeneration in the Digital Era: How to Develop Smart City
Strategies in Large European Cities. TECHNE: Journal of Technology for Architecture and
Environment, 5(2), 110-119.
Bouabdellah, K., Noureddine, H., & Larbi, S. (2013). Using Wireless Sensor Networks for Reliable
Forest Fires Detection. Procedia Computer Science, 19, 794-801.
Boyd, D., & Crawford, K. (2012). Critical Questions for Big Data: Provocations for a Cultural,
Technological, and Scholarly Phenomenon. Information, Communication & Society, 15(5), 662-679.
Castells, M. (1996). The Rise of the Network Society. Oxford: Blackwell Publishing Ltd.
Castillo-Effen, M., Quintela, D. H., Jordan, R., Westhoff, W., & Moreno, W. (2004). Wireless Sensor
Networks for Flash-flood Alerting. In Proceedings of the Fifth IEEE International Caracas
Conference on Devices, Circuits and Systems, 2004, Punta Cana, 3-5 November 2004 (pp. 142-
146). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).
Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-
209.
Clay, X. (2016). Feed Thy Neighbour? There's an App for That - Xanthe Clay Tried Out Food-sharing
App Olio. The Telegraph. https://www.telegraph.co.uk/food-and-drink/features/feed-thy-neighbour-
theres-an-app-for-that---xanthe-clay-tried-ou/. Accessed 10 November 2017.
Colding, J., & Barthel, S. (2017). An Urban Ecology Critique on the “Smart City” Model. Journal of
Cleaner Production, 164, 95-101.
Collotta, M., Pau, G., Salerno, V., & Scatá, G. (2012). Wireless Sensor Networks to Improve Road
Monitoring. In M. Matin (Ed.), Wireless Sensor Networks: Technology and Applications (pp. 323-
346). Rijeka: InTech.
Cowley, R., Joss, S., & Dayot, Y. (2018). The Smart City and Its Publics: Insights from Across Six UK
Cities. Urban Research & Practice, 11(1), 53-77.
Deakin, M., & Reid, A. (2016). Smart Cities: Under-gridding the Sustainability of City-districts As Energy
Efficient-low Carbon Zones. Journal of Cleaner Production, 173, 39-48.
Díaz-Ramírez, A., Tafoya, L. A., Atempa, J. A., & Mejía-Alvarez, P. (2012). Wireless Sensor Networks
and Fusion Information Methods for Forest Fire Detection. Procedia Technology, 3, 69-79.
Diez, T., & Posada, A. (2013). The Fab and the Smart City: The Use of Machines and Technology for
the City Production by Its Citizens. In Proceedings of the 7th International Conference on Tangible,
Embedded and Embodied Interaction, Barcelona, 10-13 February 2013 (pp. 447-454). New York
City, NY: ACM Press.
Ericsson (2013). Ericsson Mobility Report: On the Pulse of the Networked Society. Ericsson.
http://www.ericsson.com/res/docs/2013/ericsson-mobility-report-november-2013.pdf. Accessed 10
June 2014.
Ericsson (2014). Ericsson Mobility Report: On the Pulse of the Networked Society. November 2014.
Ericsson. https://www.ericsson.com/assets/local/mobility-report/documents/2014/ericsson-mobility-
report-november-2014.pdf. Accessed 13 January 2018.
Ericsson (2015). Ericsson Mobility Report: On the Pulse of the Networked Society. November 2015.
Ericsson. https://www.ericsson.com/assets/local/mobility-report/documents/2015/ericsson-mobility-
report-nov-2015.pdf. Accessed 13 January 2018.
Ericsson (2016). Ericsson Mobility Report: On the Pulse of the Networked Society. November 2016.
Ericsson. https://www.ericsson.com/assets/local/mobility-report/documents/2016/ericsson-mobility-
report-november-2016.pdf. Accessed 13 January 2018.
Ericsson (2017). Ericsson Mobility Report: On the Pulse of the Networked Society. June 2017. Ericsson.
https://www.ericsson.com/assets/local/mobility-report/documents/2017/ericsson-mobility-report-
june-2017.pdf. Accessed 13 January 2018.
Erol-Kantarci, M., & Mouftah, H. T. (2011). Wireless Sensor Networks for Cost-efficient Residential
Energy Management in the Smart Grid. IEEE Transactions on Smart Grid, 2(2), 314-325.
European Commission (2010). A Commitment Towards Europe's Energy and Climate Policy. Brochure.
European Commission. http://www.eumayors.eu/IMG/pdf/com_brochure_en.pdf. Accessed 5 June
2014.
Evans, D. (2011). The Internet of Things: How the Next Evolution of the Internet Is Changing Everything.
White paper. Cisco Systems.
http://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. Accessed 14 June
2014.
Eyre, J., & Bier, J. (1998). DSP Processors Hit the Mainstream. Computer, 31(8), 51-59.
FAO (2013). The State of Food and Agriculture: Food Systems for Better Nutrition. Rome: Food and
Agriculture Organization of the United Nations.
FAO, IFAD, & WFP (2015a). Achieving Zero Hunger: The Critical Role of Investments in Social
Protection and Agriculture. Rome: Food and Agriculture Organization of the United Nations.
FAO, IFAD, & WFP (2015b). The State of Food Insecurity in the World: Meeting the 2015 International
Hunger Targets: Taking Stock of Uneven Progress. Rome: Food and Agriculture Organization of the
United Nations.
Faulkner, M., Olson, M., Chandy, R., Krause, J., Chandy, K. M., & Krause, A. (2011). The Next Big
One: Detecting Earthquakes and Other Rare Events from Community-based Sensors. In 2011 10th
International Conference on Information Processing in Sensor Networks (IPSN), Chicago, IL, 12-14
April 2011 (pp. 13-24). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).
Fietkiewicz, K. J., & Stock, W. G. (2015). How Smart Are Japanese Cities? An Empirical Investigation
of Infrastructures and Governmental Programs in Tokyo, Yokohama, Osaka and Kyoto. In T. X. Bui,
& R. H. Sprague (Eds.), Proceedings of the 48th Hawaii International Conference on System
Sciences (HICSS), Kauai, HI, 5-8 January 2015 (pp. 2345-2354). Piscataway, NJ: Institute of
Electrical and Electronics Engineers (IEEE).
Fischer, J., Redlich, J., Scheuermann, B., Schiller, J., Gunes, M., Nagel, K., Wagner, P., Scheidgen,
M., Zubow, A., Eveslage, I., Sombrutzki, R., & Juraschek, F. (2013). From Earthquake Detection to
Traffic Surveillance: About Information and Communication Infrastructures for Smart Cities. In Ø.
Haugen, R. Reed, & R. Gotzhein (Eds.), System Analysis and Modeling: Theory and Practice. 7th
International Workshop, SAM 2012, Innsbruck, Austria, October 1-2, 2012. Revised Selected
Papers (pp. 121-141). Berlin: Springer.
Fox, K. (2016). FoodCloud: New App Proves a Nourishing Idea for Wasted Food. The Guardian.
https://www.theguardian.com/environment/2016/dec/04/new-app-proves-a-nourishing-idea-for-
wasted-food-foodcloud. Accessed 10 August 2017.
Gantz, J. F., Chute, C., Manfrediz, A., Minton, S., Reinsel, D., Schlichting, W., & Toncheva, A. (2008).
The Diverse and Exploding Digital Universe: An Updated Forecast of Worldwide Information Growth
Through 2011. White Paper. IDC. https://www.ag.bka.gv.at/at.gv.bka.wiki-
bka/img_auth.php/f/fe/Diverse-exploding-digital-universe.pdf. Accessed 10 March 2018.
Gantz, J., & Reinsel, D. (2011). Extracting Value from Chaos. Press Release. IDC.
https://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf. Accessed
10 March 2018.
Gartner (2013). Gartner Says the Internet of Things Installed Base Will Grow to 26 Billion Units by 2020.
Press release. Gartner. http://www.gartner.com/newsroom/id/2636073. Accessed 12 December
2013.
Harris, S. A. (2017). Food Waste App OLIO Has Become A Lifeline for Those Who Can't Afford to Feed
Themselves. HuffPost. https://www.huffingtonpost.co.uk/entry/food-waste-app-olio-hidden-
hunger_uk_595f4212e4b0d5b458e97c36. Accessed 5 March 2018.
Hejlová, V., & Voženílek, V. (2013). Wireless Sensor Network Components for Air Pollution Monitoring
in the Urban Environment: Criteria and Analysis for Their Selection. Wireless Sensor Network, 5(12),
229-240.
Holler, J., Tsiatsis, V., Avesand, S., Karnouskos, S., & Boyle, D. (2014). From Machine-to-Machine to
the Internet of Things: Introduction to a New Age of Intelligence. Oxford: Elsevier.
Holmes, H. (2018). New Spaces, Ordinary Practices: Circulating and Sharing Within Diverse
Economies of Provisioning. Geoforum, 88, 138-147.
IBM Corporation (2012). The Four V's of Big Data. Infographic. IBM Corporation. http://www-
01.ibm.com/software/data/bigdata/. Accessed 12 January 2014.
ITU (2014). Global ICT Developments. Dataset. International Telecommunication Union.
http://www.itu.int/en/ITU-D/Statistics/Documents/statistics/2014/stat_page_all_charts_2014.xls.
Accessed 10 April 2014.
ITU (2016). Key ICT Indicators for Developed and Developing Countries and the World (totals and
Penetration Rates). Dataset. International Telecommunication Union. https://www.itu.int/en/ITU-
D/Statistics/Documents/statistics/2017/ITU_Key_2005-2017_ICT_data.xls. Accessed 20 January
2018.
ITU and UNESCO (2016). The State of Broadband: Broadband Catalyzing Sustainable Development.
September 2016. International Telecommunication Union and UNESCO.
https://www.itu.int/dms_pub/itu-s/opb/pol/S-POL-BROADBAND.17-2016-PDF-E.pdf. Accessed 27
March 2018.
Jeffrey, J., Patil, R. G., Kaipu Narahari, S. K., Didagi, Y., Bapat, J., & Das, D. (2012). Smart Parking
Service Based on Wireless Sensor Networks. In IECON 2012 - 38th Annual Conference on IEEE
Industrial Electronics Society, Montreal, 25-28 October 2012 (pp. 6029-6034). Piscataway, NJ:
Institute of Electrical and Electronics Engineers (IEEE).
Jordana, S. (2010). BIG’s Proposal for the Audi Urban Future Award. Online article. ArchDaily.
http://www.archdaily.com/77103/bigs-proposal-for-the-audi-urban-future-award/. Accessed
08.03.2014.
Kitchin, R. (2015). Making Sense of Smart Cities: Addressing Present Shortcomings. Cambridge
Journal of Regions, Economy and Society, 8(1), 131-136.
Komninos, N., & Mora, L. (2018). Exploring the Big Picture of Smart City Research. Scienze Regionali:
Italian Journal of Regional Science, 1(2018), 15-38.
Kulkarni, A., & Sathe, S. (2014). Healthcare Applications of the Internet of Things: A Review.
International Journal of Computer Science and Information Technologies, 5(5), 6229-6232.
Kurzweil, R. (2004). The Law of Accelerating Returns. In C. Teuscher (Ed.), Alan Turing: Life and
Legacy of a Great Thinker (pp. 381-416). Berlin: Springer.
Lee, J., Hancock, M. G., & Hu, M. (2014). Towards an Effective Framework for Building Smart Cities:
Lessons from Seoul and San Francisco. Technological Forecasting and Social Change, 89, 80-99.
Lei Win, T. (2017). There Is Now ‘Tinder for Food’ and It Could Help Save the Environment. The
Independent. https://www.independent.co.uk/news/business/news/tinder-food-waste-olio-help-
save-environment-a8113091.html. Accessed 3 March 2018.
Leydesdorff, L., & Deakin, M. (2011). The Triple-helix Model of Smart Cities: A Neo-Evolutionary
Perspective. Journal of Urban Technology, 18(2), 53-63.
Lynch, K. (1960). The Image of the City. Cambridge, MA: The MIT Press.
Mack, C. (2011). Fifty Years of Moore’s Law. IEEE Transactions on Semiconductor Manufacturing,
24(2), 202-207.
McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business
Review, 90(10), 60-68.
McEachran, R. (2016). Can a Craigslist for Cucumbers Tackle the UK's Food Waste?. The Guardian.
https://www.theguardian.com/media-network/2016/may/12/craigslist-uk-food-waste-sharing-
economy-apps. Accessed 5 June 2017.
Mitchell, W. J. (1995). The City of Bits: Space, Place, and the Infobahn. Cambridge, MA: The MIT Press.
Mitchell, W. J. (1999). E-topia: Urban Life, Jim--but Not as We Know It. Cambridge, MA: The MIT Press.
Mitchell, W. J. (2003). Me++: The Cyborg Self and the Networked City. Cambridge, MA: The MIT Press.
Mitchell, W. J. (2004). The City of Bits Hypotesis. In S. Graham (Ed.), The Cybercities Reader (pp. 123-
128). New York City, NY: Routledge.
Moore, G. (1965). Cramming More Components onto Integrated Circuits. Electronics, 38(8), 1-4.
Mora, L., & Bolici, R. (2016). The Development Process of Smart City Strategies: The Case of
Barcelona. In J. Rajaniemi (Ed.), Re-city: Future City - Combining Disciplines (pp. 155-181).
Tampere: Juvenes Print.
Mora, L., & Bolici, R. (2017). How to Become a Smart City: Learning from Amsterdam. In A. Bisello, D.
Vettorato, R. Stephens, & P. Elisei (Eds.), Smart and Sustainable Planning for Cities and Regions:
Results of SSPCR 2015 (pp. 251-266). Cham: Springer.
Mora, L., Bolici, R., & Deakin, M. (2017). The First Two Decades of Smart-City Research: A Bibliometric
Analysis. Journal of Urban Technology, 24(1), 3-27.
Mora, L., & Deakin, M. (2019). Untangling Smart Cities: From Utopian Dreams to Innovation Systems
for a Technology-Enabled Urban Sustainability. Amsterdam: Elsevier.
Mora, L., Deakin, M., & Reid, A. (2018a). Combining Co-Citation Clustering and Text-Based Analysis
to Reveal the Main Development Paths of Smart Cities. Technological Forecasting and Social
Change, doi: https://doi.org/10.1016/j.techfore.2018.07.019.
Mora, L., Deakin, M., & Reid, A. (2018b). Smart City Development Paths: Insights from the First Two
Decades of Research. In A. Bisello, D. Vettorato, P. Laconte, & S. Costa (Eds.), Smart and
Sustainable Planning for Cities and Region: Results of SSPCR 2017 Cham: Springer.
Mora, L., Deakin, M., & Reid, A. (2018c). Strategic Principles for Smart City Development: A Multiple
Case Study Analysis of European Best Practices. Technological Forecasting and Social Change,
doi: https://doi.org/10.1016/j.techfore.2018.07.035.
Mora, L., Deakin, M., Reid, A., & Angelidou, M. (2018d). How to Overcome the Dichotomous Nature of
Smart City Research: Proposed Methodology and Results of a Pilot Study. Journal of Urban
Technology, doi: https://doi.org/10.1080/10630732.2018.1525265.
Pierce, F. J., & Elliott, T. V. (2008). Regional and On-farm Wireless Sensor Networks for Agricultural
Systems in Eastern Washington. Computers and Electronics in Agriculture, 61(1), 32-43.
Pinstrup-Andersen, P., Rahmanian, M., Allahoury, A., Guillou, M., Hendriks, S., Hewitt, J., Iwanaga, M.,
Kalafatic, C., Kliksberg, B., Maluf, R., Murphy, S., Oniang’o, R., Pimbert, M., Sepúlveda, M., Tang,
H., Prakash, V., Ambuko, J., Belik, W., Huang, J., Timmermans, A., & Gitz, V. (2014). Food Losses
and Waste in the Context of Sustainable Food Systems. Report. High Level Panel of Experts on
Food Security and Nutrition (HLPE). http://www.un.org/en/zerohunger/pdfs/HLPE_FLW_Report-
8_EN.pdf. Accessed 5 September 2016.
Plessi, V., Bastianini, F., & Sedigh, S. (2007). A Wireless System for Real-time Environmental and
Structural Monitoring. In R. Obermaisser, Y. Nah, P. Puschner, & F. J. Rammig (Eds.), Software
Technologies for Embedded and Ubiquitous Systems: 5th IFIP WG 10.2 International Workshop,
SEUS 2007, Santorini Island, Greece, May 2007. Revised Papers (pp. 456-465). Berlin: Springer.
Praharaj, S., Han, J. H., & Hawken, S. (2017). Urban Innovation Through Policy Integration: Critical
Perspectives From 100 Smart Cities Mission in India. City, Culture and Society, 12, 35-43.
Rector, K. (2018). Enhancing Accessibility and Engagement for Those with Disabilities. IEEE Pervasive
Computing, 17(1), 9-12.
Ryan, J. (2010). A History of the Internet and the Digital Future. London: Reaktion Books Ltd.
Saha, M., Hara, K., Behnezhad, S., Li, A., Saugstad, M., Maddali, H., Chen, S., & Froehlich, J. E.
(2017). A Pilot Deployment of An Online Tool for Large-Scale Virtual Auditing of Urban Accessibility.
In ASSETS'17: Proceedings of the 19th International ACM SIGACCESS Conference on Computers
and Accessibility, Baltimore, MD, 29 October - 01 November 2017 (pp. 305-306). New York City,
NY: ACM Press.
Santi, P., Resta, G., Szell, M., Sobolevsky, S., Strogatz, S., & Ratti, C. (2014). Quantifying the Benefits
of Vehicle Pooling with Shareability Networks. PNAS, 111(37), 13290-13294.
Saunders, T., & Baeck, P. (2015). Rethinking Smart Cities from the Ground Up. Research report. Nesta.
http://www.nesta.org.uk/sites/default/files/rethinking_smart_cities_from_the_ground_up_2015.pdf.
Accessed 2 November 2015.
Schaller, R. R. (1997). Moore's Law: Past, Present, and Future. IEEE Spectrum, 34(6), 52-59.
Schreiner, C. (2016). International Case Studies of Smart Cities: Rio De Janeiro, Brazil. Discussion
paper. Inter-American Development Bank.
https://publications.iadb.org/bitstream/handle/11319/7727/International-Case-Studies-of-Smart-
Cities-Rio-de-Janeiro-Brazil.pdf?sequence=1&isAllowed=y. Accessed 15 July 2017.
Shark, A. R. (2015). Technology and Public Management. New York City, NY: Routledge.
Shwayri, S. T. (2013). A Model Korean Ubiquitous Eco-city? The Politics of Making Songdo. Journal of
Urban Technology, 20(1), 39-55.
Stertz, B. (2014). MIT, Audi Launch HubCab Project in New York City. Press Release. AUDI.
https://www.audiusa.com/newsroom/news/press-releases/2014/03/mit-audi-hubcab-project-new-
york-city. Accessed 5 May 2015.
Szell, M., & Groß, B. (2014). HubCab - Exploring the Benefits of Shared Taxi Services. In D. Offenhuber,
& C. Ratti (Eds.), Decoding the City: How Big Data Can Change Urbanism (pp. 28-39). Basel:
Birkhäuser.
Taylor, C. (2013). Three Enormous Problems Big Data Tech Solves. Wired.
http://www.wired.com/2013/08/three-enormous-problems-big-data-tech-solves/. Accessed 12
January 2014.
Tselentis, G., Domingue, J., Galis, A., Gavras, A., Hausheer, D., Krco, H., Lotz, V., & Zahariadis, T.
(Eds.) (2009). Towards the Future Internet: A European Research Perspective. Amsterdam: IOS
Press.
UNDP (2015). Smartphone App to Turn E-trash Into Hard Cash in China Is Among Winning Innovations
at Global Solutions Summit. United Nations Development Programme. http://www.asia-
pacific.undp.org/content/rbap/en/home/presscenter/pressreleases/2015/09/29/smartphone-app-to-
turn-e-trash-into-hard-cash-in-china-is-among-winning-innovations-at-global-solutions-
summit.html. Accessed 10 March 2018.
UNDP (2016a). China: Turning E-Trash into Cash. United Nations Development Programme.
http://www.asia-pacific.undp.org/content/rbap/en/home/ourwork/development-
impact/innovation/projects/china-ewaste.html. Accessed 10 March 2018.
UNDP (2016b). China’s E-waste Recycling App Goes Global. United Nations Development
Programme. http://www.asia-
pacific.undp.org/content/rbap/en/home/presscenter/pressreleases/2016/06/02/china-s-e-waste-
recycling-app-goes-global-.html. Accessed 10 March 2018.
United Nations (2014). World Urbanization Prospects: The 2014 Revision. ST/ESA/SER.A/352. United
Nations. https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.pdf. Accessed 31 March
2015.
United Nations (2017a). New Urban Agenda. A/RES/71/256. United Nations. http://habitat3.org/wp-
content/uploads/NUA-English.pdf. Accessed 10 December 2017.
United Nations (2017b). World Population Prospects: The 2017 Revision, Key Findings and Advance
Tables. ESA/P/WP/248. United Nations.
https://esa.un.org/unpd/wpp/publications/Files/WPP2017_KeyFindings.pdf. Accessed 10
December 2017.
Vanolo, A. (2014). Smartmentality: The Smart City as Disciplinary Strategy. Urban Studies, 51(5), 883-
898.
WHO (2016). World Health Statistics 2016: Monitoring Health for the SDGs. Geneva: World Health
Organization.
Wiig, A. (2015a). IBM's Smart City as Techno-utopian Policy Mobility. City: analysis of urban trends,
culture, theory, policy, action, 19(2-3), 258-273.
Wiig, A. (2015b). The Empty Rhetoric of the Smart City: From Digital Inclusion to Economic Promotion
in Philadelphia. Urban Geography, 37(4), 535-553.
Yigitcanlar, T., & Kamruzzaman, M. (2018). Does Smart City Policy Lead to Sustainability of Cities?.
Land Use Policy, 73, 49-58.
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