Smart Cities and Innovative Urban Technologies

Tommi Inkinen, Tan Yigitcanlar, Mark Wilson

• Publisher: Routledge
• Year: 2020
• ISBN:
  9781000329605
• Edition: 1
• Topic: Architecture
• Subtopic: Urban Planning & Landscaping

Over the past decade smart urban technologies have begun to blanket our cities, forming the backbone of a large intelligent infrastructure. Along with this development, dissemination of the smart cities ideology has had a significant imprint on urban planning and development. Smart Cities and Innovative Urban Technologies focuses on the concepts of smart cities and innovative urban technologies. It contains research that provides insight into spatial formations of information and communication technologies, and knowledge production practices from various perspectives—including analyses of public and private sectors together with NGOs and other stakeholders. It provides a state-of-the-art analysis from multidisciplinary point-of-view in urban studies.

Contributions in this edited volume include theoretical developments as well as empirical analyses. This book will be of great use to various audiences including academics as well as practitioners, spatial developers, planners, and public administrators in order to increase understanding of the dynamics and factors effecting smart cities conceptual maturation and their physical emergence. Information generated in these chapters, particularly regarding the challenges and obstacles of smart cities and innovative urban technologies, are intended to be of benefit to the key local actors in making decision in their cities or/and peripheral locations.

This book was originally published as a special issue of the Journal of Urban Technology.

Smart City Planning from an Evolutionary Perspective

N. KomninosiD, C. KakderiiD, A. PanoriiD, and P. Tsarchopoulos

ABSTRACT

In the theory of urban development, the evolutionary perspective is becoming dominant. Cities are understood as complex systems shaped by bottom-up processes with outcomes that are hard to foresee and plan for. This perspective is strengthened by the current turn towards smart cities and the intensive use of digital technologies to optimize urban ecosystems. This paper extends the evolutionary thinking and emerging dynamics of cities to smart city planning. It is based on recent efforts for a smart city strategy in Thessaloniki that enhances the economic, environmental, and social sustainability of the city. Taking advantage of opportunities offered by the IBM Smarter Cities Challenge, the Rockefeller 100 Resilient Cities, the World Bank, and the EU Horizon 2020 Program, Thessaloniki shaped a strategy for an inclusive economy, resilient infrastructure, participatory governance, and open data. This process, however, does not have the usual features of planning. It reveals the complex dimension of smart city planning as a synthesis of technologies, user engagement, and windows of opportunity, which are fuzzy at the start of the planning process. The evolutionary features of cities, which until now were ascribed to the working of markets, are now shaping the institutional aspects of planning for smart cities.

Smart Cities from an Evolutionary Perspective: Frame of Reference

Masdar City is a landmark in twenty-first-century urban development as it is the first zero carbon city, opening up an era of technology-led sustainability and green growth. But, is Masdar a city? According to The Guardian (Goldenberg, 2016) only 300 people so far live on the site and all are students at the Institute of Science and Technology. In fact, Masdar is actually a group of buildings, a large physical complex; more an engineering construct than a city. It will become a city in the future, when people and human activities, culture, institutions, and behaviors give purpose and use to infrastructures and buildings. Masdar will evolve into a city, as all cities do; they evolve and become cities rather than being constructed as cities from scratch. This idea of “cities becoming cities” rather than “cities planned as cities” is a core premise of evolutionary thinking about urban development. Cities are extremely complex and chaotic systems; many forces work simultaneously in their making and even small variations in the outcome interact and produce huge changes in results. Economic and political forces create numerous constraints on cities, yet there is room for genuine development that is not bound by deterministic conditions.

Evolutionary thinking holds a preeminent position in urban and regional development theory. Cities and regions offer resources that are actualized by selective mechanisms that drive change and growth. Lambooy (2002) argues that urban regions offer effective contexts for development through an evolutionary process where cognitive, innovative, and organizational competencies are influenced by a selection environment composed of institutions, markets, and spatial structure. This environment drives the choice between alternative planning ideas and designs for new investments in city services and infrastructures. Here there is an analogy to the way Nelson and Winter (1977) have described innovation as a purposive, but inherently stochastic activity, which is guided by an external selection environment that determines how different technologies are selected and change over time. The innovation selection environment is shaped by market and non-market forces, consumer preferences, investment, and imitation processes, as well as political and regulatory control over firms. Simmie and Martin (2010) widen this understanding of how innovation in cities is produced, connecting the development of cities and regions to four conceptual frameworks that offer an evolutionary account of resilience and adaptation: (1) generalized Darwinism which places emphasis on variety, novelty, and selection; (2) path dependence theory that underlines historical continuity “lock-in” and new path creation; (3) complexity theory with its emphasis on self-organization, bifurcations, and adaptive growth; and (4) panarchy that links resilience and “adaptive cycles.” Boschma (2004) points out the uniqueness of urban and regional growth paths from an evolutionary perspective, since the competitiveness of a region depends on intangible, non-tradable assets resting on a knowledge base embedded in the region’s specific institutional setting. Transferring growth models from one region to another is questionable as there is no “optimal” development model, and new successful trajectories and developmental paths emerge spontaneously and unexpectedly in space. Bettencourt et al. (2010) argue that agglomeration non-linearities connect most urban socioeconomic indicators with population size, making larger cities centers of innovation, wealth, and crime. They find that local urban dynamics display long-term memory, so cities under- or out-perform their size expectation and maintain such advantage for decades.

All the above statements are meaningful for smart city planning: a process that highlights the uniqueness of each city trajectory, is based on rapidly changing digital technologies, and is ready to value opportunities offered over time rather than copycat planning, locked-in optimal models and one-size-fits-all solutions. The case study we discuss in this paper presents a decision-making environment in a state of constant change, which is discontinuous and non-linear, but offers unexpected windows of opportunity; a complexity that has few commonalities with spatial planning as an ordered process that guides actions from an existing situation to an envisaged future (See also De Roo and Silva, 2016). The scientific ambition of the paper is to reveal the evolutionary dimension of smart city (or intelligent city)¹ planning, due to rapidly changing digital technologies and opportunities that in many cases do not exist at the start of the planning process, which justify the need to replace rigid and well-defined city plans with roadmaps that enable them to integrate evolving technologies and initiatives.

Thus, in this paper, we expand the evolutionary perspective of urban growth to smart city planning. We argue that due to the complexity of smart city development processes and the multi-disciplinary character of smart city technologies, smart city planning is shaped by evolutionary processes too. Evolutionary processes are characterized and affected by essential diversifications in the capacity of societies to generate technical innovations that are suitable to their needs (Rosenberg, 1982). These differences also relate to higher complex systems of policy design that form pools of opportunities for funding and research. Cities and urban planning processes are affected by these dynamic environments, when trying to efficiently exploit existing opportunities for policy formation, in order to achieve a leading position within the global context, to attract more funds and inward investment. It is important to understand that urban and regional developmental evolutionary paths depend on the nature of selection environments, such as public funding, administrative rules, policy frameworks, and others. In this case, the selection process is shaped by political, economic, and cultural factors and the competencies of carrying actors and institutions (Lambooy, 2002). Urban contexts influence the ways in which local governments can create and shape opportunities for innovation.

Planning for smart cities—or the use of digital technology to innovate and improve urban ecosystems—has become a major strand of contemporary urban planning literature. Since the beginning of 2017, publications on smart cities have accounted for close to 50 percent of all publications related to urban planning (Google Scholar data). Yet, major aspects of this new planning model are not well understood, especially the interaction between and integration of long-term, top-down plans and short-term, bottom-up initiatives.

The planning objectives and the type of smart city projects that cities implement are also highly diverse (Yigitcanlar, 2016). Take for instance, three well-known cases of smart city strategy: Singapore Intelligent Nation, Amsterdam Smart City, and Smart Santander. A sector-focused approach in Singapore is implemented using web-based platforms in the domains of digital media, financial services, manufacturing, logistics, and others, compared to projects focusing on sustainability, energy savings, CO₂ reduction, and user participation in Amsterdam, and the deployment of numerous sensors and Internet of Things infrastructure in Santander over which technology providers are asked to develop applications and e-services. These cases illustrate very diverging approaches both in terms of planning priorities and the understanding of how smart cities work.

To our mind, smart city planning defines a distinct phase in the evolution of urban planning, a new planning paradigm that differs substantially from the Twentieth Century and mainly the post-WWII schools of planning (Hall, 1988). This perspective nurtured the discussion about a new science of cities (Batty, 2013; Bettencourt and West, 2010) with cities seen as entities that enable communication and networking, and therefore producing externalities for wealth and the saving of infrastructure, regardless of the economic and geographical context. However, the critical factors that clearly differentiate smart city planning from previous planning perspectives are the knowledge base and the mode of operation. The City Beautiful movement and the plans of Haussmann in Paris, Burnham in Chicago, Lutyens and Baker in New Delhi, Griffin in Canberra, and Hébrard in Thessaloniki were based on knowledge supplied by engineering sciences, architecture, and landscape design. Later, throughout most of the Twentieth Century, the modernist movement for the rebuilding of urban centers and/or suburban sprawl was based on understanding the role of the state in urbanization, regulations and policy incentives for urban development and building, control of land uses, creation of large-scale infrastructure for mobility, social housing, and welfare economics; in sum, a knowledge base provided by social sciences, theories of location, land and traffic management, and strategic planning. Currently, the making of digital, smart, and intelligent cities, uses different materials, such as broadband communication networks, sensors, big datasets, software applications, and e-services. Their knowledge base is offered by programming languages, algorithms, mining large datasets, analytics, software design and development, and user engagement and co-design. This historical expansion of city planning’s knowledge base has been cumulative and interdisciplinary with each subsequent field of knowledge adding new elements to the previous one, but also retaining most of the previous theoretical construction.

Planning for smart cities starts with the creation of the urban digital space, an agglomeration of digital hardware and software, datasets from the public administration, sensors and smart meters, social media, and new e-services in every domain of the city. This new layer of digital space and technologies has the capacity to change and optimize all aspects of cities: the economy, life, utilities, and governance. We have called this process “innovation circuit 1” (IC1) which creates the digital space of cities. The overall smart urban system is made of heterogeneous and uncoordinated initiatives by the public administration, global social media companies, national telecom companies, IT developers, e-service providers, and users; each actor adding some digital component to a common pool of resources, and each one offering new modes of user engagement, participation, and empowerment. In parallel to the formation of the urban digital space, two other processes of innovation emerge: more informed decision-making and governance of public and private investments that drive the change of cities (“innovation circuit 2” [IC2]); and more efficient citizen behavior based on urban awareness that guides the use of urban space and infrastructure through intelligent systems, GPS, and sensor-based solutions (“innovation circuit 3” [IC3]) (Komninos, 2014, 2016b). These three circuits, taken together, define smart city planning and describe the operation of smart or intelligent cities as complex cyber-physical systems of innovation. Innovation circuits 2 and 3 are based on and become possible thanks to the digital space of cities. Innovation circuits IC1, IC2, and IC3 work in tandem; there is no evolution among them. They occur simultaneously; the moment IC1 is introduced, depending on its functionality, it enables better decision-making and / or optimized user behavior. When IC1 relies on web 2.0 technologies, collaboration platforms or crowdsourcing solutions, decision-making becomes participatory with the engagement of users. They constitute forms of citizen empowerment and data awareness, either by the city producers or the city users.

Understanding the planning and making of smart cities through the juxtaposition of digital elements, which are heterogeneous, uncoordinated and usually not integrated, and through novel producer and user behavior, which is also fragmented and diverse, is far from the usual concept of urban planning we have been used to. Thus, smart city planning, as control and guida...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Citation Information
• Notes on Contributors
• Introduction: Smart Cities and Innovative Urban Technologies
• 1 Smart City Planning from an Evolutionary Perspective
• 2 Smart Cities and Mobility: Does the Smartness of Australian Cities Lead to Sustainable Commuting Patterns?
• 3 The (In)Security of Smart Cities: Vulnerabilities, Risks, Mitigation, and Prevention
• 4 E-Capital and Economic Growth in European Metropolitan Areas: Applying Social Media Messaging in Technology-Based Urban Analysis
• 5 How to Overcome the Dichotomous Nature of Smart City Research: Proposed Methodology and Results of a Pilot Study
• 6 “Mapping” Smart Cities
• 7 Towards Post-Anthropocentric Cities: Reconceptualizing Smart Cities to Evade Urban Ecocide
• Index