Framework for Smart City Model Composition: Choice of Component Design Models and Risks

Framework for Smart City Model Composition: Choice of Component Design Models and Risks

Soon Ae Chun, Dongwook Kim, June-Suh Cho, Michael Chuang, Seungyoon Shin, Daesung Jun
Copyright: © 2021 |Pages: 20
DOI: 10.4018/IJEPR.20210701.oa4
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Abstract

This paper is a reflective overview of the knowledge on online conversion of services in the perspective of urban planning. It points that traditional planning aimed at building optimal spatial relationships between particular functions in urban environment. Appropriate decision-making rules had been introduced, contributing to a hierarchical land-use structure. This conventional approach has been recently challenged by the rapid ICT development which added a lively, virtual, non-spatial dimension of urban economy. The well-established foundations of urban planning started to shake, calling for a new paradigm. This paper looks for an alternative to traditional planning which would be able to develop policies for omnichannel services (i.e., enterprises that use both online and offline channels for communicating and distributing their products). The advantages of ‘e-planning' in managing omnichannel services are outlined and a conclusion is drawn that only a multi-channel approach can bring appropriate answers to contemporary developments in services sector.
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1. Introduction

Recent Information and Communication Technology (ICT) advances have created a hyper-networked world, connecting people, machines, and devices. Every sector of our society has been affected. A majority of daily activities in business, government, healthcare, education, etc. occur online, generating huge amounts of data and creating what has come to be known as the Big Data age. Many cities around the world are taking initiatives to design and build smart cities projects in order to make technology innovations that address various challenges, such as infrastructure issues, traffic congestion, energy shortages, environmental pollution, and the lack of economic opportunities, as well as public safety, public health, social wellbeing, and high-quality education. The major application domains of these smart cities include urban planning, transportation, the environment, energy, social, economy, and public safety & security (Zheng et al., 2014).

There exist many definitions for a smart city, such as one claiming that a “smart city is a city seeking to address public issues via ICT-based solutions on the basis of a multi-stakeholder, municipally based partnership” (Manville et al., 2014). By another definition, a smart city “integrates hardware, software and network technologies in order to connect seven critical city infrastructure components and services: city administration, education, healthcare, public safety, real estate, transportation and utilities” (Washburn & Sindhu, 2009). Other definitions can be found in Albino et al., 2015, and Paskaleva, 2011. Yet another approach stresses citizen-centric and citizen-driven innovations (Albino et al., 2015; Lee & Hancock, 2012; Lee & Lee, 2014), and others call for a triple helix model in which the dynamic interactions among academics, government, and businesses with different networked contributions can create ICT-based urban innovations (Leydesdorff & Deakin, 2011). IBM’s “smarter cities” concept also “emphasizes the need to better apply advanced information technology, analytics and systems thinking” (Dirks et al., 2010).

With many technology alternatives to consider, and many stakeholders to involve, many governments face various design options and models to consider and choose from. They face the problem of deciding which design alternatives may be the most appropriate in each particular smart city project. Smart city projects require multiple layers of design decisions among many possible alternatives, such as what services may be needed, who would provide the necessary data for creating the services, which stakeholders exist and whether or not they need to participate in the design process, creation/development tasks, or evaluation-only, etc. In addition to these design decisions, with the resurgence of Artificial Intelligence, smart cities are expected to become “intelligent cities,” fueling progress in every aspect of life. This intelligent agent-based and automation-driven society has forced citizens to redefine their routine operations, service consumption, and even their value systems. For instance, the online information-seeking behavior would change from primarily text-based interactions to multi-modal (voice and video, VR) interactions, from information-pulling to automated information recommendation and pushing, or from keyword-based query results to reasoning-based answers.

As society is soon going to be heavily dependent on these AI-driven advanced intelligent systems (including robots, “intelligent” devices, embedded systems, etc.), everywhere from routine operations to critical decision recommendations (Russell et al., 2015), it is important to design and develop a reliable, robust intelligent society, but also to recognize potential risks and harm in the design phase. The intelligent/smart city innovations affecting millions of citizens should not be an exception. There are great opportunities in terms of designing this new intelligent society (Hager et al., 2017), but policymakers need to consider the risks of such systems that may affect the daily lives of citizens all over the world.

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