Accepted Manuscript Title: A methodological framework for assessment of ubiquitous cities using ANP and DEMATEL methods Authors: Tahere Ghaemi Rad, Abolghasem Sadeghi-Niaraki, Alireza Abbasi, Soo-Mi Choi PII: DOI: Reference:
S2210-6707(17)30789-8 https://doi.org/10.1016/j.scs.2017.11.024 SCS 854
To appear in: Received date: Revised date: Accepted date:
4-7-2017 19-11-2017 19-11-2017
Please cite this article as: Rad, Tahere Ghaemi., Sadeghi-Niaraki, Abolghasem., Abbasi, Alireza., & Choi, Soo-Mi., A methodological framework for assessment of ubiquitous cities using ANP and DEMATEL methods.Sustainable Cities and Society https://doi.org/10.1016/j.scs.2017.11.024 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
A methodological framework for assessment of ubiquitous cities using ANP and DEMATEL methods Tahere Ghaemi Rad1, Abolghasem Sadeghi-Niaraki 1,2*, Alireza Abbasi3, Soo-Mi Choi2 1Faculty of Geomatics Eng, Geoinformation Tech. Center of Excellence, K.N.Toosi Univ. of Tech., Tehran, Iran 2Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea 3School of Engineering & Information Technology, UNSW, Canberra, Australia
* Correspondence:
[email protected]
Highlights
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Smart and u-city is solution to exacerbating urban problems.
The main components and criteria of a smart and ubiquitous city are introduced. A methodological framework for establishing and assessment of u-cities is developed. Using ANP and DEMATEL methods to rank the importance of the criteria/ components, the u-coefficients for Tehran and Seoul are calculated 0.449 and 0.918 respectively.
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Tehran can be a smart city by enhancing its transportation, healthcare, and economy.
Abstract
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The increasing development of civilization is one of the important issues exacerbating urban problems, such as pollution, poor sustainability, and weak security. The creation of ubiquitous cities (ucity), with intelligent convergence systems, is a solution to overcoming these problems. The u-city is at a more advanced level than a smart city, and building a smart city is a step toward the u-city. Thus, this study develops an effective framework to determine the required platform toward establishing a u-city by explores the main components of a smart city, such as citizens, environments, and key infrastructures, and the criteria to measure each component. The interaction between the components and their criteria, are specified using the Analytical Network Process (ANP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods. Finally, according to the weights obtained, a ubiquitous coefficient for a city is proposed. In this study, the u-coefficients for Tehran, Iran and Seoul, South Korea are obtained (45% and 92%, respectively), which verify the accuracy of the suggested framework for determining ubiquitous conditions of cities. Tehran has the highest weights in the basic components (citizens and governments) and it needs to invest in building a ubiquitous infrastructure in transportation, healthcare, and the economy.
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Keywords: Ubiquitous City, Smart City, Ubiquitous Coefficient, Analytical Network Process (ANP), Decision-Making Trial and Evaluation Laboratory (DEMATEL)
1.
Introduction
In recent years, there has been an increasing trend in the migration of large number of people to urban areas. It is forecasted that more than 60% of the population will live in an urban environment by 2030(Gaur, Scotney et al. 2015). Globalization, urbanization, and industrialization have been recognized as three important drivers leading human civilization to the overpopulation of cities and its consequent problems, such as inadequate public transportation, pollution, poor sustainability, weak security, and slow business generation(Abellá-García, Ortizde-Urbina-Criado et al. 2015), that requires cities to compete for resources to increase their citizens’ quality of life (Belanche, Casaló et al. 2016).
The above intractable problems make an urgent need to develop and apply innovative solutions and sophisticated methods in the field of urban planning and design (Bibri and Krogstie 2017).One of the approaches taken for dealing with the problems caused by urbanization is to move toward the creation of ubiquitous cities (u-cities), which can be operationalized through ubiquitous computing, in which computing technology becomes nearly invisible by being embedded into every objects (Lee, Uhm et al. 2007), and helps to improve the quality of services.
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Weiser (1993) has been recognized as a pioneer who proposed ‘Ubiquitous Computing’ project at the Xerox Palo Alto Research Centre in the US, constructing an environment in which “digital networks and devices link individual residents not only to other people but also to goods and services whenever and wherever they need” (Lee, 2008). Ubiquitous means having access to any data or services at any time and in any place via any device through any network. It includes various technologies to connect seamlessly the physical objects of the real world (Jung, Pyeon et al. 2008; Hashemi and Sadeghi-Niaraki 2016). A u-city can, therefore, be broadly defined as a city which operates beyond time and space (Hyang-Sook, Byung-Sun et al. 2007). In a u-city, digital devices such as computers will become elements of an environment in which any user can continually interact with hundreds of interconnected digital devices (Weiser 1993) at anytime and anywhere and therefore be able to make decision based on the available information, just like a human. sensors and actuators are embedded into the physical environment (e.g., traffic lights, and parking lots) and various personal devices (e.g., smart phones, and computers), and all these entities are connected via digital networks which often provides services such as location-based services (Maass and Varshney 2012, Jamali et al. 2015). The concept of the ucity could enhance the urban economy by embedding information and communication technologies (ICT) in municipal infrastructure and urban facilities and, for instance, help to reduce wasting resources and time (Yigitcanlar and Lee 2014).
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It is worthy to note that the concept of the smart city differs from that of the u-city. In a smart city, traditional urban services are replaced by electronic services, and the use of programmable devices becomes more common, but in a u-city, too many sensors are embedded into the fabric of daily life and smart devices are interconnected on network enables anyone in any place with any device at any time do anything desired. So, generally, the u-city is at a more advanced level than the smart city, and building a smart city is one step toward a u-city.
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Despite the importance of this issue, there are few discussions in academic literature on the relevant theory and / or frameworks (Lee, 2014); basic elements and their interrelationships are still ambiguous and there is no reliable framework for the comparisons. On the other hand, the required technologies span multiple fields and cannot be effective without integration and relationship analysis. So, in order to fill the aforementioned gaps, this research aims to develop an operational framework for the evaluation of city's level of smartness through determining the main components of a smart city and their interactions. To do so, first, the main components of a smart city (e.g., citizens, governments, homes, environments, power networks, water networks, transportation networks, health, security, the economy, and the education systems) and the criteria to measure them are determined; then, the interactions and degree of importance of each component are specified using the DecisionMaking Trial and Evaluation Laboratory (DEMATEL) method and the Analytical Network Process (ANP), and according to the weights obtained, a ubiquitous coefficient (u-coefficient) is calculated which shows the current ubiquity state of a city. Finally, to evaluate the accuracy of our proposed framework, the u-coefficients for Tehran (Iran), as our case study, and Seoul (South Korea), as a pioneer in this field, are calculated and compared.
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Section 2 reviews the most relevant studies on smart and ubiquitous technologies for determining a framework to make a smart city and a u-city. Section 3 determines and describes the components of a smart city and criteria for each one. The ubiquitous criteria of each component and the results of implementing ANP and DEMATEL on the framework are presented in Section 4, where the u-coefficient for Tehran is also calculated as a case study. Section 5 covers the evaluation process of the proposed framework by calculating the u-coefficient for Seoul and also discussion about the results. Finally, the paper concludes by summarizing the contribution of this study and addressing existing challenges which requires future works in section 6.
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Literature review
Several smart documents are drafted for a number of large cities (e.g. Bilbao, Spain; Seoul, South Korea; London, England; and Chicago, USA) that describes local projects and infrastructures to achieve a smart city. Also, several conceptual and strategic models have been provided discussing how to design and develop smartness in cities (Angelidou 2014). Although smart cities represent a conceptual urban development model based on the utilization of human, collective, and technological capital to enhance development and prosperity
in urban agglomeration, strategic planning for development still remains a rather abstract idea for several reasons, including the fact that it refers to largely unexplored and interdisciplinary fields. For this reason, (Angelidou 2014) reviewed the factors that differentiate policies for the development of smart cities in an effort to provide a clear view of the strategic choices that come forth when mapping out such a strategy. A range of strategies for smart city development, from national to local, new versus existing, hard versus soft infrastructure, to economic sector-based versus geographically based strategies, have been mentioned in the research, and it has finally been noted that cities should identify their priorities and commence development based on their needed infrastructure.
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Nam and Pardo (2011) offered strategic principles aligning to the three main dimensions (technology, people, and institutions) of smart city: integration of infrastructures and technology-mediated services, social learning for strengthening human infrastructure, and governance for institutional improvement and citizen engagement. This research has also explored the practical implications of the conceptual model suggested(Nam and Pardo 2011).
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Lee et al. (2014) studied six key conceptual dimensions (i.e., urban openness, service innovation, partnerships formation, urban proactiveness, smart city infrastructure integration, and smart city governance) and 17 subdimensions of smart city practices. They combined these perspectives in a conceptual framework highlighting the processes for building a smart city and used Seoul metropolitan city and San Francisco as two case studies. They also reported eight ‘stylized facts’ needed to develop a sustainable smart city(Lee, Hancock et al. 2014).
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Monfaredzadeh and Krueger (2015) aimed to improve the social sustainability of cities by providing a particular conceptual focus on the potential of smart city strategies. A measurement framework of social sustainability was suggested, and the attributes were also mentioned. They also emphasized the fact that a city will be smart to when responding to the needs of the population without covering ultra-wideband(Monfaredzadeh and Krueger 2015).
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Shin (2009), assessing qualitative data related to the South Korean u-city projects, highlights the need for a comprehensive approach to integrate ‘technological possibilities’ with ‘social application needs’ as the key challenge of such projects(Shin 2009). Qualitative indicators for smart city business models were also discussed by Walravens (2015). That research starts from an established business model framework and expands it to include an additional set of indicators required to successfully perform a qualitative analysis of the business models for new (digital) services offered by cities(Walravens 2015).
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Security and privacy challenges in a smart city were studied by (Elmaghraby and Losavio 2014) and (Bartoli, Hernández-Serrano et al. 2011). Security includes preventing illegal access to information and fending off attacks causing physical disruptions in service availability. As digital citizens have more and more personal devices that provide data about their locations and activities, privacy seems to be at risk. These studies summarized the key challenges (e.g. privacy, networking connectivity, complexity, security services, sensitive data organization, etc.), emerging technology standards such as IEEE 1 (http://www.ieee.org/) and IETF 2 (http://www.ietf.org/), and issues to be watched for in the context of privacy and security in smart cities, and also presented a model representing the interactions between people, servers, and things. Those are the major elements in the smart city, and their interactions must be protected.
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Bhunia (2014) proposed fuzzy-assisted data gathering and an alert scheme based on the Internet of Things (IoT) for healthcare services in a smart city(Bhunia, Dhar et al. 2014). A smart waste collection/management system was discussed by (Digiesi, Facchini et al. 2015) and (Gutierrez, Jensen et al. 2015). Digiesi (2015) proposed a decision support system to help public administrators at the municipal level to design and plan an integrated waste management system to minimize net carbon emissions(Digiesi, Facchini et al. 2015). Gutierrez (2015) presented a waste collection solution, using intelligent trash cans, and an IoT prototype embedded with sensors to read, collect, and transmit trash volume data over the Internet(Gutierrez, Jensen et al. 2015). Sharif and Sadeghi-Niaraki (2017) states a survey for ubiquitous sensor network simulation and emulation environments which can be used for implementation various sensors of u-city services in simulated environments. The structure and elements of smart education were studied by (Soltani 2012) and semantics were considered in (Effati and Sadeghi‐Niaraki 2015). Caponio et al. (2015) analyzed variables related to energy consumption and proposed a simulation model based on System Dynamics which allows the testing of “what-if” scenarios and analysis of the results of implementing energy efficiency policies. It has been shown that the proposed SD model is effective at simulating and improving the local energy planning policies in the residential building sector(Caponio, Massaro et al. 2015). For more information about different aspects of smart cities, please refer
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to (Debnath, Chin et al. 2014), (Mizuki, Mikawa et al. 2012), (Whittle, Allen et al. 2013), (Chan, Campo et al. 2009), (Hunashal and Patil 2012), (Jedliński 2014), and (Giuffrè, Siniscalchi et al. 2012).
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One of the key challenges in urban computing is the great diversity and density of people, devices, and built-up artifacts in urban places. Dealing with this volume of diversity with the available smart systems and services is not easily possible, and it seems essential to add ubiquitous attributes to the available smart services in order to weave them into the fabric of everyday life until they become indistinguishable from it. The future ubiquitous computing environment in urban areas will incorporate a wide variety of devices and services from different manufacturers and developers. These technical aspects of implementing smart city in the real world such as spatial computing data structures and processing techniques, developing unique applications and algorithms to distributed and mobile systems, providing interoperability to access urban services data with distinct computing platforms and preferences, etc., are also considered in (Amirian, Alesheikh et al. 2010) and (Laube, Duckham et al. 2009). Mattoni (2015) developed an operating model to help administrators in planning the actions and strategies in order to fill the gap between theory and practice. They developed a model based matrices of integration to find suitable strategies for each territorial context and also integrate the different characterizing aspects of smart city. Results show that geographical levels effect on the network of actions and coupling of axes and topics(Mattoni, Gugliermetti et al. 2015).
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Oh (2011) considered some aspects of ubiquitous services in u-city implementation and represented the pioneer driving forces of u-city construction in Korea with highly developed ubiquitous information technology (IT) that generates abundant problems with pioneering examples. Survey results suggest that 22.7% of residents of the ucities Dongtan and Songdo are satisfied, whereas 27.5% of residents are unsatisfied with living in them due to the idea that u-city construction damages private property. Thus, the u-city should be planned and provided as “graded u-city systems” that depend mainly on the income levels, cultural levels, and IT functional usability levels of the residents, because most of the u-cities in Korea have been building fairly equivalent contents of internal and external features(Oh and Oh 2011).
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The current status of projects for developing company towns, innovative cities, and the multifunction administrative city into the new administrative capitals of South Korea, and the exploration of strategies for integrating ubiquitous IT into these projects, was surveyed by (Hyang-Sook, Byung-Sun et al. 2007). Suggestions were made for successful integration of ubiquitous IT in new urban development projects, and its key benefits discussed. For this purpose, ubiquitous IT-enabled services must be tailored to the regional characteristics and intended functions of each of these cities as the first step. Then, to minimize risk factors, a linked project between u-city and company towns projects should be provided which will be a more effective approach to the goal of regional revitalization. Finally, conjugating u-city projects with regional revitalization can minimize government, and hence is a wiser way to spend taxpayers' money.
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Development of a u-healthcare system was discussed in (Ogunduyile, Olugbara et al. 2013). In this paper, reporting life prototype implementation of a wearable ubiquitous healthcare system (WUHS) designed for monitoring physiological data of the elderly. This was carried out using integrated wireless biomedical sensors, such as an accelerometer and a pulse oximeter. A similar approach was implemented based on IoT technology for monitoring elderly individuals suffering from Alzheimer Disease (Raad, Sheltami et al. 2015). Using wearable electrocardiogram (ECG) wireless sensors, the described prototype was able to capture vital signs and deliver the desired data remotely for elderly patients staying at home. Also, an implementation of ubiquitous health system (u-health) using smart-watches sensors (Termeh and Sadeghi-Niaraki 2015). A u-crime prevention system operating on the web was proposed by (Moon, Heo et al. 2014) in which they mainly explored the system components, and attempted to approach the system from the perspective of big data, which has lately received lots of attention. The attributes of a u-education system were also discussed in (Lee, Jung et al. 2012), (Neto and Sales 2015) and (Li, Guo et al. 2009). (Ha, Sohn et al. 2007), (Ilkko and Karppinen 2009), and (Yamazaki 2005) considered the main elements of a u-home environment.
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All the studies mentioned above explored only one aspect of a u-environment, i.e., security, education, healthcare, etc. So, this article gathers all the ubiquitous elements together for assessment, and makes an integrated framework consisting of different aspects of the u-city.
3. Methodology In order to propose a framework as an operational benchmark for the implementation of all components related to the smart city, the main components of a smart city is determined through reviewing the available literatures and smart documents; then, any criteria that can lead the smart components to ubiquity is defined. This study uses 16 questionnaires filled out by 16 experts in different fields including GIS (seven), Information Technology
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(five), and urban managers (four). They were asked to compare validity and also strength of interrelationships between defined criteria. Then, Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to produce a criteria relationship network for consequent multi-criteria decision making (MCDM) process. This method is able to effectively analyze direct and indirect relations between components of a system in respect to type and complexity of the system which can be useful for our recommended network due to its complexity. Finally, the Analytical Network Process (ANP) method is used to assign weights to each of the criteria and components due to its ability for considering different levels of interrelations between components of a complex system. At the end, according to the weights obtained, a ubiquitous coefficient (u-coefficient) based on the current situation of a city is calculated, determined by fuzzy values. South Korean city of Seoul has been used as a benchmark to evaluate the accuracy of our proposed framework as South Korea has been identified as one of the leading countries in information and communications technology and digital world with many projects for creating smart and ubiquitous cities (Shin, 2009; Lee, et al, 2008; Shwayri, 2013; Angelidou, 2014). So, at the last step, the u-coefficients for Tehran, (Iran) and Seoul, (South Korea) are calculated through the framework and compared.
Concept and main components of a smart city
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As (Odendaal 2003) says "The concept of a ‘‘smart city’’ is difficult to define. Like most concepts associated with the impact of Information Technology (IT), it lends itself to a broad interpretation. The concept is intrinsically linked to that of the knowledge-based economy: the use of research and new technology to explore new frontiers in science, industry and commerce." According to different resources and studies which have been mentioned in the literature section, it can be concluded that a smart city should have intelligence characteristics according to the following definition:
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A smart city is a city with smart network infrastructure, and consists of smart citizens who live in smart homes (buildings), which are located in a smart environment under a smart security and safety system, who benefit from a smart education and smart healthcare system, and who are managed by a smart governance and administration system that makes smart policies, especially in a smart economy and smart finance (the authors).
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So, it can be interpreted from the definition that a smart city should have two basic elements including ‘smart citizens’ and ‘smart government’ which have their own sub-elemnets as shown in Fig. 1.
Traditional services can become smart through programming what-if scenarios, and consequently, by being able to act automatically in different situations and under dynamic conditions. According to the definition, a smart
city is a city that offers its services through IT-based networks. Despite the benefits that these kinds of services would have in order to minimize current problems of urbanization (e.g. pollution, poor sustainability, weak security etc.), they have many restrictions on their use. In other words, use of these services includes restrictions on location, time, devices, availability of the IT network, and so on. These challenges in a smart city can be solved by moving it toward a ubiquitous city. 3.2.
Identifying the criteria for the components
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A u-city is full of smart and context-aware equipment that have the ability to provide invisible computing (Hyang-Sook, Byung-Sun et al. 2007). In a u-city, computers are the elements of the environment and, as (Weiser 1991) said in his first theory of ubiquitous computing, every service should be human-centric instead of computer-centric. This approach can definitely overcome smart city restrictions. In order to produce a framework for converting smart components of a city to ubiquitous ones, the important ubiquitous criteria for each component of the city have been identified through previous studies and available smart documents which are presented in Table 2.
Determining the interactions between components
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In this study, the interactions between components besides their degree of importance have been specified using DEMATEL and ANP methods. In the following, the principles of the theory of each method have been explained (Huang and Yoon 2011, Arabsheibani, Kanani Sadat et al. 2016).
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3.3.1. DEMATEL
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DEMATEL is a method proposed by the Battelle Memorial Institute and is used to construct the interrelations between factors/criteria. DEMATEL is able to analyze direct and indirect relations between components of a system in respect to type and complexity of the system (Arabsheibani, 2016). DEMATEL has been used as an aggregation method in this study, and different ideas obtained from 16 experts have been aggregated into one matrix using the following steps.
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Step 1: Calculate the initial average matrix by scores. A group of experts investigates the relationship between sets of paired criteria. Each respondent produces a direct matrix, and an average matrix (A) is then derived using the mean of the same factors/criteria in the various direct matrices of the respondents. ⋯ ⋱ ⋯
𝑎1𝑛 ⋮ ] 𝑎𝑛𝑛
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𝑎11 𝐴=[ ⋮ 𝑎𝑛1
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Where aij is the average of paired comparisons of the respondents in the comparison of i to j. Step 2: Calculate the initial influence matrix. The initial influence matrix, X(X=[xij]nn), can be obtained by normalizing the average matrix: 𝑋 = 𝑠×𝐴
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Where s can be calculated through Equation 3 as follow: 𝑠 = min[1⁄ ,1 ] 𝑚𝑎𝑥𝑖 ∑𝑛𝑗=1|𝑎𝑖𝑗 | ⁄𝑚𝑎𝑥𝑗 ∑𝑛𝑖=1|𝑎𝑖𝑗 |
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Step 3: Derive the full direct/indirect influence matrix. According to Equation 4, the elements of the total relation matrix can be obtained. The matrix consists of all the relations, including direct and indirect relations, between the criteria. 𝑇 = 𝑋 + 𝑋 2 + ⋯ + 𝑋 𝑘 = 𝑋(𝐼 − 𝑋)−1 Step 4: Set a threshold value and obtain a network relation map (NRM).
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Based on matrix T, each factor tij of matrix T provides network information about how factor i affects factor j. Setting a threshold value by expert and decision makers to filter the minor effects denoted by the factors of matrix T is necessary in order to isolate the relation structure of the factors; in other words, only factors where the influence value in matrix T is higher than the threshold value can be chosen and converted into the NRM. The value of threshold can be adopted by assessing the mutual effects of each pair of criteria on each other as a measure to maintain more serious effects and also eliminate the smaller ones by the experts and decision makers. When the threshold value and relative NRM have been decided, the NRM can be shown.
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3.3.2. Analytical Network Process
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Saaty first proposed the Analytical Hierarchy Process (AHP), which is a strong and flexible multi-criteria decision analysis approach (Wind and Saaty 1980). AHP helps decision makers to set priorities and choose the best alternative when both qualitative and quantitative aspects are considered (Cakmak and Cakmak 2014; Sadeghi-Niaraki et al., 2011). ANP, also introduced by Saaty, is a more general form of AHP. Where the AHP models a decision-making framework using a unidirectional hierarchical relationship among decision levels, the ANP allows for more complex interrelationships among the decision levels and attributes.
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ANP is a coupling of two parts. The first part consists of a control hierarchy (or network) of criteria and subcriteria that controls the feedback networks. The second part consists of the networks of influence that contain the factors of the problem and the logical groupings of these factors into clusters. Each control criterion (or subcriterion) has a feedback network. A supermatrix of limiting influence that gives the priorities of the factors in the network is computed for each network (Bayazit and Karpak 2007). Feedback and interdependence among the criteria can be computed from the supermatrix. If the relationships among the criteria are not interdependent, the value of the pairwise comparison is 0. However, if interdependent and feedback relationships exist among the criteria, the value would not be 0 anymore, and the unweighted supermatrix, M, would be obtained. If the matrix is not column stochastic (columns sum to 1), the decision maker needs to provide the weights to make it column stochastic and obtain weighted supermatrix W. The limited weighted supermatrix 𝑊 ′ can be calculated based on Equation 5, where k is the power to which the matrix W is converted into a column stochastic matrix. And the accurate relative weights among the criteria can be acquired by considering the gradual convergence of the interdependent relationship (Wu, Tseng et al. 2012): 𝑊 ′ = lim (𝑊)𝑘
Calculating Ubiquitous Coefficient
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𝑘→∞
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The ubiquitous coefficient (UC) of a city can be calculated by considering the condition of each criterion in that city and multiplying the weight of each criterion in the city with its own weight, calculated through the Analytical Network Process. The condition of a criterion in a city can be expressed in binary or with fuzzy values. For binary expression, it should be clear whether a criterion is available in a city or not, but because of the generality of this method, a more flexible one is recommended. Therefore, in this study, the conditions of each criterion in a city can be expressed by the three numbers in Table 1.
Finally, for the city’s evaluation in terms of ubiquitous capabilities, the UC can be calculated with Equation 6: 𝑈𝐶 = ((∑𝐽𝑗=1 ∑𝐼𝑖=1 𝑆𝑖𝑗 )⁄(∑𝐽𝑗=1 𝐼𝑗)) × 100
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in which UC is the ubiquitous coefficient, Sij is the weight of criteria i of component j, J is the number of components, and Ij is the number of criteria in each component. By considering the best condition for each criterion, the maximum u-coefficient is almost 1, and by considering the worst condition for each criterion, the minimum u-coefficient is 0 In this study, the criteria have been considered in Tehran and also in Seoul for evaluation purposes. Because of the interaction between components and criteria, the Analytical Network Process was used as a weighting method.
Implementation and results
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Ubiquitous criteria related to each component are provided in the second column of Table 2 through using available intelligent documents and related papers and also interviewing related experts. Each component can be examined from different aspects, and different criteria can be allocated for their evaluation. Fig. 2 shows the interrelations between components established with a network diagram by a team of experts, including seven GIS specialists, five IT engineers, and four urban managers through ANP analysis. An α-cut of 0.04 is considered by averaging expert’s judgments about supermatrix values, which have been obtained through ANP analysis to finalize the total-influence matrix. The effort has been made to define an α-cut to prevent missing of valuable data besides reducing the complexity of the relationships between criteria. The number of identified criteria for each component is shown besides the components names which show the availability of interrelations between criteria of each component.
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According to the interrelations available between components, the supermatrix should be a 11×11 matrix, as seen in Equation 7, where some elements equal zero reflecting lack of relation between them:
𝑊𝐶−𝐶 [ ⋮ 𝑊𝐸𝑑−𝐶
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𝑊𝐶−𝐸𝑑 ⋮ ] 𝑊𝐸𝑑−𝐸𝑑
(7)
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When the pair-wise comparisons were completed by the experts, the supermatrix was constructed, and the relative importance of components and their criteria were identified by considering the interrelationships between them. The normalized relative importance values of criteria are presented in Table 3. Note that for abbreviations, the first letters of the names of each component are used (see table 1).
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For many years, South Korea has been a pioneer in the information technology (IT) sector, and the trend will continue until it attains its ultimate vision of creating a ubiquitous society where people can connect to the web, television and other digital services anytime, anywhere(Shin, 2009). Therefore, because of the city's pioneering role in the field of building ubiquitous cities for its citizens, it has been used as a benchmark to evaluate the accuracy of our proposed framework. So, the conditions of Tehran and Seoul for each of the criteria, which have been extracted through interviewing the experts and activists of each of the fields and also using intelligent documents of Seoul, are represented in Table 4 according to the weights defined in Table 2.
Now, by multiplying final values of the corresponding condition values in Tehran, the u-coefficient of that city can be calculated using Equation 6 as follows: 𝑈𝐶𝑇𝑒ℎ𝑟𝑎𝑛 = 0.432334⁄99 = 0.004367 × 100 = 0.4367
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Overview of the conditions of the two cities in the criteria makes it clear that Seoul has much better conditions according to ubiquitous elements and criteria that are available in that city. So, it is expected that a higher ucoefficient be obtained through Equation 6 for this city. If Equation 6 be applied to the condition values of Seoul, the u-coefficient of Seoul can be calculated as: 𝑈𝐶𝑆𝑒𝑜𝑢𝑙 = 0.873156⁄99 = 0.008819 × 100 = 0.8819
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Evaluation and Discussion
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The value obtained for the u-coefficient of Tehran shows that this city has a u-coefficient of less than 50%, which emphasizes the need to promote planning and investments to enhance ubiquitous capabilities due to their advantages for solving urban problems.
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The calculated u-coefficient verifies that Seoul is a pioneer city in the field of ubiquity, and also confirms the accuracy of the suggested benchmark for measuring the u-coefficient of cities. In order to compare smartness condition of Tehran and Seoul more precisely, Figure 4 shows final scores of each criterion calculated by multiplying its relative importance in the weight which assigned according to the condition of that criterion in the study areas. As the chart shows, chart of Seoul in all the criteria on the graph is dominant in Tehran. So, this can be useful for urban planners and policy makers to know the weak points and take steps to strengthen them.
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According to Table 4, Tehran has the highest weights in the basic components of a ubiquitous city (citizens and governance), and it needs more attention on building ubiquitous infrastructures, especially in transportation, healthcare, and economy. Figure 5 shows the relative importance of those criteria in the field of transportation, healthcare and economy which their conditions in Tehran were zero (see Table 4).
By comparison the relative importance of each criterion, the most important projects which have the most priority in order to improve the smartness of Tehran in the field of transportation, healthcare and also economy are as follow:
Creating an automatic parking system.
Increasing the relationship between industry and education in order to create jobs in accordance with
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Creating a comprehensive database about public health. Creating long-term investment opportunities to fund such projects. Providing a system to monitor automatically the actual technical status of a moving vehicle, its operating conditions and so on. Providing a smart system to control the timing of stop lights automatically. Building a system to detect routes, intersections, passengers, etc.
industry needs.
include building industry-wide partnerships among high-tech sectors Making a system which is able provide early warning of disasters. Providing an interaction and communication system for organizations involved in the field of public health. Providing a smart network system for providing information and interacting with users. Creation of a real-time patient direction during the process of patient care from the patient's arrival at the institution to his/her departure from it. Creation of an air baggage-tracking service.
Creating a smart system for integrated event management. Producing a precise and comprehensive tax system for development. Providing a condition to remotely visiting and controlling patients. Adaptation of policies to rely on the creation of capital through the production and export of products, rather than relying on fuel sales.
Conclusion
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Moving toward u-cities is inevitable, owing to the large amount of urban problems that result from urbanization. The first step to making a u-city is to provide some basic infrastructure, which comprises the main components of a smart city. In other words, making a smart city is a step toward making it a ubiquitous city. The infrastructure includes 11 components, as shown in Fig. 1. This study focused on developing a framework to compute the u-coefficient of a city and assess its level of readiness using these components and their relative ubiquitous criteria. In order to achieve this, the interrelations between components and their criteria have been determined using the DEMATEL method, and the relative importance of the criteria was computed through ANP. Finally, the u-coefficient of the city can be calculated with Equation 1.
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To evaluate the suggested framework, two cities (Tehran and Seoul) with different levels of development in “smartness” were selected. The u-coefficient obtained for Tehran (UCTehran=0.4367) shows that this city has a ucoefficient less than 50%, which emphasizes the need to promote planning and investments to enhance its ubiquitous capabilities. The conditions in Tehran for each criterion show that it needs more extensive programs, especially for its service infrastructures in health, water, education, and economy, in order to achieve ubiquitous growth. In the next step, the u-coefficient of Seoul, as a benchmark for being a ubiquitous city, was calculated (UCSeoul=0.8819). As it is obvious from the u-coefficient obtained, Seoul has much better condition, based on the ubiquitous elements and criteria that are available in that city.
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By reviewing intelligent plans in Tehran and Seoul, the obtained u-coefficients look quite logical. The plan to make Tehran a smart city is recent (12-year-old) and its related policies have been limited to activities such as creating and using smart transportation cards and electronic tickets, internet payments, smart software to inquire complications, etc. infrastructure intelligence programs technology upgrade, and high-speed Internet bandwidth are the fundamental elements of a smart city which are missing in the Tehran plan. On the other hand, Seoul has more mature intelligent program and is a pioneer in the field of building ubiquitous cities for its citizens. This gap has been reflected in the u-coefficient values calculated in this paper. Limitations and future works
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It is obvious that precision of results can be improved by considering more precise methods for studying the dynamics and also the patterns of behavior between the components. Considering patterns of behavior available between the components introduced in this paper is computationally expensive and requires huge work because of the amount of criteria defined for them. So, this can be a challenging issue for future studies.
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On the other hand, as mentioned in Section 3.2, conditions for each criterion in a city were expressed with three numbers (1, 0.5, and 0) in this study, which indicate that the criterion is completely available and pervasive, completely available but not pervasive, or not available, respectively. Although this is a more precise method than a binary designation, it undoubtedly has its own restrictions and generalities. So, it is best to use the suggested framework for a more detailed study of the conditions for each criterion in a city to achieve more precise results for better comparison of cities in the field of smartness. As the last suggestion, we propose to develop a comparative result with similar city approaches to substantiate the practicality of the development of ubiquitous cities in order to achieve sustainable urban development based on the proposed framework. Finally, it should be mentioned that the authors make no claim for the completeness of the set of collected criteria and the framework presented has the ability to become more accurate.
Acknowledgments
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This research was supported by Korean MSIT (Ministry of Science and ICT) under the ITRC support program (IITP-2017-2016-0-00312) supervised by the IITP.
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Smart Healthcare
Smart City
Smart Citizens
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Smart Education
Smart Governance and Admin
Smart Policy
Smart Pwer network
Smart Infrastructure
Smart Water network
Smart Economy and Finance
Smart Transportation network
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Figure 1: Smart city components (by the authors)
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Figure 2: Interrelations between ubiquitous components
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Figure 3: Relative importance of smartness indicators
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Relative importance of smartness indicators
Comparison of final scores obtained for each criterion in Seoul and Tehran 0.08 Seoul
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Figure 4: Comparison of final scores obtained for each criterion in Seoul and Tehran
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Figure 5: Relative importance of criteria with zero value for Tehran
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Criteria The use of sensors for reporting external conditions (C1) The use of a unified biometric ID to access public services (C2) Being creative and able to suggest climate-friendly actions (C3) High participation and involvement in public life and social cohesion (voting, municipalities, etc) (C4) Respect privacy rights (C5) The use of smartphones for measurements, calculations, interactions (C6) The use of different apps for managing and doing work through network software (C7) The holding of electronic elections (E. voting) (G1) The implementation of administrative affairs inside and outside organizations under networks (G2) The existence of a comprehensive geographic information system including Cadaster (G3) The existence of a central data warehouse for storing, retrieving and accessing data (G4) The holding of electronic meetings (E. meetings) (G5) The existence of a management information system (G6) The existence of a public smart urban system to benefit from municipal services (G7) The ability to regulate environmental conditions (temperature, humidity, etc) (H1) The ability to detect people who are present (H2) The ability to detect the type of activity a person is doing (H3) The ability to detect unusual conditions and alarms for security (H4) The existence of smart devices and appliances (smart phone, smart mailbox, smart TV, smart floor, etc.) (H5) The ability to measure energy, gas, and water consumption by household appliances (H6) The ability to share internet and entertainment content with other smart appliances and other smart homes (H7) The ability to manage tools and equipment inside the house (H8) The ability to interact with robots to support basic activities and mobility (H9) The provision of smart waste management and recycling, even at the house level (En1) The ability to have alternative energy suppliers, even at the house level (En2) The ability to continuously monitor air, water, and noise pollution indicators (En3) The existence of an integrated system for managing any type of sewage (En4) The ability to control consumption of energy and natural resources (En5) The existence of information and communication networks embedded in the environment (En6) The ability to develop a co-operative forest network in order to cover forestry, environmental protection, and the timber industry (En7) The ability to remotely detect faults (P1)
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Table 1: Main components and their related criteria of a ubiquitous city Main components
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U-environment (En) (Woodhouse, Howlett et al. 2000)
U-power network (P)
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U-water network (W) (Mizuki 2012), (Whittle, Allen et al. 2013)
The ability to gather information on decentralized and renewable energy sources (P2) The use of high-voltage direct current (HVDC) instead of high-voltage alternating current (P3) The ability to reduce electricity use in buildings, and to regulate energy consumption based on cost and time (P4) The ability to provide two-way transfer of data between the network and its elements (P5) The existence of a real-time control network (P6) The existence of an interface for communicating with users and informing them (P7) The ability to predict possible power outages and replace other resources to prevent outages (P8) The ability to measure water-quality parameters online (W1) The ability to detect pipe bursts and leakage problems (W2) The existence of an interface for communicating with users and informing them (W3) The use of conventional treatment technologies for water supplies and sewage disposal (W4) The ability to analyze the potential impact on the network of an operational event and minimize negative impacts (W5) The ability to detect and avoid water shortages (W6) The ability to remotely manage customers (W7) The ability to improve water quality through a smart water system (W8) The ability to provide essential transportation information for passengers (T1) The ability to sell tickets everywhere (T2) The ability to control the timing of stop lights due to traffic conditions (T3)
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U-transportation (T) Debnath, Chin et al. ( )2014
The ability to monitor the actual technical status of a vehicle, its operating conditions, etc (T4) The ability to detect routes, intersections, passengers, etc (T5) The existence of automatic parking systems (T6) The existence of smart toll/parking charge payment (T7) The existence of two-way communication between vehicles and infrastructures as well as data exchange (T8) The ability to predict traffic flow (T9) The ability to provide early warning of disasters (T10) The ability to smartly manage events (T11) air baggage-tracking service(T12) The existence of a comprehensive database about public health (He1) The existence of an interaction and communication system for organizations involved in the field of public health (He2) The ability to remotely visit and control patients (He3) The existence of real-time patient directions during the process of patient care from the patient's arrival at the institution to his/her departure from it (He4) The existence of a smart network system for providing information and interacting with users (He5) The ability to collect, manage, and control an elderly patient's physiological data (He6) The existence of 24-hour emergency service (He7) The existence of various access levels for safe use of services (S1) The ability to manage emergency services (ambulance, firefighting, etc) so they arrive on site more quickly and safely without causing disruptions to other traffic flows (S2) The ability to efficiently admit people to public institutions, local government offices, or even major hotels, and enhance security by limiting and logging the movement of users (S3)
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U-security (S) (Elmaghraby and Losavio 2014), (Bartoli, HernándezSerrano et al. 2011)
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U-education (Ed) (Soltani 2012), (Shi, Xie et al. 2003), (Morze, Glazunova et al. 2013)
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U-economy (Ec) (Ahmad and Hoffmann 2008)
The existence of a comprehensive information system about community members (S4) The ability to handle financial affairs via radio frequency identification (RFID) card (S5) The ability to provide authorities with a wide range of information about an RFID card's owner when identified by the civil guard or police (S6) The ability to monitor spaces to identify unusual people or objects (S7) ammunition management system(S8) The ability to provide chances for new, long-term investments in order to fund projects (Ec1) The existence of a precise and comprehensive tax system for development (Ec2) The existence of appropriate financial rules to support private companies dealing with all types of risk (Ec3) The ability to create a participatory environment to provide and pay back entrepreneurship loans (Ec4) The ability to concentrate on return of investment in projects (Ec5) The ability to rely on the creation of capital through the production and export of products, rather than relying on fuel sales (EC6) The ability to create jobs in accordance with industry needs (Ec7) The existence of industry-wide partnerships among high-tech sectors(Ec8) The existence of an e-board and a network environment in each classroom for sharing screens between teacher and students (Ed1) The ability to remotely control student devices (Ed2) The existence of a central server that aids teachers in the course of administration, for content management, and for user management and communications (Ed3) The ability to provide remote exchanges between teachers and students (Ed4) The existence of a central question bank (Ed5) The ability to track individual student progress through information graphs or tables, and to encourage participation by creating customized learning plans based on student comprehension (Ed6) The ability to monitor student attendance through smart cards (Ed7) The ability to provide group collaboration between students through their smart tablets (Ed8) The ability to access online learning sources, such as scientific databases, information kiosks, media libraries, scientific laboratories, etc, everywhere (Ed9) The ability to deliver education through different devices, from television sets to iPods to mobile phones to netbooks, beyond schools and into homes (Ed10) The ability to provide real-time online testing and analysis (Ed11) The ability to easily link learners with mentors from industry (Ed12)
The ability to share and use the diversity of experience and thought to help solve complex global or regional problems (Ed13) The existence of an interface for online relationships between parents and schools (Ed14) The existence of research-based learning, instead of education-based learning (Ed15) The ability to provide remote and network education (Ed16) The ability to save all classroom experiences, such as notes, slides, video, and audio (Ed17) The ability to draw up an individual educational program for each student from the set of training elements (Ed18)
Condition
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Table 2: Weights assigned to the condition of the indicators If the indicator is If the indicator is If the indicator is not available and also available but not available at all pervasive pervasive yet 1 0.5 0
Table 3: The normalized relative importance of criteria(%) En
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Ed
C1= 1.73
G1= 1.25
H1= 1.67
H8= 0.53
En1= 1.67
P1= 0.34
W1= 0.45
T1= 3.35
T9= 0.60
He1= 0.81
S1= 1.16
Ec1= 0.72
Ed1= 5.17
Ed9= 0.17
Ed17 = 0.37
C2= 3.38
G2= 3.51
H2= 0.51
H9= 0.22
En2= 0.51
P2= 0.37
W2= 0.41
T2= 0.49
T10= 0.55
He2= 0.52
S2= 0.22
Ec2= 0.40
Ed2= 0.53
Ed10 = 0.37
Ed18 = 0.56
C3= 1.42
G3= 3.30
H3= 0.21
En3= 0.21
P3= 0.66
W3= 0.52
T3= 0.60
T11= 0.44
He3= 0.34
S3= 0.31
Ec3= 0.84
Ed3= 0.55
Ed11 = 0.56
C4= 3.12
G4= 2.37
H4= 2.46
En4= 2.46
P4= 0.39
W4= 0.50
T4= 0.66
He4= 0.48
S4= 0.72
Ec4= 0.29
Ed4= 0.32
Ed12 = 0.47
C5= 3.95
G5= 1.54
H5= 0.50
En5= 0.50
P5= 0.53
W5= 0.51
T5= 0.59
He5= 0.50
S5= 0.35
Ec5= 0.59
Ed5= 0.48
Ed13 = 0.41
C6= 4.75
G6= 2.30
H6= 1.33
En6= 1.33
P6= 0.54
W6= 0.39
T6= 0.57
He6= 0.26
S6= 0.50
Ec6= 0.26
Ed6= 0.31
Ed14 = 0.42
C7= 6.78
G7= 2.87
H7= 0.50
En7= 0.50
P7= 0.48
W7= 0.50
T7= 4.08
He7= 0.43
S7= 0.48
Ec7= 0.57
Ed7= 0.46
Ed15 = 0.41
P8= 0.34
W8= 0.42
T8= 0.59
S8= 0.51
Ec8= 0.58
Ed8= 0.42
Ed16 = 0.48
T12= 0.48
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As it was mentioned in section 3.1, the main elements of a ubiquitous city can be summarized in two basic components namely ‘ubiquitous citizen’ and ‘ubiquitous government’. On the other hand, the obtained values resulted from ANP analysis have also gained higher relative importance for these two components. So, this can verify the interpretation of the main elements in Fig. 1. Fig. 3 shows this fact more properly.
Table 4. Condition of Tehran (T) and Seoul (S) in each of the criterion based on defined weights G2
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Ed16
T=0 S=1 C2
T=0.5 S=1 G3
T=0 S=0 H4
T=0 S=0.5 En3
T=0 S=0.5 P4
T=0.5 S=1 W4
T=0.5 S=1 T4
T=0 S=0.5 T12
T=1 S=1 S1
T=0 S=0 Ec1
T=0 S=0.5 Ed1
T=0 S=1 Ed9
T=1 S=1 Ed17
T=1 S=1 C3
T=0 S=1 G4
T=0.5 S=1 H5
T=1 S=1 En4
T=1 S=1 P5
T=1 S=1 W5
T=0 S=0 T5
T=0 S=0 He1
T=1 S=1 S2
T=0 S=1 Ec2
T=0 S=0.5 Ed2
T=1 S=1 Ed10
T=0 S=1 Ed18
T=0 S=1 C4
T=1 S=1 G5
T=0.5 S=1 H6
T=1 S=1 En5
T=1 S=1 P6
T=0.5 S=1 W6
T=0 S=0.5 T6
T=0 S=0.5 He2
T=0.5 S=1 S3
T=0 S=0.5 Ec3
T=0 S=1 Ed3
T=1 S=0.5 Ed11
T=0 S=1
T=1 S=1 C5
T=0.5 S=1 G6
T=0.5 S=1 H7
T=0 S=1 En6
T=1 S=1 P7
T=1 S=1 W7
T=0 S=1 T7
T=0 S=0.5 He3
T=1 S=1 S4
T=0 S=1 Ec4
T=1 S=1 Ed4
T=0 S=1 C6
T=1 S=1 G7
T=0 S=0 H8
T=0 S=0.5 En7
T=0.5 S=1 P8
T=1 S=1 W8
T=0 S=1 T8
T=0 S=0.5 He4
T=1 S=1 S5
T=1 S=1 Ec5
T=0.5 S=0.5 Ed5
T=1 S=1 C7
T=0 S=1 H1
T=0.5 S=1 H9
T=0 S=0 P1
T=1 S=1 W1
T=0 S=0.5 T1
T=0 S=0.5 T9
T=0 S=0.5 He5
T=0.5 S=1 S6
T=0.5 S=0.5 Ec6
T=0 S=1 Ed6
T=0 S=1 Ed14
T=0.5 S=1 G1
T=0.5 S=1 H2
T=0 S=0.5 En1
T=1 S=1 P2
T=1 S=1 W2
T=0.5 S=1 T2
T=0.5 S=1 T10
T=0 S=0.5 He6
T=1 S=1 S7
T=0.5 S=0.5 Ec7
T=0 S=0 Ed7
T=0 S=0.5 Ed15
T=0 S=1
T=0 S=0
T=0 S=1
T=1 S=1
T=1 S=1
T=0.5 S=1
T=0.5 S=1
T=0 S=0.5
T=0 S=1
T=1 S=0.5
T=0.5 S=1
T=0.5 S=0 Ed12 T=0.5 S=0.5 Ed13
SC R
U
N
A M ED PT CC E A
T=1 S=1
IP T
C1
A ED
PT
CC E
IP T
SC R
U
N
A
M
