The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis

Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning

This study is intended to develop a stress measurement and visualization system for stress management in terms of simplicity and reliability. We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms. Our proposed method attempts to examine the relation between mood states and extracted categories in human communication from facial expressions, gaze distribution area and density, and rapid eye movements, defined as saccades. Using a psychological check sheet and a communication video with an interlocutor, an original benchmark dataset was obtained from 20 subjects (10 male, 10 female) in their 20s for four or eight weeks at weekly intervals. We used a Profile of Mood States Second edition (POMS2) psychological check sheet to extract total mood disturbance (TMD) and friendliness (F). These two indicators were classified into five categories using self-organizing maps (SOM) and U-Matrix. The relation between gaze and facial expressions was analyzed from the extracted five categories. Data from subjects in the positive categories were found to have a positive correlation with the concentrated distributions of gaze and saccades. Regarding facial expressions, the subjects showed a constant expression time of intentional smiles. By contrast, subjects in negative categories experienced a time difference in intentional smiles. Moreover, three comparative experiment results demonstrated that the feature addition of gaze and facial expressions to TMD and F clarified category boundaries obtained from U-Matrix. We verify that the use of SOM and its two variants is the best combination for the visualization of mood states.

Smart City Thailand: Visioning and Design to Enhance Sustainability, Resiliency, and Community Wellbeing

A “Smart City” framework was used to investigate and develop visions of alternative futures for a peri-urban superblock north of Bangkok, Thailand. The Smart City framework considers seven smart pillars: environment, economy, energy, mobility, people, living, and governance, with a focus on community wellbeing that is supported by information and communication technology (ICT). A mixed-method approach that included: community and industry surveys, both online and face-to-face (total n = 770); in depth, semi-structured, stakeholder interviews; passive participant observation; and photo-documentation was used to inform and organize the project visions and designs. Several themes emerged from the community surveys and key stakeholder interviews: (i) connected green space is highly valued and effectively links multiple smart pillars, enhancing community wellbeing and resiliency to flooding; (ii) superblock mobility, connectivity, and sustainable development could be achieved through a seamless, integrated public-transit system following the principles of transit-oriented development (TOD); (iii) the superblock should prepare for the implementation of Thailand 4.0 through the improved programmatic and physical integration of local industry, community, and universities, including plans for a Digital Village and co-work space. Example designs that address these considerations and vision alternative futures for the superblock are presented in this Smart City case study.

A Holistic and Interoperable Approach towards the Implementation of Services for the Digital Transformation of Smart Cities: The Case of Vitoria-Gasteiz (Spain)

Cities in the 21st century play a major role in the sustainability and climate impact reduction challenges set by the European agenda. As the population of cities grows and their environmental impact becomes more evident, the European strategy aims to reduce greenhouse gas emissions—the main cause of climate change. Measures to reduce the impact of climate change include reducing energy consumption, improving mobility, harnessing resources and renewable energies, integrating nature-based solutions and efficiently managing infrastructure. The monitoring and control of all this activity is essential for its proper functioning. In this context, Information and Communication Technology (ICT) plays a key role in the digitisation, monitoring, and managing of these different verticals. Urban data platforms support cities on extracting Key Performance Indicators (KPI) in their efforts to make better decisions. Cities must be transformed by applying efficient urban planning measures and taking into account not only technological aspects, but also by applying a holistic vision in building solutions where citizens are at the centre. In addition, standardisation of platforms where applications are integrated as one is necessary. This requires interoperability between different verticals. This article presents the information platform developed for the city of Vitoria-Gasteiz in Spain. The platform is based on the UNE 178104 standard to provide a holistic architecture that integrates information from the different urban planning measures implemented in the city. The platform was constructed in the context of the SmartEnCity project following the urban transformation strategy established by the city. The article presents the value-added solutions implemented in the platform. These solutions have been developed by applying co-creation techniques in which stakeholders have been involved throughout the process. The platform proposes a step forward towards standardization, harmonises the integration of data from multiple vertical, provides interoperability between services, and simplifies scalability and replicability due to its microservice architecture.

Sensors for Sustainable Smart Cities: A Review

Experts confirm that 85% of the world’s population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities.

A Social Network Analysis of the Spanish Network of Smart Cities

This paper explores the relations of centrality and hierarchy between cities and firms implementing Smart City strategies in the context of the Spanish Network of Smart Cities (RECI). While the literature has usually focused on the global dimension of cities and firms networks, exploring a national case offers interesting insights about the presence of multinational firms in these contexts and the role played by medium-sized cities in their market expansion. The analysis is based on a two-mode network of cities and firms participating in Smart City projects with the usual measures of betweenness, in-degree and closeness, as well as computing the Gini index for each of them to assess the levels of inequality. We then explore whether the structural advantages of participating in these networks have a leveling effect or rather reinforce existing hierarchies of cities. Second, we explore how firms are intertwined in Smart City projects and whether medium-sized local firms have a relevant presence. Our findings suggest these networks become a regional gateway for multinational firms to expand their presence in Smart City national markets, rather than empowering medium-sized cities and small national firms.

Assessing Global Environmental Sustainability Via an Unsupervised Clustering Framework

The importance of sustainable development has risen in recent years due to the significant number of people affected by lack of access to essential resources as well as the need to prepare for and adapt to intensifying climate change and rapid urbanization. Modeling frameworks capable of effectively assessing and tracking sustainability lie at the heart of creating effective policies to address these issues. Conventional frameworks, such as the Environmental Performance Index (EPI), that support such policies often involve ranking countries based on a weighted sum of a number of relevant environmental metrics. However, the selection and weighing processes are often biased. Moreover, the ranking process fails to provide policymakers with possible avenues to improve their country’s environmental sustainability. This study aimed to address these gaps by proposing a novel data-driven framework to assess the environmental sustainability of countries objectively by leveraging unsupervised learning theory. Specifically, this framework harnesses a clustering technique known as Self-Organized Maps to group countries based on their characteristic environmental performance metrics and track progression in terms of shifts within clusters over time. The results support the hypothesis that the inconsistencies in the EPI calculation can lead to misrepresentations of the relative sustainability of countries over time. The proposed framework, which does not rely on ranking or data transformations, enables countries to make more informed decisions by identifying effective and specific pathways towards improving their environmental sustainability.

The State of Smart Cities in China: The Case of Shenzhen

China is at the midpoint of its urbanisation—the largest scale in human history. The recent smart city movement is influencing the discourse and practice of China’s urbanisation, with numerous cities claiming to build smart cities and/or adopting some forms of smart city strategies and initiatives. A so-called ‘latecomer’s advantage’ is being exploited to advance their pursuit for a smart city status, not only to catch up with overseas counterparts, but to overtake them and become international leaders. This local-level enthusiasm strikes a chord with the central government’s strategy of building an ‘innovative nation’ to drive its economic transformation towards a knowledge economy. This converging central-local interest is creating a ‘smart city mania’ across the nation, which, however, has not received due attention in the international literature, and thus deserves critical examination and reflection to inform policy debates. To address this gap, this study investigates the state of smart cities in China, based on a case study of Shenzhen, China’s fastest-growing, experimental city. Shenzhen grew from a fishing village into an international metropolis in 40 years, and has now won a nickname of ‘China’s Silicon Valley’ or ‘China’s smartest city’. This study analyses the state of Chinese smart cities and the pursuit for a smart Shenzhen from the perspectives of the smart city as a concept, as an urban development paradigm, and as an urban regime, drawing upon the international smart city literature. It concludes that a technology-centric approach to smart cities in China, as illustrated by the Shenzhen case, have advanced innovation capacity and economic growth through capitalising on a ‘latecomer’s advantage’. However, this ‘latecomer’s advantage’ may translate into a ‘latecomer’s disadvantage’ for this approach’s lack of institutional adaptation, and for its insufficient attention to social and environmental problems covered under the shiny economic boom. This latecomer’s disadvantage is likely to impact the long-term sustainability of Chinese cities.