Smart cities and quality of life: a quantitative analysis of citizens' support for smart city development

PurposeThis study examined a tourism destination, Macao, a fast-progressing smart city under development, vis-à-vis a set of smart city quality of life (SCQOL) domains and verified their effects on citizens' attitudes, perceptions and support for citizen-centric smart city development (SCD).Design/methodology/approachThis study adopted a quantitative approach. In particular, a questionnaire survey was used to collect data from Macao citizens. Factor analysis was used to identify SCQOL domains, while multiple linear regression and cluster analysis were used to achieve the research objectives.FindingsThis study identified five SCQOL domains: smart environment, smart people, smart livelihood, smart economy and economic policy, and smart mobility. Each of the domains had a different influence on citizens' attitudes and support for SCD. Three citizen segments (passive, neutral and enthusiastic supporters) were identified.Practical implicationsThe five SCQOL domains, their effects on citizens' support for SCD and the three citizen segments identified can help implement the appropriate measures to enhance the target groups' SCD. The findings are also of practical value in evaluating the citizen-centric approaches on smart progress in other contexts.Originality/valueThe concept of smart technology has been widely applied to all aspects of city development. The main goal of SCD is to enhance citizens' quality of life. However, most studies have only explored smart cities and quality of life in isolation. Grounded on citizen centrality, this study contributes to the literature on SCD by proposing a new concept of SCQOL, identifying the domains constituting SCQOL and their influence on citizens' support for SCD.

3D CITYGML BUILDING MODELS DEVELOPMENT WITH CROSS-SCALE QUERY DATABASE

CityGML model-based is now a norm for smart city or digital twin city development for better planning, management, risk-related modelling and other applications. CityGML comes with five levels of details (LoD, in version 2.0) of buildings. The LoDs are also known as pre-defined multi-scale models requiring a large storage-memory-graphic consumption than a single scale model. LoD CityGML models are primarily constructed using point cloud measurements and images of multiple systems, resulting in a range of accuracies and detailed model representations. Additionally, it entails several software, procedures, and formats for the construction of the respective LoDs prior to the final result in the CityGML schema. Thus, this paper discusses several issues of accuracy and consistency, proposing several quality controls (QC) for multiple data acquisition systems (e.g. airborne laser systems and mobile laser systems), model construction techniques (e.g. LoD1, LoD2, and LoD3), software (interchange formats), and migration to a PostgreSQL database. Additionally, the paper recommends the importance of minimising implementation errors. A scale-specific unique identifier is introduced to link all associated LoDs, enabling cross-LoD information queries within a database. Proper model construction, accuracy control, and format interchange of LoD models in accordance with national and international standards will undoubtedly encourage and expedite data sharing among data owners, agencies, stakeholders, and public users. A summary of the work and accomplishments is included, as well as a plan for future research on this subject.

Spatial Evolution of Coastal Tourist City Using the Dyna-CLUE Model in Koh Chang of Thailand during 1990–2050

Spatial evolution can be traced by land-use change (LUC), which is a frontier issue in the field of geography. Using the limited areas of Koh Chang in Thailand as the research case, this study analyzed the simulation of its spatial evolution from a multi-scenario perspective on the basis of the 1900–2020 thematic mapper/operational land imager (TM/OLI) remote sensing data obtained through the transfer matrix model, and modified LUC and the dynamic land-use change model (Dyna-CLUE). Over the past 30 years, the expansion of recreation areas and urban and built-up land has been very high (2944.44% and 486.99%, respectively) along the western coast of Koh Chang, which replaced the original mangrove forests, orchards, and communities. Logistic regression analysis of important variables affecting LUC revealed that population density variables and coastal plain topography significantly affected LUC, which showed strong β coefficients prominently in the context of a coastal tourist city. The results of the LUC and logistic regression analyses were used to predict future LUCs in the Dyna-CLUE model to simulate 2050 land-use in three scenarios: (1) natural evolution scenario, where a large patch expansion of agricultural land extends along the edge of the entire forest boundary around the island, particularly the southwestern areas of the island that should be monitored; (2) reserved area protection scenario, where the boundary of the conservation area is incorporated into the model, enabling forest preservation in conjunction with tourism development; and (3) recreation area growth scenario, where the southern area is the most susceptible to change at the new road crossing between Khlong Kloi village to Salak Phet village, and where land-use of the recreation area type is expanding. The model-projected LUC maps provide insights into possible changes under multiple pathways, which could help local communities, government agencies, and stakeholders jointly allocate resource planning in a systematic way, so that the development of various infrastructures to realize the potential impact on the environment is a sustainable coastal tourist city development.

Sponge City Policy and Sustainable City Development: The Case of Shenzhen

The massive construction of buildings has changed the city’s aquatic ecological environment. The aquatic ecological condition of the city has been deteriorated with serious water issues. To coordinate various departments to jointly build the sponge city and improve the water environment, the Shenzhen government formulated the policy note on “Interim Measures for the Construction Management of Shenzhen Sponge City”. This article discusses the impact of the policy note on Shenzhen’s construction of sponge cities. The result shows that the policy is effective from the perspective of the environment. However, in the face of a complex water ecological environment, there are still some deficiencies in the policy. We therefore put forward policy suggestions for the Shenzhen municipal government to better manage its construction of sponge city in the future.

A City for Smart Technology Transformation

The main purpose of this chapter is to present how a smart city is governed, managed, and operated. It describes smart city governance and identifies the special relation the government of the city would have with the citizens as well as communities. In addition, governance considerations related to operations are described, including critical city government challenges. The second important topic in this chapter is the City-Citizens Relations highlighting urban growth, needed investments, and role of smart technologies in the city development. In addition, other issues include strategic goals of smart cities, strategic framework for city governments, and financing smart city projects.

Determinants of Development Achievements on District/City of Central Java in 2019

In 2019, Badan Perencanaan Pembangunan Nasional (Bappenas) awarded Central Java as the province with the best Perencanaan dan Pembangunan Daerah (PPD). However, if it is reviewed at the district/city level, it shows that there are still many areas that have low development achievements. In accordance with the United Nations Development Programme (UNDP) proposal, the Human Development Index (HDI) is used as an indicator of the achievement of district/city development whose calculations are good enough to describe development from both a social and economic perspective. The large difference in HDI between districts/cities in Central Java and the distribution of development achievements are still centered around the provincial capital, namely Semarang City, this indicates the occurrence of inequality in development achievements at the district/city level in Central Java. Because the observations in this study are districts/cities in Central Java, the linkage between district/city causes spatial autocorrelation. Therefore, spatial regression model is used to determine the model that has spatial autocorrelation. This study aims to determine the achievements of development and its determinants in the districts/cities of Central Java in 2019 using the spatial regression analysis method. From the results of the study, it is known that there is a dependence on development achievements between districts/cities in Central Java which is influenced by the regional capacity factor is characterized by PAD and economic growth; operational resource factors characterized by DAU, DAK and technology; and the level of poverty.

Alignment With Sustainable Development Goals

There are diverse ways and concepts to align the development of smart cities with the UN Sustainable Development Goals (SDGs). This chapter is concerned with ways of making sure that any smart city transformation is aligned in full or in part with the SDGs to achieve sustainability. It outlines fundamentals that affect any plan of smart city development. After discussing basic commitments of smart city sustainability, such as the Aalborg and other commitments, the chapter proceeds to distinguish between horizontal, vertical alignment, and blended alignment with SDGs. Emphasis is placed on two relevant SDGs: Goal 11 and Goal 17. An accelerated policy of action is suggested (Smart Rush). Finally, the chapter addresses the modalities of enhancing innovation and participation in smart sustainable cities.