1. Introduction
Cities are currently acting as engines for economic development, providing opportunities for expansion while dealing with several internal and external challenges, such as environmental hazards, resource depletion, high energy consumption, and high production of CO
2 and greenhouse gas emissions, which results in pollution [
1]. Additionally, the number of people living in cities is continually growing; by 2050, 68% of the world’s population is predicted to reside in cities [
2]. Rising populations increase other major concerns, such as community safety, the efficiency of mobility, and poor quality of life, especially in districts and neighborhoods.
The neighborhood or district is an important scale of development to consider since it is the core of cities. If neighborhoods suffer then cities subsequently suffer, moreover, neighborhoods need attention in order to develop livable and sustainable environments. Moreover, smart neighborhoods improve the social networking and capital of the populace [
3]. Smart cities can be developed at different geographical scales, starting from the neighborhood, and should lead to the enhancement of quality of life and neighborhood livability [
4]. The integration and growth of smart cities and smart neighborhoods can therefore help to alleviate the effects of urbanization and the environmental risks that most cities, especially in developing countries, are currently facing. This requires the development of new, functional, and user-friendly services and technologies, especially in the fields of energy, transportation, and information and communication technology (ICT). These solutions also need a combination of approaches in terms of advanced technology solutions, research, innovation, and deployment [
5].
Several urban computing strategies can be deployed to support the systematic development of smart neighborhoods. Urban computing involves the use of smart technologies and the internet of things (IoT) as tools to reach sustainability and progressively foster resilient neighborhoods. Furthermore, it is essential to know the neighborhoods and citizens’ needs to implement the right strategies and methods of smart technology to promote sustainability. Additionally, it is not only about smart technologies but also about the collection of relevant data that can guide us to solutions [
6]. Moreover, the considerations should include how to manage the data and connect technology with neighborhood challenges to resolve them. Urban computing is generally understood to be a technique for acquiring, integrating, and analyzing large-scale heterogeneous data created by a range of resources inside urban environments, such as sensors, gadgets, cars, structures, and humans [
7]. It is relevant to urban informatics, which involves the application of information and communication technologies in managing and understanding urban areas [
8].
In the context of Saudi Arabia, it has been deduced from various literature reviews that there is a lack of a smart and sustainable framework for Saudi neighborhoods, especially in the use of smart technologies to support sustainable development. Recent studies have examined social sustainability in Saudi neighborhoods [
9], greenspace usability [
10], heritage management [
11], the transformation of housing typology [
12], and life-cycle-based strategic framework for smart sustainable cities at the city scale [
13]. Thus, the focus of this article is to discuss the development of a smart and sustainable neighborhood framework by applying the concept of urban computing and various smart and sustainable standards. The development of the proposed framework goes through a process; firstly, starting by exploring multiple strategies and other frameworks from various literature reviews on the concept of urban computing. Then secondly, examining and analyzing sustainable cities or neighborhood standards in addition to smart standards. Consequently, the proposed framework is tested for its success in maintaining an economic, environmental, and social neighborhood by applying it to the Alkhaledia district in Jizan. The framework has the goal of creating an inclusive neighborhood, with a focus on having social connectivity and environmental protection. This goal, which is relevant to the current need of Saudi districts, applies to other communities. More importantly, the Saudi Arabia Vision 2030 features focus areas of “vibrant society”, “thriving economy”, and “ambitious nation”, which establish the need for more sustainability-oriented neighborhoods that facilitate smart technologies to support sustainability and its measures.
However, the proposed framework with its context of smart technologies and dependence on data raises many challenges and limitations. Having a data-driven framework can have multiple risks, such as data security, privacy, and acquisition. Since the collection of data is restricted and confined to only what resources are available, the success of the framework depends on how much data about the neighborhood is accessible. Furthermore, having the framework depends on citizens’ willingness to provide data which can cause difficulties in obtaining the data.
The analysis of this article focuses on the four elements of urban computing: urban sensing, data management, data analytics, and delivery of services. The concept of urban computing will be used as a structured procedure to implement smart aspects to transform neighborhoods into smart neighborhoods, and an assessment of sustainability by using sustainable indicators to measure social, economic, and environmental aspects of a neighborhood. This paper will also explore how to reflect this on current Saudi neighborhoods by studying literature reviews and a case study. The main objectives of this article are to:
Investigate the sustainability standards and smart indicators to be used in smart sustainable neighborhoods;
Develop a smart and sustainable urban computing framework for neighborhoods;
Assess Alkhaledia district, Jizan, as a case study, using the proposed framework.
5. Discussion
This article is mainly concerned with developing a framework based on the idea of urban computing, and the process of developing the framework went through multiple stages of thorough analysis to make the SSUCF. Therefore, the results of each table interpret different meanings and significance. The data on the four smart and sustainable standards, the interpretation of the urban computing concept, and their analysis support the theory that urban computing has great significance in the enhancement of neighborhood quality of life, sustainability, and smartness, as highlighted by Keshavarzi et al. [
4] and Alshuwaikhat et al. [
6]. A few of the chosen indicators fell under all the urban computing categories, while others had only certain urban computing categories that can relate to the indicator. Therefore, this proves that urban computing can be used as a structured format for the development of sustainability and smartness.
The initial framework for SSUCF contains the three main chosen dimensions of “people”, “environment”, and “prosperity”, which were deduced from the filtered smart and sustainable standards while considering the local context and alignment with the Saudi 2030 vision. The table has a total of 42 indicators that relate to the environmental, social, and economic aspects that a neighborhood should focus on, to ensure the success of the framework. Furthermore, this table demonstrates the correlation between urban computing and the three chosen dimensions, which also illustrates how critical the sequence of sensors, data management, data analysis, and services are (in that order) for the indicators to be effective and efficient. Each indicator has its essential urban computing layer, which is analyzed by various literature reviews and other sustainable standards. Abusaada and Elshater [
28] developed a similar framework of urban design for smart sustainable cities. However, their framework focused on two dimensions—urban economy and placemaking—which did not explicitly consider the environmental dimension.
Table 4,
Table 5 and
Table 6 show, as stated in
Section 3.2.1, how sensors are the first detectors for data that help us attain the information to procure sustainable and efficient services [
29]. This shows how important attaining data by sensors is in the urban sensing category, where 62% of the 42 indicators needed the sensory system. Moreover, the data management category showcases the importance of data being neatly organized by an indexing structure as it integrates both spatiotemporal and textual information for efficient data analytics. This finding shows that the category is critical to the achievement of the goal of the framework by having 90% of the 42 indicators including urban data management.
Urban data analytics is the use of big data analytics to analyze relations of data for the success of urban computing in neighborhoods [
30,
31,
32,
33,
34], and 78% of the indicators may need big data analysis for the efficient use of indicators. Finally, 90% of the indicators are relevant to the delivery of services category. This shows that as part of having an inclusive and higher quality of life neighborhood, we must focus on the needs of residents and how our technology and smart gadgets can be of help for solving problems efficiently. The SSUCF has multiple limitations, some of which are how resources are limited and how ready decision-makers are for a smart-driven neighborhood. This is in terms of awareness of developers and the availability of resources for the technical success of the neighborhood projects.
Section 4.2 shows the findings of the case study, in the Alkhaledia district, Jizan. The section presents the tailored indicators for Alkhaledia from the developed framework, since the Alkhaledia project has sustainable and smart objectives, some indicators are met, however, they need some alteration to fit into the SSUCF. This was developed by analyzing indicators by the criteria of “must have”, “should have”, and “nice to have”, and relying on the project characteristics mentioned in the literature review case study section, as to whether these indicators are found or not, and if found, it could be enhanced into the structured format of urban computing categories. If an indicator is not found and if it is a must-have for a smart sustainable neighborhood, then it will undergo the thorough process of urban computing categories to form a successful indicator that serves its residents, economy, and environment. Although this filtering of indicators yields 27 indicators, it is partly a result of the lack of data regarding the Alkhaledia districts and their policies. Therefore, the limitation of resources and data creates a constrained development of indicators for enhanced development of the SSUCF.
Another set of challenges regarding the use of data in this framework are the data-specific issues, which can fall under data acquisition, privacy, and security. Since data and computing together form the critical component of urban computing, it must be executed efficiently by considering all of its components. The most crucial factor is data privacy and security since smart applications not only gather a variety of information from people and their social networks that are sensitive to privacy [
35,
36], but also operate facilities and have an impact on people’s lives. Furthermore, data acquisition is another challenge, as the collection of data from sensors creates a large volume of information that might be difficult to analyze with redundant data.
The SSUCF focuses on social, environmental, and economic factors. The “people” dimension relates to the socially important indicators that ensure a better quality of life.
Table 4 shows the first three indicators of the people dimension in SSUCF. Furthermore, this dimension accounts for the residents’ related data and what we can do when we acquire these data to better serve the people which in turn demands a security system that ensures the protection of the community’s data, hence the cybersecurity indicator shown in
Table 4. As mentioned in the literature review, there are precautions to ensure data security, and that is important, as a technology-based system is more prone to cyber risks, therefore, addressing these sensitive topics is critical to ensure better performance of the smart and sustainable neighborhood. These indicators are enhanced into a comprehensive indicator by plugging in the urban computing categories. The “environmental” dimension, which was missing in Abusaada and Elshater’s framework [
28] includes a set of indicators that promotes environmental sustainability. Though energy and water consumption and recycling indicators are covered in Alkhaledia, carbon emissions are not covered. Carbon emissions are regarded as one of the important environmental indicators since they are related to climate change [
37,
38,
39].
The final dimension of this framework is “prosperity”, which encompasses the economical aspect of the neighborhood that secures longevity and prosperity in the district. A neighborhood built with this framework can ensure the complete comfort of residents to live a sustainable and smart life. These objectives ensure the availability of services to enhance the quality of life in the Alkhaledia district.
Table 6 shows some of the indicators that are found in the “prosperity” dimension, chosen by analyzing the report documents available from the Alkhaledia district and selecting the appropriate indicators from the selected standards to support the neighborhood’s prosperity. Consequently, having urban computing layers enhances the production of this dimension, with the help of a literature review to understand how technology can elevate the prosperity of the neighborhood. For instance, affordable housing is a critical consideration when developing this framework, and since the Alkhaledia district has the design objective of constructing an affordable housing residential area, urban computing can be plugged into this neighborhood and framework.
6. Conclusions
This article aims to establish neighborhoods that support a healthy environment, a strong community, and growing economic value by having a smart and sustainable neighborhood framework, facilitated using intelligent concepts and technologies, such as urban computing. Creating smart sustainable neighborhoods is crucial to addressing the ever-changing demands of Saudi citizens who live in urban areas. A comprehensive and integrated approach to a neighborhood’s social, economic, and environmental values is lacking in Saudi neighborhoods. As a result, it creates a variety of negative effects, including poor environmental protection, poor quality of life, and insufficient services and amenities. More importantly, Saudi Vision 2030 has three main areas of focus: “vibrant society”, “thriving economy”, and “ambitious nation”, which emphasize the need for neighborhoods that are more environmentally conscious and has smart technologies to support sustainability and its measures. To build a brighter future for the country and its people, the 2030 Vision is used as the foundation for the project’s proposed framework and acts as its primary goal. Furthermore, an application of this framework was created in the Alkhaledia district, for further advancement of the framework.
Two key elements are crucial to the creation of this framework: the concept of urban computing, which is the identification and analysis of the four subcategories of urban computing: urban sensing, urban data management, urban data analytics, and the delivery of services; and selecting the appropriate indications from the four sets of standards which are ITU, ETSI, and SDG 11 for sustainable cities and communities. These two elements influenced how the SSUCF is developed. Consequently, a tailored framework is developed to be implemented in the Alkhaledia district to enhance the effectiveness of this framework. This was accomplished by having filtered indicators that aim to improve the Alkhaledia district. This framework development shows that urban computing may be used as a basis for support, together with smart and sustainable standards, to establish a framework for a neighborhood that is both smart and sustainable. Additionally, this framework can be used in other Saudi neighborhoods to improve the quality of life, environmental protection, and economic values when accommodating the relevant data.
Furthermore, urban computing involves more than just smart technologies, it also considers how to gather the relevant data that will help us find solutions, manage that data, and link technology with local problems to find solutions. This serves the residents, helps the economy, and supports the environment. Since many Saudi neighborhoods do not use smart technologies to assist sustainable growth, this framework supports tackling this gap. Additionally, this analysis shows that urban computing could serve as a constructive and organized format for the use of smart technologies to serve the sustainability of the community, economy, and environment. These results were found by a matrix table checking how many smart or sustainable indicators can fall under urban computing categories.
The final finding is the application of the SSUCF in the Alkhaledia district, Jizan. This framework is tailored to the context of Jizan and its available data; therefore, to achieve these smart and sustainable goals, a variety of strategies must be used, along with advanced technology solutions, research, inventions, and deployment found in this framework. Various indicators were chosen to improve the Alkhaledia neighborhood and build a more comprehensive and inclusive district. This was achieved by first studying the current state of Alkhaledia and having indicators enhance the present elements of Alkhaledia, and constructing it if it does not yet exist.
Moreover, this framework has limitations that are related to data collection, data privacy, data security, and data analysis. Even though data collection is made easy with sensors, it could create challenges since it might collect redundant and high-volume data. Furthermore, data privacy and security are vital concerns, since all relevant data of citizens are collected to create an enhanced quality of life. Furthermore, other limitations of this article include the filtration of indicators that resulted from a lack of data regarding Jizan policies and the neighborhood’s readiness for a technology-based infrastructure and development. Additionally, Alkhaledia has different sectors to focus on, and this demands integration between stakeholders and investors by involving all personal data to make decision-making more cohesive for smart and sustainable neighborhoods.
Future studies might explore enhancing the indicators concerning residents’ quality of life—that is, more comprehensive indicators should be considered and extended to address more neighborhood sectors, subject to the availability of resources and data. This article is important for building a smart and sustainable neighborhood, especially in the Saudi region where it is most needed. Furthermore, despite this development of the framework being tailored to the Alkhaledia district, it can be implemented in other neighborhoods and regions since it has room for further enhancement for a more integrated, smart, and sustainable neighborhood framework. Nonetheless, with the aid of smart technology, the SSUCF can foster communities that are inclusive in all social, economic, and environmental aspects. Future studies can explore how the concepts of Kate Raworth’s “doughnut economy” or “circular economy” can be integrated into the development of smart sustainable indicators [
40,
41]. These indicators can create not only a guideline heuristic for different cities to start to think about sustainable initiatives, but might also address sustainable macro perspectives.