Smart, Sustainable, Resilient, and Inclusive Cities: Integrating Performance Assessment Indicators into an Ontology-Oriented Scheme in Support of the Urban Planning Practice
Abstract
:1. Introduction
- Describe the essential building blocks/key drivers/fundamental concepts of the smart city (classes of the ontology), based on the findings, empirical evidence, and recommendations derived from the international literature.
- Delineate the direct relations between the ontology’s fundamental classes in order to capture the dynamics of their interactions.
- Integrate a unified, multidimensional, global indicator framework into the ontology, thereby embedding the dimensions of smartness, sustainability, resilience, and inclusiveness into the new conceptual model, and, finally, providing a useful planning tool for performance assessment and benchmarking purposes.
2. Methodological Approach for Building the New Ontological Scheme
- Step 1—‘Demarcating the contextual background’: serves as the backbone of the entire research and endeavors to detect and analyze the major challenges and threats—the actual instigators of the colossal smart city wave—that hammer contemporary urban environments.
- Step 2—‘Setting the scene’: delves into the emerging concept of smart cities, tracing its diachronic evolution and spatial expansion in order to provide a comprehensive understanding of its significance and transformative potential. The key elements and defining characteristics of smart cities, such as advanced technologies, data-driven and knowledge-based decision-making processes, as well as sustainable urban practices, are critically examined. Particular focus is placed on the deep impact of technological advancements on fostering smart, sustainable, resilient, and inclusive urban development. This step also explores how technological innovations have revolutionized urban planning and management, thus enabling the shaping of more efficient, equitable, and environmentally friendly cities. Additionally, the most prevalent state-of-the-art technologies and tools, used to effectively implement (participatory) spatial planning exercises in the smart city context, are outlined. Moreover, this step offers a glimpse into real-world examples of smart, sustainable, resilient, and inclusive cities (S2RICs) and inspects noteworthy case studies that embody the principles and goals of S2RICs. The investigation of successful examples uncovers valuable insights into the practical implementation of smart city initiatives and the integration of sustainability, resilience, and inclusiveness goals.
- Step 3—‘Embedding the notions of smartness, sustainability, resilience, and inclusiveness’: includes the process of structuring and deploying a multifaceted, integrated, and comprehensive indicator framework for assessing the performance of smart, sustainable, resilient, and inclusive cities (S2RICs). It digs into the complexities involved in evaluating the effectiveness and impact of urban development initiatives within the S2RIC paradigm, emphasizing the urgent need for a holistic and multidimensional approach to performance measurement. In particular, this step focuses on the formulation of a robust and comprehensive set of indicators that encompass various dimensions of urban performance, including social equity, environmental sustainability, economic vitality, and technological innovation. These indicators aim to capture the nuanced and interconnected aspects of urban development within the S2RIC framework, ensuring a balanced and thorough evaluation process. The proposed indicator framework may serve as a valuable tool for policy makers, urban planners, and researchers, to assess and monitor the progress of S2RIC initiatives effectively, thereby facilitating evidence-based decision-making and fostering continuous improvement. The unified indicator framework is constructed on the basis of a thorough exploration of seven global, widely recognized indicator frameworks, pertinent to the evaluation of urban sustainability performance (see Figure 1), and comprises 597 indicators in total, out of 1096 indicators that were initially inspected; its conceptual design is roughly sketched in Figure A1 of Appendix A.
- Step 4—‘Delving into the ontological reality: provides a general overview of the scientific field of semantics and ontologies and explores various smart city ontological representations. It investigates how ontologies can be employed to capture the intricate interdependencies and relations that exist within the smart city systems, facilitating in this way comprehensive knowledge representation. Moreover, this step offers a structured framework for organizing and managing information through the utilization of ontological models, thus enabling the development of intelligent systems and decision support tools that are better equipped to address urban challenges effectively.
- Step 5—‘Development of an OWL ontology for Smart, Sustainable, Resilient, and Inclusive (S2RIC) cities: is the core of the present paper and thoroughly describes the developmental procedure of the S2RIC Ontology (S2RICO), an ontological representation specifically designed to integrate the assessment of smart, sustainable, resilient, and inclusive cities’ performance into the planning practice. It outlines the conceptual framework and methodology employed to construct the S2RICO and provides valuable insights into the processes and considerations involved in creating a comprehensive knowledge model. The S2RICO may serve as a powerful tool for researchers, policymakers, and urban planners to grasp and overcome the complexities of S2RIC environments and assist them in incorporating data-driven, holistic approaches to urban development.
3. Materials and Methods
- Semantic exploration of the smart city concept by identifying its main key drivers/core components (classes);
- Delineation of their direct interrelationships (object properties);
- Provision of a common formal language and understanding among the different actors;
- Integration of a global, unified indicator framework into the ontological scheme.
3.1. Catalysts for the Development of the S2RICO Scheme
3.2. Steps of Ontological Development
- Demarcation of the domain and scope of the ontology.
- Reuse of existing ontologies.
- Enumeration of key domain-specific terms.
- Definition of classes and class hierarchy.
- Establishment of relations between classes.
- Assignment of properties and their respective values to classes.
- Addition of instances.
3.2.1. Demarcation of the Domain and Scope of the Ontology
- Which specific domain will the ontology cover?
- What are the underlying objectives for its creation?
- What types of questions is the ontology expected to resolve?
- Who constitutes the target audience, and who will be responsible for its ongoing maintenance?
3.2.2. Reuse of Existing Ontologies
3.2.3. Enumeration of Key Domain-Specific Terms
3.2.4. Definition of Classes and Class Hierarchy
3.2.5. Establishment of Relations Between Classes
3.2.6. Assignment of Properties and Their Respective Values to Classes
3.2.7. Addition of Instances
3.3. Creation of Defined Classes—Query and Reasoning
- 1032 classes (the multitude of classes is due to the large number of indicators included in the ontology);
- 46 object properties;
- 50 data properties;
- 68 individuals;
- 9 annotation properties.
4. Discussion
- Ensure the relevance of the represented domain. Stakeholders are an indispensable source of domain knowledge, experience, and expertise. Therefore, their engagement guarantees that the ontology reflects accurately key concepts, relations, and pertinent terminology.
- Enhance usability. Feedback provided from participants regarding the ontology’s structure, terminology, and user interface helps refine the ontology, making it more accessible and functional.
- Foster broader adaptation. Engaged stakeholders are more likely to use and promote the ontology within their networks, organizations, or communities.
- Improve quality. Diverse and broad participation facilitates the identification of ontological errors, inconsistencies, or gaps, thereby boosting its completeness, accuracy, and applicability.
- Challenges in ontology maintenance: maintaining a smart city ontology represents a critical technical aspect that should be taken into account during its design and development process. As urban landscapes continue to evolve, introducing new data, entities, and relations, ontologies must undergo periodic updates and refinements to ensure they remain accurate and relevant [74]. This, in turn, demands considerable effort and resources, which may hinder the efficient utilization of the S2RICO in urban planning.
- Replicability concerns: despite certain commonalities, each city possesses a unique essence, defined by its distinct attributes, specificities, and priorities. Therefore, an ontology that proves to be effective in one urban context may not be directly applicable or entirely suitable in another.
- Limited stakeholder engagement: possible limited stakeholder participation during the update of the S2RICO may result in its failure to capture the diverse needs and perspectives of the broader local community, thereby diminishing its relevance and practicality.
- Privacy and security measures: data collection and sharing within a smart urban ecosystem may give rise to serious concerns regarding the usage of that data and the access to it [75]. Additionally, an ontology’s limited ‘waterproofness’ may expose it to heightened vulnerability, rendering it susceptible to cyber-attacks and potential breaches of sensitive information.
- Coverage issues: the practical application of the S2RICO may unveil concepts or areas that are not sufficiently covered, and thus the proposed model may fail to accurately represent the complexity and diversity inherent in urban systems [48].
5. Conclusions
- Improvement of data analysis: ontological structures facilitate complex analyses, such as executing intricate queries, deducing hierarchies, and understanding relations, thereby revealing insights unattainable through conventional data analysis techniques.
- Facilitation of data discovery and exploration: indicator-oriented ontologies allow users to uncover and explore data based on concepts and indicators pertinent to the domain of interest, therefore making it easier to identify patterns, trends, and valuable insights.
- Enhancement of data quality and reliability: integrating indicators ensures standardized and precise data collection, minimizing errors, ambiguities, and inconsistencies in data handling, by providing clear definitions, semantic relations, and contextual information.
- Boosting of data-driven decision-making: indicator-based ontological representations constitute a holistic and integrative framework for analyzing and interpreting data, supported by standardized metrics and a shared understanding of their significance. Such an approach strengthens evidence-based decision-making, enabling policy makers to evaluate options effectively, identify trends, monitor progress, and make informed choices that support sustainable, resilient, and inclusive urban development.
- Increasing of transparency and accountability: clearly defining indicators and the methodologies for their calculation ensures that data are valid and verifiable, thereby building trust in decision-making processes by reinforcing their transparency and accountability, since these are grounded in credible evidence.
- Facilitation of data integration and interoperability: ontologies provide a shared vocabulary and a common understanding of domain concepts and relations, which allow seamless data exchange and integration across diverse systems and organizations. Populated with a well-established, commonly accepted, standardized set of indicators, S2RICO enables the efficient combination and comparison of heterogeneous data sources, fostering interoperability.
- Support for long-term urban goals: the continuous monitoring and evaluation of key metrics related to smartness, sustainability, resilience, and inclusiveness allow for informed long-term planning. By aligning urban development strategies with environmental, economic, and social objectives, S2RICO helps to identify areas for improvement and ensures the sustained advancement of cities towards sustainability.
- Emphasizing standardization: the creation of a smart city ontology lies in the standardization of involved terms and concepts. Establishing a common language and framework is essential for securing interoperability and seamless communication among diverse smart city systems and stakeholders.
- Prioritizing collaborative efforts: the construction of a smart city ontology is an inherently complex undertaking that necessitates the involvement of various stakeholders, including governmental bodies, urban planners, technology providers, and citizens. Their collective expertise and perspectives are vital for capturing the multifaceted nature of smart cities and crafting a comprehensive ontological representation.
- Ensuring flexibility and scalability: ontologies should be designed to accommodate future developments and changes. With the rapid evolution of technologies and the emergence of new applications, it is imperative that the framework remains adaptable, capable of integrating new concepts and relations without disrupting its existing structure.
- Committing to continuous refinement and updates: a smart city ontology is not a one-time task, but rather an ongoing process that requires constant updates to reflect technological advancements, shifts in urban infrastructure, evolving citizen needs, and changing city dynamics. Regular feedback and active participation from stakeholders play a critical role in maintaining the ontology’s relevance and accuracy.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
GDP | Gross Domestic Product |
ICTs | Information and Communication Technologies |
S2RICO | Ontology for Smart, Sustainable, Resilient, and Inclusive Cities |
S2RICs | Smart, Sustainable, Resilient, and Inclusive Cities |
OWL2 | Ontology Web Language 2 |
W3C | World Wide Web Consortium |
SWRL | Sematic Web Rule Language |
UML | Unified Modelling Language |
E-R | Entity-Relationship |
DL | Description Logic |
ISO | International Organization for Standardization |
DC | Dublin Core |
RDF-S | Resource Description Framework Schema |
FOAF | Friend of a Friend |
BFO | Basic Formal Ontology |
GFO | General Formal Ontology |
DOLCE | Descriptive Ontology for Linguistic and Cognitive Engineering |
SUMO | Suggested Upper Merged Ontology |
Appendix A
Appendix B
Indicators | Innovation | Entrepreneurship | Finance | Employment | Economic Image and Attractiveness | Productivity | Trade | (Inter)national Embeddedness | Urban Agriculture and Food | Transport and Mobility | Technology | Environmental Quality | Environmental Protection and Awareness | Waste | Wastewater | Biodiversity | Water | Sewage/Drainage | Energy | Lifelong Learning, Training and Level of Qualification | Social and Ethnic Plurality | Participation in Public Life | ICT Skills | Culture and Sports | Health and Care | Safety and Security | Housing and Buildings | Education | Social Cohesion and Inclusion | Quality of Life and Well-Being | Participation in Decision Making/Active Citizens | Public Social Services and Budgeting | Urban Planning | Transparent Governance |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
17.1 Total water consumption per capita | ||||||||||||||||||||||||||||||||||
17.2 Freshwater consumption | ||||||||||||||||||||||||||||||||||
17.3 Level of water stress: freshwater withdrawal as a proportion of available freshwater resources | ||||||||||||||||||||||||||||||||||
17.4 Total domestic water consumption per capita | ||||||||||||||||||||||||||||||||||
17.5 Compliance rate of drinking water quality | ||||||||||||||||||||||||||||||||||
17.6 Proportion of households with water saving installations | ||||||||||||||||||||||||||||||||||
17.7 Efficient use of water (use per GDP)—Water productivity | ||||||||||||||||||||||||||||||||||
17.8 Change in water-use efficiency over time | ||||||||||||||||||||||||||||||||||
17.9 Percentage of water loss in the water distribution system | ||||||||||||||||||||||||||||||||||
17.10 Average annual hours of water service interruptions per household | ||||||||||||||||||||||||||||||||||
17.11 Availability of smart water meters | ||||||||||||||||||||||||||||||||||
17.12 Percentage of the city’s water distribution network monitored by a smart water system | ||||||||||||||||||||||||||||||||||
17.13 Percentage of drinking water tracked by real-time, water quality monitoring station | ||||||||||||||||||||||||||||||||||
17.14 Environmental water quality monitored by ICT | ||||||||||||||||||||||||||||||||||
17.15 City freshwater sources monitored using ICT | ||||||||||||||||||||||||||||||||||
17.16 Availability of visualised real-time information regarding water use | ||||||||||||||||||||||||||||||||||
17.17 Number of different sources providing at least 5% of total water supply capacity | ||||||||||||||||||||||||||||||||||
17.18 How many years ahead does the city’s water plan look (e.g., does it analyze the city’s 10 year + needs?) | ||||||||||||||||||||||||||||||||||
17.19 Percentage of city population that can be supplied with potable water by alternative methods for 72 h during disruption |
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Panagiotopoulou, M.; Stratigea, A.; Kokla, M. Smart, Sustainable, Resilient, and Inclusive Cities: Integrating Performance Assessment Indicators into an Ontology-Oriented Scheme in Support of the Urban Planning Practice. Urban Sci. 2025, 9, 33. https://doi.org/10.3390/urbansci9020033
Panagiotopoulou M, Stratigea A, Kokla M. Smart, Sustainable, Resilient, and Inclusive Cities: Integrating Performance Assessment Indicators into an Ontology-Oriented Scheme in Support of the Urban Planning Practice. Urban Science. 2025; 9(2):33. https://doi.org/10.3390/urbansci9020033
Chicago/Turabian StylePanagiotopoulou, Maria, Anastasia Stratigea, and Margarita Kokla. 2025. "Smart, Sustainable, Resilient, and Inclusive Cities: Integrating Performance Assessment Indicators into an Ontology-Oriented Scheme in Support of the Urban Planning Practice" Urban Science 9, no. 2: 33. https://doi.org/10.3390/urbansci9020033
APA StylePanagiotopoulou, M., Stratigea, A., & Kokla, M. (2025). Smart, Sustainable, Resilient, and Inclusive Cities: Integrating Performance Assessment Indicators into an Ontology-Oriented Scheme in Support of the Urban Planning Practice. Urban Science, 9(2), 33. https://doi.org/10.3390/urbansci9020033