GhaffarianHoseini, AmirGhaffarianHoseini, AliBurgess, AndrewPatel, Urva Rajnikant2025-10-152025-10-152025http://hdl.handle.net/10292/19956Background: Urbanization presents significant global challenges, including environmental degradation, strained infrastructure, and rising socio-economic inequalities. New Zealand, with its dispersed urban centres and low population density, faces distinctive planning issues that demand innovative solutions. The Sustainable Development Goals (SDGs), particularly SDG 11, emphasize the importance of creating inclusive, safe, resilient, and sustainable cities. Digital Twin Technology (DTT) has emerged as a promising tool in urban planning by offering real-time data integration and predictive analytics. However, its current applications remain largely focused on infrastructure and environmental monitoring, often neglecting socio-economic dimensions. This research evolved through three critical phases: a detailed exploration of DTT's role in advancing SDG 11 in New Zealand, a qualitative investigation through expert interviews, and the development of an integrated framework combining open-source DT platforms with New Zealand's Living Standards Framework (LSF) for comprehensive urban well-being assessment. Originality: The first phase of this research provided a critical review of DTT’s potential to support SDG 11 in New Zealand, identifying critical gaps in its socio-economic applications and alignment with well-being frameworks. The second phase deepened this understanding through expert interviews with urban planners, policymakers, and technologists, uncovering practical challenges, opportunities, and governance issues in integrating socio-economic dimensions into DTT platforms and shortlisting relevant LSF indicators. Building on these findings, the research developed a novel Python-based dashboard to operationalize the integration of LSF with DTT. This dashboard extends the scope of DTT beyond infrastructure management, enabling comprehensive well-being assessments through advanced features such as ARIMA modelling, correlation analysis, and predictive assessment. Furthermore, in a first-of-its-kind evaluation, open-source DTT platforms, including Eclipse Ditto and FIWARE, were assessed for their ability to integrate socio-economic data. This evaluation highlighted critical technical barriers, such as schema matching, scalability, and data privacy, while also identifying innovative pathways for overcoming these challenges. This progression underscores the originality of this research, offering a unique, multidimensional approach to urban planning that bridges the gap between static socio-economic assessments and dynamic real-time analytics. Aim: The aim of this study is to explore the potential that DTT has for achieving SDG 11, gain deep insights from experts, and develop a dynamic framework that contributes toward an integrated socio-economic and environmental improvement in urban planning. The resulting framework bridges traditional well-being assessment tools with state-of-the-art digital technologies and provides actionable insights for policymakers and urban planners. Methodology: The research adopted a phased, mixed-methods approach. The first phase conducted a systematic review to assess DTT’s role in supporting SDG 11, focusing on its applications in sustainable urban development. The second phase used qualitative analysis of expert interviews with stakeholders, including urban planners and technologists, to identify practical challenges and contextual factors affecting DTT implementation. Key themes included data interoperability, governance, and stakeholder engagement. In the final phase, quantitative analyses of historical and current data (2017–2023) were performed using statistical tools, geospatial mapping, and ARIMA-based time-series forecasting. Insights from the earlier phases informed the development of a Python-based dashboard that integrates socio-economic well-being indicators into DTT platforms. Feasibility testing on open-source platforms such as Eclipse Ditto and FIWARE addressed challenges such as schema matching, scalability, and secure data protocols. Results: The SDG-focused analysis revealed the untapped potential of DTT in addressing urban challenges in New Zealand, with significant gaps in its socio-economic applications. Expert interviews highlighted critical barriers, including fragmented data systems, limited stakeholder alignment, and governance challenges, while also identifying opportunities for integrating well-being metrics into DTT platforms. The framework developed in the final phase demonstrated the feasibility of bridging these gaps. The Python-based dashboard visualized regional disparities in well-being metrics, such as air quality and commuting times, and revealed interdependencies among indicators, such as the link between internet access and education outcomes. Benchmarking features identified underperforming regions and set measurable targets for improvement. Findings: This research demonstrates the transformative potential of integrating socio-economic well-being metrics into DTT platforms to enhance urban planning and address SDG 11. The findings emphasize the importance of stakeholder engagement and governance frameworks, as identified in the expert interviews, in overcoming barriers to integration. The framework and its dashboard represent a replicable model from the integration of well-being metrics in real-time urban planning tools, offering policymakers actionable and strategic insights for informed decision-making. While there were interoperability, data privacy, or scalability issues, it highlights the adaptability of the proposed integrated framework to the specific urban context of New Zealand. Based on the global trend, qualitative interviews, and deeper quantitative analysis, the research makes a holistic and significant contribution to the ongoing discourse on urban development, particularly in the areas of urban resilience, inclusivity, and sustainability.enThesisOpenAccess