Characterizing the Data Landscape for Digital Twin Integration in Smart Cities

Abstract

The advent of smart cities signifies a paradigm shift in urban management, predicated on the synergistic integration of multifarious data sources. This study presents a rigorous examination of the data milieu inherent to smart cities, focusing on the instrumental role of diverse data streams in shaping the development and functionality of urban digital twins. It traverses the data topography of Internet of Things (IoT) sensors, satellite imagery, social media analytics, urban infrastructure databases, and utility and service logs, delineating their individual and collective contributions to the digital representation of urban landscapes. Addressing the quintessential aspects of volume, velocity, variety, veracity, and value, the research foregrounds the complexity and necessity of effective data amalgamation in a digital twin framework, alongside privacy and real-time processing imperatives. The study's seminal contribution lies in bridging a critical knowledge gap by mapping out the characteristics and interdependencies of these data sources, elucidating the intricate dynamics within smart city ecosystems. Future work aims at the inception of scalable integration platforms capable of accommodating the ever-expanding and intricate fabric of urban data. These platforms must harmonize the burgeoning data flux while maintaining efficacy, thus catalyzing the seamless unification of data streams and enhancing the acumen and agility of smart cities.