  As urban areas grapple with unprecedented challenges stemming from population
growth and climate change, the emergence of urban digital twins offers a
promising solution. This paper presents a case study focusing on Sydney's urban
digital twin, a virtual replica integrating diverse real-time and historical
data, including weather, crime, emissions, and traffic. Through advanced
visualization and data analysis techniques, the study explores some
applications of this digital twin in urban sustainability, such as spatial
ranking of suburbs and automatic identification of correlations between
variables. Additionally, the research delves into predictive modeling,
employing machine learning to forecast traffic crash risks using environmental
data, showcasing the potential for proactive interventions. The contributions
of this work lie in the comprehensive exploration of a city-scale digital twin
for sustainable urban planning, offering a multifaceted approach to data-driven
decision-making.
