Beyond Data, Towards Sustainability: A Sydney Case Study on Urban Digital Twins

TL;DR

This paper explores Sydney’s urban digital twin, demonstrating its potential for sustainable urban planning through data integration, visualization, correlation analysis, and predictive modeling to support proactive decision-making.

Contribution

It provides a comprehensive case study of a city-scale digital twin, integrating diverse data sources and applying advanced analysis techniques for urban sustainability.

Findings

Digital twin enables spatial ranking of suburbs

Correlation analysis reveals variable interdependencies

Machine learning forecasts traffic crash risks

Abstract

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.