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

Ammar Sohail, Bojie Shen, Muhammad Aamir Cheema, Mohammed Eunus Ali, Anwaar Ulhaq, Muhammad Ali Babar, Asama Qureshi

TL;DR

This paper presents a Sydney case study of an urban digital twin (UDT) that fuses real-time and historical data across weather, emissions, crime, and traffic into an interactive spatial-temporal platform. The authors implement a four-component system (data management, analysis, database, viewer) with a Dockerized deployment, and demonstrate sustainability-oriented applications including interactive suburb rankings, automatic correlation insights, spatial autocorrelation analysis, and traffic-risk prediction using environmental data. They propose a formal scoring framework for selecting interesting correlations and apply global and local spatial autocorrelation measures to reveal patterns such as crime and wastewater emissions clustering. The results show potential for data-driven, proactive urban planning by identifying high-risk areas and informing policy interventions.

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.

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

TL;DR

This paper presents a Sydney case study of an urban digital twin (UDT) that fuses real-time and historical data across weather, emissions, crime, and traffic into an interactive spatial-temporal platform. The authors implement a four-component system (data management, analysis, database, viewer) with a Dockerized deployment, and demonstrate sustainability-oriented applications including interactive suburb rankings, automatic correlation insights, spatial autocorrelation analysis, and traffic-risk prediction using environmental data. They propose a formal scoring framework for selecting interesting correlations and apply global and local spatial autocorrelation measures to reveal patterns such as crime and wastewater emissions clustering. The results show potential for data-driven, proactive urban planning by identifying high-risk areas and informing policy interventions.

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.
Paper Structure (19 sections, 4 equations, 6 figures, 4 tables)

This paper contains 19 sections, 4 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Digital Twins and Variants
  4. Spatial Digital Twins and Case Studies
  5. Our Urban Digital Twin
  6. The System
  7. Datasets
  8. Representative Sustainability Applications
  9. Interactive Suburb Rankings
  10. Automated Correlation Insights
  11. Scoring The Insights
  12. Recommending Interesting Insights
  13. Spatial Correlation Analysis
  14. Global Spatial Autocorrelation
  15. Local Spatial Autocorrelation
  16. ...and 4 more sections

Figures (6)

  • Figure 1: A system diagram illustrating the workflow of our urban digital twin.
  • Figure 2: Ranking of suburbs in descending order of transport-related emissions in 2018. Actual emission values can be seen by clicking on a suburb in the map. The Digital Twin allows visualizing the data sets according to various categories and across different years.
  • Figure 3: The digital twin finds the most prominent correlations from underlying data considering the user-selected spatial and temporal ranges. Here, the user-selected area is shown as a green shaded rectangle and the temporal range is 21-Jun-2022 to 21-Jun-2023. Five most prominent correlations are shown and the chart visualises traffic crashes and NO2.
  • Figure 4: Spatial correlation
  • Figure 5: Local autocorrelaton maps (aka Spatial Lag Choropleth Map) with Moran local scatter plot of Crimes
  • ...and 1 more figures