Meet the editor

Dr. Sara Shirowzhan is a lecturer at the School of Built Environment (BE), University of New South Wales (UNSW), Sydney, Australia, where she teaches the City Analytics and Construction programs. She also serves as the co-chair of BE's Smart Cities and Infrastructure Cluster. Dr. Shirowzhan works as tomorrow's leading champion for the Chartered Institute of Building (CIOB). Her research interests include sensing technologies,

enhanced GIS, BIM, digital twins, and artificial intelligence in technologies pertinent to BE informatics. She teaches and supervises students at UNSW in the areas of GIS, BIM, digital twins, AI, machine learning, city analytics, urban informatics, smart cities, infrastructure, construction informatics, and other relevant topics. She now serves on the editorial boards of the journals *MDPI* and *Advances in Civil Engineering*. She is also a topic board member of the *ISPRS International Journal of Geo-Information* as well as *Buildings*. Dr. Shirowzhan received her Ph.D. in Geomatics Engineering from the School of Civil and Environmental Engineering, UNSW.

Contents

**Section 1**

in Smart Cities

**Section 2**

**Section 3**

Modeling and Control

Visual Data Science *by Johanna Schmidt*

*by Vung Pham and Tommy Dang*

*by Yunus Egi and Engin Eyceyurt*

**Preface XI**

Web/Dashboard Data Visualisation **1**

**Chapter 1 3**

**Chapter 2 23**

Digital Twins **35**

**Chapter 3 37**

**Chapter 4 57**

Machine Learning and Future of Data Science **73**

**Chapter 5 75**

**Chapter 6 89**

JavaScript Implementation of Scagnostics and Its Applications

Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support

*by Nuno Alpalhão, Miguel de Castro Neto and Marcel Motta*

3D Point Cloud-Based Tree Canopy Visualization for a Smart

Digital Twin of the Mining Shaft and Hoisting System as an Opportunity to Improve the Management Processes of Shaft

Using Trend Extraction and Spatial Trends to Improve Flood

*by Jacob Hale, Suzanna Long, Vinayaka Gude and Steven Corns*

*by Piotr Kalinowski, Oskar Długosz and Paweł Kamiński*

Deployment of Mobile Communication Systems

Infrastructure Diagnostics and Monitoring

## Contents

*by Johanna Schmidt*


Preface

Data science, data visualisation, and digital twins are trending in many disciplines. Appropriate data visualisation and analytics, enabled by data science, are required for informed decision-making in a variety of sectors. If expertise in advanced data analytics techniques are available, advanced data analytics approaches such as Artificial Intelligence (AI) and real-time, web-based, and interactive visualisations

Advanced data visualisation methods, such as 3D, 4D, and so on, as well as dashboards, are great tools for better communication with stakeholders, better understanding and modelling of the current situation, forecasting future trends, and digital twinning of buildings, urban neighbourhoods, infrastructure, and

Professionals, academics, managers, planners, and policymakers have discovered that improved analytical methods of data science, such as machine/deep learning or AI, promise to provide superior insights from data, allowing them to make more educated decisions. In organisations, web-based systems that visualise such insights allow rich interactions among team members, clients, project managers, and

This book highlights established and advanced data science and visualisation technologies, given the benefits of data science, visualisation, and digital twinning. This book is divided into three sections based on the overall themes of the chapters.

Section 1 addresses web and dashboard-based visualisations. In the first chapter of this section, Scanostics features are implemented in JavaScript to illustrate their usability in 2D, 3D, and higher dimensions on the Web. In this chapter, Pham and Dang begin by describing the mathematical definitions of these Scanostics features, then provide installation instructions and implementation scripts for using these features on multivariate data via their GitHub page. In the second chapter in this section, Alpalhao, Castro Neto, and Motta discuss the benefits of developing dashboards for better decision-making in smart cities and show off their developed dashboard for monitoring spatiotemporal mobility patterns and indicators during

Section 2 deals with 3D modelling of trees using point cloud data and digital twinning in the mining industry. In their chapter, Egi and Eyceyurt offer a system that uses machine learning algorithms to reconstruct topography from point cloud data and utilizes 3D tree modelling in such an environment for mobile communications. They also highlight the usability of their sensor fusion technology in smart city applications. In the final chapter of this section, Kalinowski, Dlugosz, and Kaminski suggest a digital twin of a mining shaft and hoisting system utilising BIM models for project

Section 3 demonstrates better flood modelling as well as the predicted future growth of visual data science. In their chapter, Hale, Long, Gude, and Corns present

cities in smart cities and built environments.

are used.

stakeholders.

the COVID pandemic.

management process improvement.
