Data visualisation: From a theoretical perspective

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Supervisor: Enrico Costanza
Coursework submitted for user centred data visualisation module

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Motivation —

This project is my attempt to advocate the importance of cognitive science aside from appealing visuals. A great number of visualisation work have focused on the content rather than the theory behind, thus lead to numbers of perception questions, for instance, inappropriate use of visualisations that lead to the increased cognitive workload. Therefore, in this work, I demonstrated human perception theories behind different types of visualisations, through which I wish to provide some insights for future UX designers.

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Prototype —

COVID-19 is sweeping the world with unprecedented speed and creating uncountable destructions to the world healthcare system. It is important to identify the early transmission pattern of the virus for controlling its epidemic. This project aims to explore the virus transmission patterns from domestic spread and import cases. It started with mapping the transmission patterns from Wuhan to the rest of China. After an intense lockdown policy starting to take place, the virus spread was successfully controlled, and the government domain task had changed from preventing local infection to overseas import. The daily updates of overseas imported cases and their arrival inspired the second part of this project. Thus, the presented visualisation illustrates the imported cases linked to their origins. This is achieved by using a Sankey diagram as it clearly shows the origin (on the left side) and arrival customs (on the right side) relationships. The width of the link is proportional to the numbers of cases.

Data is sourced from national health commission of China on the 26th March, 2020.


There is an ongoing trade war between China and the United State, as the tension between two superpowers started to raise up. Huawei, a Chinese multinational technology company, is among the centre of all the disputes. Not only succeed in consumer communication product, but Huawei is also the pioneer in telecommunication, semiconductor and loT disciplinary. This project aims to visualise the business field of Huawei and its subsidiary by network diagrams. From this approach, it hopes to help people gain a better understanding of Huawei, the Chinese tech giant.

The first stage of visualisation was generated by flourish, from which each node represents for a different company, and each line mark stands for the relationships of between two nodes (for example, Company A holds 100% shares of Company B). The fields of the company are distinguished by colour, and the registered capital of the company is represented by node sizes. The width of the connections reflects the percentage of shares between a parent company and a subsidiary.


China has made a breakthrough to boost economic growth in the past few decades, yet it still faces problems raised from unbalanced development across industries. This project aims to provide a novel view on interpreting such rapid development. This involves two subtasks, the first is to plot the time series data of three basic industry growth and their proportion changes, while the other part is to have a closer look of those structural adjustments by visualising categorical data from import and export goods. The presented visualisations argue detailed look over the structure of the national industry by plotting imports and exports of goods and services data. Inspired by previous research on filtering, I designed a selection panel to allow users freely switch between export and import data session within one page without the need to switch between two separate pages.

It is apparent that more than half of the exports are machinery, electrical equipment and textile products, as China is often referred to as the world factory. While for import, food and mineral take up a great percentage. To compare the categorical difference between import and export products, users can simply refer to the colours and size of each cell to depict the difference.