Data Visualisation

1. HCI 0283 Lecture 1 Introduction Data Visualisation

2. Timetable • Lectures Mondays 10:00 – 11:40 Lab R316 • Labs • Tölvunarfræðin Thursdays 10:00 – 11:40 Lab K203 • Auðlindafræðin Thursdays 12:25 – 14:15 Lab K203 • N.B. These may change. Check Stéfaniaregularly.

3. Assessment • One essay coursework • 15% of the final mark • One data analysis coursework • 15% of the final mark • One 3-hour exam • 70% of the final mark • You must gain a passing grade in the coursework AND a passing grade in the exam to pass this module

4. Textbooks • The main textbook for the course is Information Visualisation (Spence) • An electronic copy of this is available in Blackboard • You will also be expected to read a selection of journal papers and other online resources which will be identified during the lectures

5. Course Outline

6. What is Visualisation? Visualise: (vb) to form a mental image or vision of something; to imagine or remember as if actually seeing. • Visualisationis therefore a cognitive ability performed by humans • It produces an internal mental image (a mental model or internal model) that allows the human to gain insight and understanding • The study of data visualisation is the study of how to present data in such a way that humans viewing the data can easily understand what they are looking at

7. What is Visualisation? • Computers are only used as tools to help the visualisation process by the use of visualisation tools • Information visualisation does not therefore need computers • There are many historical cases where hand-drawn visualisations have had a major public or political effect • Maps are a form of information visualisation

8. Maps as Visualisations • Maps are excellent cases of information visualisations • Large amounts of information can be conveyed in a small area • Relative and absolute location • Population • Individual objects • Links between objects…

12. Maps as Visualisation • The above maps could have been created (in theory) any time in the past 1000 years • Computers allow us to create maps (=visualisations) with active content • GIS (Geographical Information Systems) are large-scale mapping programs designed to show geographical data in many ways

15. Maps as Visualisations • Sometimes such a large amount of data is available on a map that some human insight is required to make sense of it • Consider the London Underground map…

17. Visualisation • When you look at the Underground map you don’t normally look at the entire thing • You focus on the parts relevant to your particular journey and ignore the rest • The relevant parts are your start point, destination, and a viable route • This may be memorised in terms of direction, colour and interchanges • It is much easier to store this smaller visualisation in memory that to store the entire map • A journey might be memorised as south on the black line then west on the red line • This then becomes our internal model of the journey, which we can access at any time

18. Visualisation • This internal model is sometimes described as a cognitive map to distinguish it from a physical map on a piece of paper or a wall • Strictly speaking, the familiar London Underground Map isn’t a map at all, it’s a diagram If you're going underground, why do you need bother about geography? It's not so important. Connections are the thing. Harry Beck, 1933 Designer of the Underground Diagram

20. Visualisation • By distorting the geography but maintaining the topology – the connections – Beck made his diagram far easier to read than a geographical map of the train lines • This principle is now in common use worldwide for travel maps

22. Scientific Visualisation • Scientific visualisation is related to, but distinct from, information visualisation • This generally requires a physical thing to be shown, e.g. stress levels in a piece of metal • In information visualisation we deal with more abstract items such as price, score, rate of change and so on which are more important than physical appearance

23. Data Types • The data to be visualised can be varied • House prices and details • Wind speeds • Train stations and networks • … • Data may not just be numerical, it may be • Numerical – e.g. integers or floats • Ordinal – e.g. days of the week • Categorical – e.g. items which have names but no categories, such as horse, dog, sheep • Different types of visualisations are appropriate for different data types • Insight and knowledge are normally needed as the information relates to a task

24. Coffee Time!

25. More Historical Examples • There are many instances where good data presentation has had an influence on science, business or politics • We will look at several of them in more detail

26. Taxation • Sir Edward Playfair wished to show that the British Empire was being taxed far more than any of the other empires of the day • Rather than producing pages of numbers he illustrated this by showing three values – population, gross national product and tax gathered

28. Taxation • The slope of the joining line for the British Empire clearly stands out • A simple diagram makes the differences obvious – you do not need to be an economist to see it • Is slope a good way of drawing attention to something?

29. Napoleon’s March • In 1812 Napoleon marched 422,000 soldiers into Russia and returned with only 10,000 of them • His mapmaker, Monsieur Minard, created a visualisation to show how the size of the army varied as it crossed eastern Europe • It shows the number of soldiers, the location, the dates and the temperatures during the march • This is one of the most famous historical visualisations and has been reworked many times • Some ‘re-visions’ are more useful than others…

31. Integrating Temperature

32. Highlighting Time

33. Extending to 3D

34. The ‘Human Touch’

35. The 1854 Cholera Epidemic • An outbreak of cholera in London in 1854 was investigated by the city’s then-chief medical officer Dr John Snow • Snow had a hypothesis that cholera was caused by contaminated water, and to show this drew a dot map which located every death and every water pump in the area. • It became clear that the deaths were clustered around a single pump; when the pump was deactivated the outbreak ceased • Similar maps are nowadays used in criminal investigations

37. MBH98 Reconstruction • In 1998 Mann, Bradley and Hughes presented data on a reconstruction of northern hemisphere temperature changes over the last thousand years • The resulting ‘hockey stick graph’ showed a clear increasing trend dating from about 1900, which has been interpreted as indicative of anthropogenic climate change • This graph has been one of the most influential data presentations of the twentieth century • It was presented as supporting evidence in the Third Assessment report by the IPCC • There is still much controversy about the accuracy of the data used to generate it but, world-wide, the graph itself is probably the best-known ‘evidence’ of global warming

38. The “Hockey Stick”

39. Word clouds and tag clouds • Visualising textural information is a difficult problem • How do you tell whether a document is of interest to you without reading it? • Historically, text has been summarised using textural abstracts and executive summaries • These can be misleading if not written well • A modern visualisation approach is to identify the most common meaningful words in a document and then to display them graphically as a word cloud • A similar approach, called a tag cloud, is used to identify tagged online materials such as photos • In this case clicking on a tag will take you to all objects labelled with that tag

40. Hávamál (Auden Translation)

41. Selection • It is important to select the information that is relevant to the task. • What do we need to know about selection? • Can it take place automatically? • Do we sometimes want to suppress information? • Is it fundamental to information visualisation? • More on this in lecture 2

42. Representation • A visualisation tool must represent abstract things in some way – slope, colour, trends, connectivity and so on • Many encoding methods are possible, but which are useful? • Can they be combined? • How do we handle 20 attributes rather than 2? • More on this in lectures 3 and 4

43. Presentation • The data must be laid out in some way – Beck distorted geography, Playfair lined his empires up • Often there is more data than can be displayed at once on the limited area of a screen • How do we solve this problem? • More on this in lecture 7

44. Scale and Dimensionality • The previous examples used relatively little data, but in many modern situations the volume of data can be huge • Dimensionality is also important • How many features can be incorporated? • What techniques are available for high dimensionality? • More on this in lectures 5 and 7

45. Interaction • The previous examples do not allow us to interact with the data, to explore it or rearrange it to gain greater insight into it • This is where computers are particularly useful! • Many interactive visualisation tools now use these techniques • More on these in lectures 2 and 5

46. Externalisation • At the start of the lecture we talked about an internal model in the mind of the user • What the user sees on a computer screen is the externalisation of the data • How data is externalised is important to the success of any visualisation • More of this in lecture 4

47. Mental Models • Visualisation is a human activity supported by computers • Visualisation takes place in the mind of the viewer, so we need to understand how this takes place • It is useful to have a model of how human beings visualise information • More on this in lecture 6

48. Invention • All of the visualisation methods we will look at had to be invented • Inventions often lead to the investigation of theoretical frameworks which will eventually lead to new inventions • Mainly, though, the success of a visualisation depends on the skill and inventiveness of the designer • Before the use of computers, the designer of a visualisation had to select, encode and present all of the raw data • All the viewer did was look at the result • With computers, the designer of a visualisation tool must allow the viewer to interact with the data by choosing the selection, encoding and presentation

49. Summary • What is visualisation? • A cognitive action performed by humans • Does not require a computer • Historical visualisations • Good visualisations have often had a significant effect on political, economic and social affairs • Significant issues • Selection, representation, presentation, dimensionality, interaction, externalisation and invention

50. Coming Soon… • Next lecture: Data Rearrangement • Homework: Read chapter 1 of Information Visualisation (Spence)