Introduction to Visualisation

Transcription

1 Introduction to Visualisation VU Visual Data Science Johanna Schmidt WS 2018/19

2 2 Definition Visualisation enables insight into data Visual information for humans Easier to interpret Allows faster analysis of the data The purpose of computing is insight, not numbers. [R. Hamming, 1962] [1]

3 3 Definition Visualisation rather old discipline Comprises intuitive illustrations (e.g., pie/bar charts, scatter plots), But nowadays also more complex applications for data analysis [1]

4 4 Visualisation disciplines Depending on the type of data that should be visualised Spatial data -> Scientific Visualisation Abstract data -> Information Visualisation

5 5 Visualisation disciplines Depending on the type of data that should be visualised Spatial data -> Scientific Visualisation Abstract data -> Information Visualisation [2] [3]

6 6 Visualisation disciplines Depending on the type of data that should be visualized Spatial data -> Scientific Visualisation Abstract data -> Information Visualisation [4]

7 7 Visualisation disciplines Scientific Visualisation Volume visualisation Flow visualisation Information visualisation 3D nd

8 8 Visualisation disciplines Nowadys, borders are not that well defined any more Information visualisation may also comprise spatial data (e.g., geographic data) Need for integration of abstract data in spatial data

9 9 Visualisation as a dynamic field Visualisation always driven by data and tasks Many different domains (medicine, biology, archeology, astronomy, business analysis, ) Different types of datasets Uses knowledge from different fields Human perception Color theory Geometry, morphology

10 10 Visualisation in computer science Area of computer graphics Generation of visual representations from data Usage of rendering techniques Strong need for data analysis Computer-generated or -supported analysis Data management Storage Processing [5]

11 11 Why Visualisation? Human vision provides high bandwidth which can be used Data getting increasingly complex (size / parameters) Statistical analysis alone may not transport the full picture

12 12 Why Visualisation? Anscombe s Quartet Developed in 1973 by the statistician Francis Anscombe Demonstration to show strength of visual data representation [6]

13 13 Why Visualisation? Anscombe s Quartet Four groups of numbers have identical statistical parameters [6] [6]

14 14 Why Visualisation? Anscombe s Quartet Visual representation of the data quite different [6]

15 [6] 15

16 16 Lecture Focus Information Visualisation [4]

17 17 Lecture Focus Information Visualisation The use of computer supported, interactive, visual representations of abstract data to amplify cognition [Card et al., 1999] [4]

18 18 Visualisation Use Cases Exploration Confirmation Presentation

19 19 Visualisation Use Cases Exploration Searching and analysis No or only minor prior knowledge about the data Find potentially useful information Confirmation Presentation [7]

20 20 Visualisation Use Cases Exploration Confirmation Goal-oriented Examination of a prior defined hypothesis More prior knowledge of the data Presentation [8]

21 21 Visualisation Use Cases Exploration Confirmation Presentation Efficient communication of data features and findings Clear defintion of what to show Often targeted towards external people [9]

22 22 Visualisation Use Cases Exploration Highly interactive Need to visualize many different aspects of the data Challenges for data storage/processing and rendering (visual clutter) Confirmation Presentation [7]

23 23 Visualisation Use Cases Exploration Confirmation Only parts of the data are needed, data can be prepared (e.g., filtered) Visualisation targeted towards goal Interactive Presentation [8]

24 24 Visualisation Use Cases Exploration Confirmation Presentation Only parts of the data are needed, data can be prepared (e.g., filtered) Simple/intuitive visualisation techniques No/minor interaction [9]

25 25 Visualisation Techniques Scatter Plots Bubble Charts Parallel Coordinates Radar Charts Box Plots Violin Plots Venn Diagrams Node-Link Diagrams

26 26 Visualisation Techniques Scatter Plots Bivariate data (2 dimensions) Visual channel: position Intuitive, easy to spot outlier/cluster, correlations, and to identify distributions More dimensions may be added by using additional visual channels For Exploration, Confirmation, and Presentation [10]

27 27 Visualisation Techniques Bubble Charts Scatter plot, but additional dimensions can be shown Uses size for additional attribute -> 3 dimensions Color used for categorical attribute For Exploration, Confirmation, and Presentation [11]

28 28 Multivariate Data Data in which analysis is based on more than two variables Analysis always needs to take several dimensions into account Usually variables have different domains (e.g., numbers, categories) Challenges for analysis and visualisation

29 29 Visualisation Techniques Parallel Coordinates Invented probably 1885, but got popular in 70s (Alfred Inselberg) Align data dimensions as vertical axes Axes need to be scaled accordingly [12]

30 30 Visualisation Techniques Parallel Coordinates Can be used to identify statistical parameters: Parallel lines: positive correlation X-shaped lines: negative correlation Random: no correlation Axes order very important to spot correlations [12]

31 31 Visualisation Techniques Parallel Coordinates Splines instead of lines [13]

32 32 Visualisation Techniques Parallel Coordinates Examples [14]

33 33 Visualisation Techniques Parallel Coordinates Examples [14]

34 34 Visualisation Techniques Parallel Coordinates Examples [15]

35 35 Visualisation Techniques Parallel Coordinates Examples [15]

36 36 Visualisation Techniques Parallel Coordinates Examples [16]

37 37 Visualisation Techniques Parallel Coordinates Examples [16]

38 38 Visualisation Techniques Parallel Coordinates Drawbacks Axes ordering

39 39 Visualisation Techniques Parallel Coordinates Drawbacks Axes ordering Overplotting [17] [18]

40 40 Visualisation Techniques Parallel Coordinates Overplotting Filtering

41 41 Visualisation Techniques Parallel Coordinates Overplotting Filtering Clustering [19]

42 42 Visualisation Techniques Parallel Coordinates Overplotting Filtering Clustering Brushing [20]

43 43 Visualisation Techniques Parallel Coordinates Visualisation technique for multivariate data For Exploration and Confirmation [12]

44 44 Visualisation Techniques Radar Charts Circular alignment of axes Axes need to be scaled Harder to spot correlations Rather used for comparisons [21]

45 45 Visualisation Techniques Radar Charts Possible to use different representations [22]

46 46 Visualisation Techniques Radar Charts Examples [23]

47 47 Visualisation Techniques Radar Charts Examples [24]

48 48 Visualisation Techniques Radar Charts Interpretation may be difficult, because of radial distortion Axes ordering important Should not be used for linear data, axes should be independent For Exploration, Confirmation, and Presentation [21]

49 49 Visualisation Techniques Box Plots Visualisation of statistical parameters Box: 50% of the data Whisker: data area Line: median Circles: outlier For (Exploration,) Confirmation, and Presentation [25]

50 50 Visualisation Techniques Violin Plots Visualisation of statistical parameters For (Exploration,) Confirmation, and Presentation [26]

51 51 Visualisation Techniques Venn Diagrams Visualisation of categorical data Especially useful for overlapping sets For (Exploration,) Confirmation, and Presentation [27]

52 52 Visualisation Techniques Node-Link Diagrams Visualisation of network data Nodes: elements Links: relations between the elements For Exploration and Confirmation [28]

53 53 Visualisation Techniques Node-Link Diagrams Overplotting [29]

54 54 Further Reading InfoVis Community Data Visualisation Catalogue VO & UE Informationsvisualisierung VO & UE Informationsvisualisierung coursenr=188305&semester=2018w

55 55 Hans Rosling Ted Talk

56 56 Visualisation principles Interaction Usage of color Usage of shapes

57 57 Visualisation principles Interaction Usage of color Usage of shapes

58 58 Interaction Especially for Exploration and Confirmation, interactive exploration of the data is needed Visual Analytics is the science of analytical reasoning supported by a highly interactive visual interface [Wong and Thomas 2004]

59 59 Interaction [30]

60 60 Interaction Overview first, details on demand [Shneiderman, 1986] Overview needed, before being able to explore the data Exploration can then lead to details

61 61 Interaction Creating an overview can be challenging due to data size Big Data Data storage/processing Overplotting Limited screen space Needs for aggregation [31]

62 62 Interaction Details by interacting with the data Selection techniques Filtering algorithms Data processing [32]

63 63 Interaction Details by interacting with the data Selection techniques Filtering algorithms Data processing Focus+context While exploring details, context should be preserved

64 64 Interaction Details by interacting with the data Selection techniques Filtering algorithms Data processing Focus+context While exploring details, context should be preserved [33]

65 65 Interaction Details by interacting with the data Selection techniques Filtering algorithms Data processing Focus+context While exploring details, context should be preserved Linking and brushing Apply filtering to all views

66 [34] 66

67 67 References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

68 68 References [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]