Visualization Foundations IDV 2015/2016

Transcription

1 Ineracive Daa Visualizaion 04 Visualizaion Foundaions IDV 2015/2016

2 Noice n Auhor João Moura Pires (jmp@fc.unl.p) n This maerial can be freely used for personal or academic purposes wihou any previous auhorizaion from he auhor, provided ha his noice is kep wih. n For commercial purposes he use of any par of his maerial requires he previous auhorisaion from he auhor. 2

3 Bibliography n Many examples are exraced and adaped from Ineracive Daa Visualizaion: Foundaions, Techniques, and Applicaions, Mahew O. Ward, Georges Grinsein, Daniel Keim, 2015 Visualizaion Analysis & Design, Tamara Munzner,

4 Table of Conens n The Visualizaion Process in Deail n Semiology of Graphical Symbols n The Eigh Visual Variables n Hisorical Perspecive n Taxonomies 4

5 Ineracive Daa Visualizaion The Visualizaion Process in Deail 5

6 The Visualizaion Process in Deail 6

7 The Visualizaion Process in Deail n Daa preprocessing and ransformaion Process he raw daa ino somehing usable by he visualizaion sysem. The firs par is o make sure ha he daa are mapped o fundamenal daa ypes The second sep enails dealing wih specific applicaion daa issues. n Mapping for visualizaions Decide on a specific visual represenaion. This requires represenaion mappings: geomery, color, and sound, for example. n Rendering ransformaions. The final sage involves mapping from geomery daa o he image This sage of he pipeline is very dependen on he underlying graphics library. 7

8 The Visualizaion Process in Deail n Daa preprocessing and ransformaion Process he raw daa ino somehing usable by he visualizaion sysem. The firs par is o make sure ha he daa are mapped o fundamenal daa ypes The second sep enails dealing wih specific applicaion daa issues. n Mapping for visualizaions Decide on a specific visual represenaion. This requires represenaion mappings: geomery, color, and sound, for example. n Rendering ransformaions. The final sage involves mapping from geomery daa o he image This sage of he pipeline is very dependen on he underlying graphics library. 7

9 Mapping for visualizaions n Mapping for visualizaions Decide on a specific visual represenaion. This requires represenaion mappings: geomery, color, and sound, for example. 8

10 Expressiveness and Effeciveness n Expressiveness An expressive visualizaion presens all he informaion, and only he informaion Mexp = The informaion ha we acually display o he user / informaion we wan o presen o he user 0 Mexp 1. If Mexp = 1, we have ideal expressiveness If he informaion displayed is less han ha desired, hen Mexp < 1. If Mexp > 1, we are presening oo much informaion. Expressing addiional informaion is poenially dangerous, because i may no be correc and may inerfere wih he inerpreaion of he essenial informaion. 9

11 Expressiveness and Effeciveness n Effeciveness A visualizaion is effecive when i can be inerpreed accuraely and quickly and when i can be rendered in a cos-effecive manner. Effeciveness hus measures a specific cos of informaion percepion. Meff = 1 / (1 + imeinerpre + imerender). 0 < Meff 1. The larger Meff is, he greaer he visualizaion s effeciveness. If Meff is small, hen eiher he inerpreaion ime is very large, or he rendering ime is large. If Meff is large (close o 1), hen boh he inerpreaion and he rendering ime are very small. 10

12 Expressiveness and Effeciveness Task: presening he car prices and mileage for 1979 Mexp(a) Mexp (b) 11

13 Expressiveness and Effeciveness The informaion in (b) can be inerpreed more accuraely or more quickly han ha in (a) for some quesions. For example, which car has he bes mileage? However, if we ask which car has he bes mileage under $11,000, Figure 4.3(b) is less efficien. 12

14 Ineracive Daa Visualizaion Semiology of Graphical Symbols 13

15 Semiology of Graphical Symbols n The science of graphical symbols and marks is called semiology. n Every possible consrucion in he Euclidean plane is a graphical represenaion made up of graphical symbols (diagrams, neworks, maps, plos, and oher common visualizaions). n Semiology uses he qualiies of he plane and objecs on he plane o produce similariy feaures, ordering feaures, and proporionaliy feaures of he daa ha are visible for human consumpion. 14

16 Symbols and Visualizaions n (a) is universally recognizable. Such images become preaenively recognizable wih experience. n (a) is perceived in one sep, and ha sep is simply an associaion of is meaning 15

17 Symbols and Visualizaions n (b) requires a grea deal of aenion o undersand; n he firs seps are o recognize paerns wihin (b) and idenify he major elemens of he image; wih he second idenifying he various relaionships beween hese. 16

18 Semiology of Graphical Symbols n Discovery of relaions or paerns occurs hrough wo main seps: The firs is a mapping beween any relaionship of he graphic symbols and he daa ha hese symbols represen. any paern on he screen mus imply a paern in he daa. If i does no, hen i is an arifac of he seleced represenaion (and is disurbing). Similarly, any perceived paern variaion in he graphic or symbol cogniively implies such a similar variaion in he daa. Any perceived order in graphic symbols is direcly correlaed wih a perceived corresponding order beween he daa, and vice versa 17

19 Feaures of Graphics n Graphics have hree (or more) dimensions. n Every poin of he graphic can be inerpreed as a relaion beween a posiion in x and a posiion in y. The poins vary in size, providing a hird dimension or variable o inerpre. 18

20 Feaures of Graphics n Graphics have hree (or more) dimensions. n Every poin of he graphic can be inerpreed as a relaion beween a posiion in x and a posiion in y. The poins vary in size, providing a hird dimension or variable o inerpre. 19

21 Feaures of Graphics n Graphics have hree (or more) dimensions. n Every poin of he graphic can be inerpreed as a relaion beween a posiion in x and a posiion in y. The poins vary in size, providing a hird dimension or variable o inerpre. 20

22 Rules of Graphics n The aim of a graphic is o discover groups or orders in x, and groups or orders in y, ha are formed on z-values; n (x, y, z)-consrucion enables in all cases he discovery of hese groups; n Wihin he (x,y,z)-consrucion, permuaions and classificaions solve he problem of he upper level of informaion; n Every graphic wih more han hree facors ha differs from he (x, y, z)- consrucion desroys he uniy of he graphic and he upper level of informaion; n Picures mus be read and undersood by he human. 21

23 Ineracive Daa Visualizaion The Eigh Visual Variables 22

24 Spaial arrangemen of marks n For he mos par, all graphic primiives will be ermed marks. n One way o encode daa for display is o map differen daa values o differen marks and heir aribues. n However, marks by hemselves do no define informaive displays, since all he marks would simply obscure all previously drawn marks; i is only hrough he spaial arrangemen of marks ha informaive displays are creaed. n Once he layou and ypes of marks are specified, hen addiional graphical properies can be applied o each mark. Marks can vary in size, can be displayed using differen colors, and can be mapped o differen orienaions, all of which can be driven by daa o convey informaion. 23

25 Eigh visual variables n eigh visual variables: posiion, shape, size, brighness, color, orienaion, exure, moion I is imporan o remember ha he resul will be an image ha is o be inerpreed by he human visual sysem 24

26 Eigh visual variables: Posiion n The firs and mos imporan visual variable is ha of posiion, he placemen of represenaive graphics wihin some display space, be i one-, wo-, or hreedimensional. n Spaial arrangemen of graphics is he firs sep in reading a visualizaion: The maximizaion of he spread of represenaional graphics hroughou he display space maximizes he amoun of informaion communicaed, o some degree. Wors case posiioning scheme maps all graphics o he exac same posiion Bes posiioning scheme maps each graphic o unique posiions, such ha all he graphics can be seen wih no overlaps. 25

27 Eigh visual variables: Screen resoluion 26

28 Eigh visual variables: Screen resoluion n geo-referenced accidens beween 2001 and 2013 in US 27

29 Eigh visual variables: Screen resoluion n Preprocessed daa: 53% of iems from original daa se 28

30 Eigh visual variables: Posiion - Scales 29

31 Eigh visual variables: Mark (or shape) n The second visual variable is he mark or shape: poins, lines, areas, volumes, and heir composiions. Marks are graphic primiives ha represen daa: n Example wih google maps n When using marks, i is imporan o consider how well one mark can be differeniaed from oher marks 30

32 Eigh visual variables: Mark (or shape) 31

33 Eigh visual variables n The posiion and marks, are required o define a visualizaion. Wihou hese wo variables here would no be much o see! n The remaining visual variables affec he way individual represenaions are displayed; n These are he graphical properies of marks oher han heir shape. 32

34 Eigh visual variables: Size n Size easily maps o inerval and coninuous daa variables, because ha propery suppors gradual incremens over some range. n I is more difficul o disinguish beween marks of near similar size, and hus size can only suppor caegories wih very small cardinaliy. n A confounding problem wih using size is he ype of mark. For poins, lines, and curves he use of size works well when marks are represened wih graphics ha conain sufficien area, he quaniaive aspecs of size fall, and he differences beween marks becomes more qualiaive. 33

35 Eigh visual variables: Size 34

36 Eigh visual variables: Brighness (ou luminance) n Brighness is he second visual variable used o modify marks o encode addiional daa variables. n While i is possible o use he complee numerical range of brighness values, human percepion canno disinguish beween all pairs of brighness values. Brighness can be used o provide relaive difference for large inerval and coninuous daa variables, or for mark disincion for marks drawn using a reduced sampled brighness scale. 35

37 Eigh visual variables: Brighness (ou luminance) 36

38 Eigh visual variables: Color n Color maps are useful for handling boh inerval and coninuous daa variables, since a color map is generally defined as a coninuous range of hue and sauraion values 37

39 Eigh visual variables: Color n When working wih caegorical or inerval daa wih very low cardinaliy, i is generally accepable o manually selec colors for individual daa values, which are seleced o opimize he disincion beween daa ypes 38

40 Eigh visual variables: Color n Check and ry wih: 39

41 Eigh visual variables: Orienaion n Orienaion is a principal graphic componen behind iconographic sick figure displays, and is ied direcly o preaenive vision. n The bes marks for using orienaion are hose wih a naural single axis; he graphic exhibis symmery abou a major axis. 40

42 Eigh visual variables: Orienaion 41

43 Eigh visual variables: Texure n Texure can be considered as a combinaion of many of he oher visual variables, including marks (exure elemens), color (associaed wih each pixel in a exure region), and orienaion (conveyed by changes in he local color). n Texure is mos commonly associaed wih a polygon, region, or surface. 42

44 Eigh visual variables: Moion n Moion can be associaed wih any of he oher visual variables, since he way a variable changes over ime can convey more informaion. n One common use of moion is in varying he speed a which a change is occurring (such as posiion change or flashing, which can be seen as changing he opaciy). n The oher aspec of moion is in he direcion for posiion, his can be up, down, lef, righ, diagonal, or basically any slope, while for oher variables i can be larger/ smaller, brigher/dimmer, seeper/shallower angles, and so on. 43

45 Effecs of Visual Variables n Selecive visual variables: Afer coding wih such variables, differen daa values are sponaneously divided by he human ino disinguished groups (e.g., for visualizing nominal values). Size (lengh, area/volume); Brighness; Texure; Color (only primary colors): varies wih he brighness value; Direcion / orienaion. 44

46 Effecs of Visual Variables n Associaive visual variables: All facors have same visibiliy (e.g., for visualizing nominal values). Texure; Color; Direcion / orienaion; Shape. 45

47 Effecs of Visual Variables n Ordinal visual variables: Afer coding wih such variables, differen daa values are sponaneously ordered by he human ino disinguished groups (e.g., for visualizing ordinal and quaniaive daa). Texure; Size; Brighness. 46

48 Effecs of Visual Variables n Check he slides by Sheelagh Carpendale, Universiy of Calgary hps://pages.cpsc.ucalgary.ca/~saul/hci_opics/pdf_files/visual-variables.pdf n For each graphic aribue evaluaes is use for each visual variable: selecive (is a change enough o allow us o selec i from a group?) associaive (is a change enough o allow us o perceive hem as a group?) quaniaive (is here a numerical reading obainable from changes in his variable?) order (are changes in his variable perceived as ordered?) lengh (across how many changes in his variable are disincions percepible?) 47

49 Effecs of Visual Variables (by Sheelagh Carpendale) 48

50 Effecs of Visual Variables (by Sheelagh Carpendale) 49

51 Effecs of Visual Variables (by Sheelagh Carpendale) 50

52 Effecs of Visual Variables (by Sheelagh Carpendale) n Check he slides by Sheelagh Carpendale, Universiy of Calgary hps://pages.cpsc.ucalgary.ca/~saul/hci_opics/pdf_files/visual-variables.pdf 51

53 Ineracive Daa Visualizaion Hisorical Perspecive 52

54 Hisorical Perspecive n Berin (1967) Semiology of Graphics n Mackinlay (1986) APT n Bergeron and Grinsein (1989) Visualizaion Reference Model n Wehrend and Lewis (1990) n Roberson (1990) Naural Scene Paradigm n Roh (1991) Visage and SAGE n Casner (1991) BOZ n Beshers and Feiner (1992) AuoVisual 53

55 Hisorical Perspecive n Senay and Ignaius (1994) VISTA n Hibbard (1994) Laice Model n Golovchinsky (1995) AVE n Card, Mackinlay, and Shneiderman (1999) Spaial Subsrae n Kamps (1999) EAVE n Wilkinson (1999) Grammar of Graphics n Hoffman (2000) Table Visualizaions 54

56 Hisorical Perspecive n In 1967, Jacques Berin, possibly he mos imporan figure in visualizaion heory, published his Sémiologie Graphique. 55

57 Hisorical Perspecive n Mackinlay (1986) inroduced a design for an auomaed graphical presenaion de- signer of relaional informaion, named APT (A Presenaion Tool) n Mackinlay wen on o describe graphical languages, defining graphical presenaions as senences of hese languages. Two graphic design crieria: expressiveness crierion, he effeciveness crierion, n The imporan aspec of Mackinlay s work perains o his composiion algebra, a collecion of primiive graphic languages and composiion operaors ha can form complex presenaions. 56

58 Hisorical Perspecive 57

59 Hisorical Perspecive n Mackinlay (1986) inroduced a design for an auomaed graphical presenaion de- signer of relaional informaion, named APT (A Presenaion Tool) n Mackinlay wen on o describe graphical languages, defining graphical presenaions as senences of hese languages. Two graphic design crieria: expressiveness crierion, he effeciveness crierion, n The imporan aspec of Mackinlay s work perains o his composiion algebra, a collecion of primiive graphic languages and composiion operaors ha can form complex presenaions. 58

60 Ineracive Daa Visualizaion Taxonomies 59

61 Taxonomies n A axonomy is a means o convey a classificaion n In visualizaion, we are ineresed in many forms of axonomies: daa, visualizaion echniques; asks; mehods for ineracion. n Based on he daa ypes and a lis of asks hey propose and classify around 100 echniques. 60

62 Keller and Keller (1994) Taxonomy of Visualizaion Goals n Classify visualizaion echniques based on he ype of daa being analyzed and he user s ask(s). n The daa ypes: scalar (or scalar field); nominal; direcion (or direcion field); shape; posiion; spaially exended region or objec (SERO). 61

63 Keller and Keller (1994) Taxonomy of Visualizaion Goals n Task lis idenify: esablish characerisics by which an objec is recognizable locae: ascerain he posiion (absolue or relaive); disinguish: recognize as disinc or differen (idenificaion is no needed); caegorize: place ino divisions or classes; cluser: group similar objecs rank: assign an order or posiion relaive o oher objecs compare: noice similariies and differences; associae: link or join in a relaionship ha may or may no be of he same ype; correlae: esablish a direc connecion, such as causal or reciprocal. 62

64 Shneiderman (1996) Daa Type by Task Taxonomy n The daa ypes: one-dimensional linear; wo-dimensional map; hree-dimensional world; emporal; mulidimensional; ree; nework. 63

65 Shneiderman (1996) Daa Type by Task Taxonomy n Shneiderman looked more a he behavior of analyss as hey aemp o exrac knowledge from he daa. n Overview. Gain an overview of he enire collecion. n Zoom. Zoom in iems of ineres o gain a more deailed view. n Filer. Filer ou unineresing iems o allow he user o reduce he size of a search n Deails-on-demand. Selec an iem or group and ge deails when needed. n Relae. View relaionships among iems. n Hisory. Keep a hisory o allow undo, replay, and progressive refinemen. n Exrac. Exrac he iems or daa in a forma ha would faciliae oher uses. 64

66 Keim (2002) Informaion Visualizaion Classificaion n Keim designed a classificaion scheme for visualizaion sysems based on hree dimensions: daa ypes, visualizaion echniques, and ineracion/disorion mehods 65

67 Keim (2002) Informaion Visualizaion Classificaion n Keim designed a classificaion scheme for visualizaion sysems based on hree dimensions: daa ypes, visualizaion echniques, and ineracion/disorion mehods. Classificaion of Visualizaion Techniques: n n n n n Sandard 2D/3D displays: x,y- or x,y,z-plos, bar chars, line graphs; Geomerically ransformed displays: landscapes, scaerplo marices, projecion pursui echniques, prosecion views, hyper-slice, parallel coordinaes; Iconic displays: Chernoff faces, needle icons, sar icons, sick figure icons, color icons, ilebars; Dense pixel displays: recursive paern, circle segmens, graph skeches; Sacked displays: dimensional sacking, hierarchical axes, worlds-wihin-worlds, ree-maps, cone rees. 66

68 Keim (2002) Informaion Visualizaion Classificaion n Classificaion of Ineracion and Disorion Techniques: n n n n n Dynamic projecion: grand our sysem, XGobi, XLispSa, ExplorN; Ineracive filering: Magic Lenses, InfoCrysal, dynamic queries, Polaris; Ineracive zooming: TableLens, PAD++, IVEE/Spofire, DaaSpace, MGV and scalable framework; Ineracive disorion: hyperbolic and spherical disorions, bifocal displays, perspecive wall, graphical fisheye views, hyperbolic visualizaion, hyperbox; Ineracive linking and brushing: muliple scaerplos, bar chars, parallel coordinaes, pixel displays and maps, Polaris, scalable framework, S-Plus, XGobi, XmdvTool, DaaDesk. 67

69 Ineracive Daa Visualizaion Furher Reading and Summary 68

70 Furher Reading n Pag from Ineracive Daa Visualizaion: Foundaions, Techniques, and Applicaions, Mahew O. Ward, Georges Grinsein, Daniel Keim, 2015 n Pag from Visualizaion Analysis & Design, Tamara Munzner Furher Reading and Summary 69