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disect 2D space - treemaps
takes tree of items with some ‘size’
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each level, proportionate to total size
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http://www.cs.umd.edu/hcil/treemap-history/](https://image.slidesharecdn.com/information-visualisation-200927162126/75/Information-Visualisation-an-introduction-57-2048.jpg)
Uploaded byAlan Dix
Information Visualisation – an introduction
The document discusses information visualization, highlighting its importance in making data comprehensible through sensory experiences, primarily visual. It covers various visualization techniques, tools, and concepts, including static and interactive visualizations, the historical evolution of visualization methods, and the factors affecting effective visual representation. Additionally, it emphasizes the role of visualization in data analysis, exploration, and consumer understanding, while introducing specific examples and approaches such as clustering and faceted browsing.
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Introduction to the concept of information visualisation by Alan Dix.
'Map Your Moves' shows 10 years of NYC migration data through a distorted interactive map.
Explains visualisation, focusing on sensory experiences and the importance of visual cues.
Explores purposes of visualisation for analysts and consumers, including understanding and exploration.
A brief history from static to interactive visualisations, emphasizing technological advancements.
Focuses on factors influencing visualisation design, including interaction, objectives, and aesthetics.
Methods for representing clusters in data visualisation, emphasizing the use of treemaps and 3D techniques.
Description of faceted browsing in digital libraries, enabling efficient data retrieval through specific attributes.
Techniques like starfields and Influence Explorer for dynamic filtering and exploring very large datasets.
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Information Visualisation – an introduction
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- 2. example Map your moves whereNew Yorkers move (10 years data) distorted map circle = moves for one zip code red – out blue – in overlaid http://moritz.stefaner.eu/projects/map%20your%20moves/
- 3. example Map your moves interactive: selectinga zip code shows where movements to/from also hiding: what you don’t show also important http://moritz.stefaner.eu/projects/map%20your%20moves/
- 5. what is visualistion? makingdata easier to understand using direct sensory experience especially visual! but can have aural, tactile ‘visualisation’
- 6. direct sensory experience N.B.sensory rather than linguisitic sort of right/left brain stuff! but ... may include text, numbers, etc.
- 7. visualising in text alignment- numbers think purpose! which is biggest? 532.56 179.3 256.317 15 73.948 1035 3.142 497.6256
- 8. visualising in text alignment- numbers visually: long number = big number align decimal points or right align integers 627.865 1.005763 382.583 2502.56 432.935 2.0175 652.87 56.34
- 9. visualising in text TableLens likea ‘spreadsheet’ ... ... but some rows squashed to one pixel high numbers become small histogram bars
- 10. visualising in text TableLens N.B.also an example of focus+context context whole dataset can also be seen in overview focus some rows in full detail
- 11. especially visual visual cortexis 50% of the brain! ... but disability, context, etc., may mean non-visual forms needed
- 13. why visualisation? for thedata analyst scientist, statistician, possibly you! for the data consumer audience, client, reader, end-user
- 14. why visualisation? consumer understanding rhetoric focus onwell understood, simple representations
- 15. why visualisation? consumer understanding rhetoric to helpothers see what the analyst has already seen infographics data journalism http://www.guardian.co.uk/news/datablog/2010/oct/18/deficit-debt-government-borrowing-data
- 16. why visualisation? consumer understanding rhetoric to persuadereaders of particular point (and not others!) lies, damn lies, and graphs the business plan hockey stick!
- 17. why visualisation? analyst understanding exploration powerful, oftennovel visualisations, training possible
- 18. why visualisation? consumer understanding exploration to makemore clear particular aspects of data confirming hypotheses e.g. box plots in stats noticing exceptions graph from: Measurement of the neutrino velocity with the OPERA detector in the CNGS beam
- 19. why visualisation? consumer understanding exploration to findnew things that have not been previously considered seeking the unknown avoiding the obvious wary of happenstance
- 21. a brief historyof visualisation from 2500 BC to 2012
- 22. a brief history... static visualisation – the first 2500 years interactive visualisation – the glorious ’90s and now? – web and mass data – visual analytics
- 23. static visualisation from claytablets to Tufte Mesopotamian tablets
- 24. static visualisation from claytablets to Tufte Mesopotamian tablets 10th Century time line
- 25. static visualisation from claytablets to Tufte Mesopotamian tablets 10th Century time line 1855 Paris-Lyon train timetable
- 27. static visualisation from claytablets to Tufte Mesopotamian tablets 10th Century time line 1855 Paris-Lyon train timetable Excel etc.
- 28. static visualisation read Tufte’sbooks ... – The Visual Display of Quantitative Information – Envisioning Information – Visual Explanations
- 29. interactive visualisation early 1990sgrowing graphics power – 3D graphics – complex visualisations – real-time interaction possible
- 30. ... and now loadsof data web visualisation data journalism http://www.guardian.co.uk/news/datablog/2010/oct/18/deficit-debt-government-borrowing-data http://www-958.ibm.com/software/data/cognos/manyeyes/
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- 36. world organisational social & political context direct interaction datavisualisation decision action processing the big picture
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- 39. choosing representations visualisation factors –visual ‘affordances’ • what we can see – objectives, goals and tasks • what we need to see – aesthetics • what we like to see what we can see what we need to see what we like to see
- 40. trade-off visualisation factors – visualaffordances – objectives, goals and tasks – aesthetics static representation trade-off interaction reduces trade-off –stacking histogram, overview vs. detail, etc. etc. interaction reduces trade-off – stacking histogram, overview vs. detail, etc. etc.
- 41. relaxing constraints normal stackedhistogram good for: –overall trend –relative proportions –trend in bottom category bad for others –what is happening to bananas? ?
- 42. make your own(iii) relaxing constraints interactive stacking histograms ... or ... dancing histograms normal histogram except ... normal histogram except ... dancing histograms
- 43. make your own(iii) relaxing constraints interactive stacking histograms ... or ... dancing histograms normal histogram except ... hover over cell to show detail
- 44. make your own(iii) relaxing constraints interactive stacking histograms ... or ... dancing histograms normal histogram except ... hover over cell to reveal detail click on legend to change baseline demonstration
- 45. kinds of interaction highlightingand focus drill down and hyperlinks overview and context changing parameters changing representations temporal fusion
- 46. Shneiderman’s visualisation mantra overview first, zoomand filter, then details on demand http://www.sapdesignguild.org/community/book_people/visualization/controls/FilmFinder.htm overview zoom and filter using sliders details on demand
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- 49. displaying groups/clusters numeric attributes –use average or region categorical attributes – show values of attributes common to cluster text, images, sound – no sensible ‘average’ to display – use typical documents/images – central to cluster ... or spread within cluster
- 50. using clusters the scatter/gatherbrowser take a collection of documents scatter: – group into fixed number of clusters – displays clusters to user gather: – user selects one or more clusters – system collects these together scatter: – system clusters this new collection ...
- 51. displaying clusters scatter-gather browser keywords(created by clustering algorithm) ‘typical’ documents (with many cluster keywords)
- 52. hierarchical data hierarchies areeverywhere! – file systems – organisation charts – taxonomies – classification trees – ontologies – xml
- 53. problems with trees... width grows rapidlyhard to fit text labels overlapping low level nodes
- 54. use 3D? cone tree –use stacked circles of subtrees
- 55. good use of3D still have occlusion ... but ‘normal’ in 3D shadows help to disambiguate but text labels difficult
- 56. cone trees cam trees horizontal layout makes labels readable small things matter!
- 57. x x/a – 4 x/b– 2 y y/c – 1 y/d – 1 y/e – 1 disect 2D space - treemaps takes tree of items with some ‘size’ – e.g. file hierarchy, financial accounts alternatively divides space horizontally/vertically for each level, proportionate to total size x [6] y [3] x/a [4] x/b [2] y/e [1] y/c [1] y/d [1] http://www.cs.umd.edu/hcil/treemap-history/
- 58. treemaps (2) later variantsimproved the shape and appearance of maps
- 59. treemaps (3) plus algorithmsfor vast data sets, for thumbnail images, etc. etc.
- 60. distort space ... treebranching factor b: – number of nodes at depth d = bd Euclidean 2D space: – amount of space at radius r = 2πr – not enough space! non-Euclidean hyperbolic space: – exponential space at radius r hyperbolic browser – lays out tree in hyperbolic space – then uses 2D representation of hyperbolic space
- 61. multiple attributes often dataitems have several attributes e.g. document: – type (journal, conference, book) – date of publication – author(s) – multiple keywords (perhaps in taxonomy) – citation count – popularity
- 62. traditional approach ... booleanqueries >new query ?type=‘journal’ and keyword=‘visualisation’ =query processing complete - 2175 results list all (Y/N) >N >refine query refine: type=‘journal’ and keyword=‘visualisation’ +author=‘smith’ =query processing complete - 0 results
- 63. faceted browsing e.g. HiBrowse(one of the earliest) multiple selection boxes – ‘or’ within box - ‘and’ between boxes digital libraries HCI 173 formal models interaction 157 task analysis visualisation 39 web keywords all 173 catarci 53 dix 9 jones 17 shneiderman 153 smith 0 wilson 22 authors all 173 book conference journal 173 other types
- 64. digital libraries HCI 173 formalmodels interaction 157 task analysis visualisation 39 web keywords all 173 catarci 53 dix 9 jones 17 shneiderman 153 smith 0 wilson 22 authors all 173 book conference journal 173 other types HiBrowse (ii) shows how many items with particular value – e.g. 39 documents with keyword=‘visualisation’ and type=‘journal’e.g. 39 documents with keyword=‘visualisation’ and type=‘journal’
- 65. digital libraries HCI 173 formalmodels interaction 157 task analysis visualisation 39 web keywords all 173 catarci 53 dix 9 jones 17 shneiderman 153 smith 0 wilson 22 authors all 173 book conference journal 173 other types HiBrowse (iii) can predict the effect of refining selection – e.g. selecting ‘smith’ would give empty resulte.g. selecting ‘smith’ would give empty result
- 66. digital libraries HCI 173 formalmodels interaction 157 task analysis visualisation 39 web all 173 catarci 53 dix 9 jones 17 shneiderman 153 smith 0 wilson 22 all 173 book conference journal 173 other keywords authors digital libraries HCI 39 formal models interaction task analysis visualisation 39 web all 39 book conference journal 39 other all 39 catarci 18 dix 1 jones 3 shneiderman 21 smith 0 wilson 7 types HiBrowse (iv) refining selection updates counts in real time all 45 book 6 conference journal 39 other all 45 catarci 19 dix 1 jones 5 shneiderman 24 smith 0 wilson 8 digital libraries HCI 45 formal models interaction task analysis visualisation 45 web
- 67. starfield (i) scatter plotfor two attributes colour/shape codes for more adjust rest with sliders dots appear/disappear as slider values change dynamic filtering
- 68. starfield (ii) when fewenough points more details appear
- 69. Influence Explorer (i) developedfor engineering models like Starfield ... but sliders show histogram how many in category (like HiBrowse) ... and how many ‘just miss’ red = full match black = all but one attribute greys = fewer matching attr’s
- 70. Influence Explorer (ii) someversions highlight individual items in each histogram similar technique has been used to match multiple taxonomic classifications
- 71. Information Scent Starfield shows whatis selected • explore using trial and error HiBrowse and Influence Explorer show what happen Pirolli et al. call this Information Scent – things in the interface that help you know what actions to take to find the information you want
- 72. very large datasets toomany points/lines to see solutions ... space-filling single-pixel per item Keim’s VisD random selection (see Geoff Ellis’ thesis) clustering visualise groups not individuals