Introduction to information visualisation for humanities PhDs
- 1. Introduction to Information Visualisation Dr Mia Ridge, @mia_out Digital Curator, British Library CHASE Arts and Humanities in the Digital Age, February 2017
- 2. While we're getting started... • Check that you can get online with the browsers Firefox or Chrome • The Exercises page contains all the links you need during the day • Check you can view it now: http://bit.ly/2kYtGx4 • Check you can log in to Viewshare with your new account http://viewshare.org/ • Timetable • 11am Tea and coffee • 1 - 1:45pm Lunch • 3 - 3:15pm Tea and coffee • 4:30pm Finish; free working time until 5pm
- 3. Overview • What is information visualisation and why use it? • The building blocks of visualisations • Exploring and critiquing interactive visualisations • Getting from the data you have to the visualisation you want
- 5. Visualisation is the graphical display of quantitative or qualitative information to create insights by highlighting patterns, trends, variations and anomalies.
- 6. From this...
- 7. ...to this
- 8. ...or this
- 9. Data visualisation can help you... Explore your data Explain your results
- 10. Why visualise information? For 'sense-making (also called data analysis) and communication' (Stephen Few) '…showing quantitative and qualitative information so that a viewer can see patterns, trends, or anomalies, constancy or variation' (Michael Friendly) '…interactive, visual representations of abstract data to amplify cognition' (Card et al) 'Distant reading' (Moretti) - focus on the shape rather than detail of a collection
- 11. Introductions • In a sentence or two, what's your interest in data visualisation? – What kinds of data do you work with? – What's the goal of any visualisations you're interested in creating? – Do you have any potential users in mind?
- 12. The building blocks of visualisation
- 14. Florence Nightingale's petal charts, 1857
- 16. John Snow's cholera map, 1854
- 17. Charles Minard's figurative map, 1869 'Figurative Map of the successive losses in men of the French Army in the Russian campaign 1812-1813'. Drawn up by M. Minard, Inspector General of Bridges and Roads in retirement. Paris, November 20, 1869.
- 18. Web 2.0 and the mashup, 2006 http://www.bombsight.org
- 19. Small multiples
- 20. The old tube map
- 21. Harry Beck, 1931
- 22. Exercise: compare n-gram tools http://bit.ly/2kYtGx4 • Think of two words or phrases you'd like to compare over time (e.g. Burma, Burmah). • Open two browser windows • In one, go to http://books.google.com/ngrams • In the other, go to http://benschmidt.org/OL/ • Enter your words or phrases in each and compare the results • Discuss with your neighbour: what differences did you find, and why?
- 26. Networks Every point on this diagram represents a male film producer. The pink dots represent men who worked exclusively with other men in the period surveyed, and the green dots represent those who worked with women. https://theconversation.com/women-arent-the-problem-in-the-film-industry-men-are-68740 Deb Verhoeven and Stuart Palmer
- 27. Visualising images and video http://www.flickr.com/photos/culturevis/5883371358/ 'Mondrian vs. Rothko', Lev Manovich, 2010. Image preparation: Xiaoda Wang
- 30. Data types • Quantitative • Qualitative • Geographic • Temporal • Media • Entities (people, places, events, concepts, things)
- 31. How do you get data to visualise? • Make it – Type it into a spreadsheet or database • Automate it – Extract it from text, images, audio or video • Find it – Lots of freely available data to practice with
- 33. Other forms of text analysis Entity recognition: turning text into things
- 35. Extracting information from video http://emotions.periscopic.com/inauguration/
- 36. Extracting information from images https://www.clarifai.com/demo
- 37. Exercise: try entity recognition Go to http://bit.ly/2kYtGx4 and follow the steps for text or images
- 39. Scholarly data visualisations • Visualisations as 'distant reading' where distance is 'a specific form of knowledge: fewer elements, hence a sharper sense of their overall interconnection' (Moretti, 2005) • Inspiring curiosity and research questions • But - which questions do they privilege and what do they leave out?
- 40. Exercise: critiquing scholarly visualisations Go to http://bit.ly/2kYtGx4 and follow the steps for Exercise 3 Pair up and discuss together before reporting back.
- 47. New York Society Library’s City Readers http://cityreaders.nysoclib.org/About/visualizations
- 48. Mapping the Republic of Letters http://www.stanford.edu/group/toolingup/rplviz/rplviz.swf
- 51. Digital Public Library of America http://dp.la/
- 54. State of the Union http://benschmidt.org/poli/2015-SOTU
- 57. From the data you have to the visualisation you want
- 58. Dealing with humanities data
- 59. Considerations for humanities data Commercial tools often assume complete, born- digital datasets – no missing fields or changes in data entry over time • Historical records often contain uncertainty and fuzziness (e.g. date ranges, multiple values, uncertain or unavailable information) • Includes metadata, data, digital surrogates
- 60. Messiness in historical data • 'Begun in Kiryu, Japan, finished in France' • 'Bali? Java? Mexico?' • Variations on USA: – U.S. – U.S.A – U.S.A. – USA – United States of America – USA ? – United States (case) • Inconsistency in uncertainty – U.S.A. or England – U.S.A./England ? – England & U.S.A.
- 61. When were objects collected? http://ibm.co/OS3HBa
- 63. Preparing data for visualisations Historical data often needs manual cleaning to: remove rows where vital information is missing tidy inconsistencies in term lists or spelling convert words to numbers (e.g. dates) remove hard returns and non-ASCII characters (or change data format) split multiple values in one field into other columns (e.g. author name, date in single field) expand coded values (e.g. countries, language)
- 64. Open Refine
- 65. …but be careful
- 66. What do you want to visualise?
- 68. What do you want to do? • See relationships among data points • Compare a set of values • Track change over time • See the parts of a whole
- 69. See relationships among data points • Scatterplot • Matrix • Network diagram
- 70. Compare a set of values • Bar chart • Bubble chart • Histogram
- 71. Track change over time • Line graph • Stack graph
- 72. See the parts of a whole • Pie chart • Treemap
- 73. Key format decisions • Static or interactive? • Print or digital? • Narrative or 'factual'? • Shape (distant view) or detail (close view)?
- 74. Purpose, data, audience, structure • Intersections of format and purpose • Data types: quantitative, qualitative, geographic, time series, media, entities (people, places, events, concepts, things) • Static, interactive; print, digital; product, process • Exploratory, explanatory: find new insights, or tell a story? Pragmatic, emotive?
- 75. Dealing with complex data • Find a visualisation type that can harbour the data in a meaningful way or reduce the data in a meaningful way. – e.g. go from individual values to distribution of values – e.g. introduce interaction: overview, zoom and filter, details on demand (Ben Shneiderman)
- 76. Exercise: 10 minute Viewshare tutorial Instructions http://bit.ly/2kYtGx4 Discuss: what did you learn about preparing data and using visualisation software?
- 81. Giorgia Lupi and Stefanie Posavec http://www.dear-data.com/all
- 82. Preparing data
- 83. Data Preparation • Generally needs to be in tables, one row per item, one column per value • Aggregate or individual values - might need to calculate totals in advance • Data should be made as consistent as possible with tools like Excel, OpenRefine
- 85. Sample advice From viewshare, on spreadsheets: • Remove any data that is not in a solid rectangular area. This includes white space, page titles, scattered cells, and additional worksheets. • Check that your formatting is consistent throughout each column (e.g. column is all in date format, currency format, etc. as appropriate). • Make sure that data of the same type but in different columns is formatted consistently (e.g. dates in different columns are in the same date format).
- 86. If all else fails... • Sketch out your visualisation on paper to test it • Iteration is key, and... • Stubbornness is a virtue!
- 87. Exercise: try views and widgets in Viewshare Instructions http://bit.ly/2kYtGx4 Views • Lists, maps, pie charts, bar charts, scatter plots, tables, timelines or galleries Widgets • Search boxes, lists, tag clouds, sliders, ranges, logos or text How might you apply these with your own data?
- 88. Design matters
- 89. Worst practice in data visualisations Source: http://www.forbes.com/sites/naomirobbins/2013/01/03/deceptive-donut-chart/
- 90. Worst practice in data visualisations Source: https://twitter.com/altonncf/status/293392615225823232
- 91. Visualisations and 'truthiness' A sample of publication printing locations 1534-1831 (British Library data) http://bit.ly/W9VM7D
- 92. Visualising uncertainty Matt Lincoln http://blogs.getty.edu/iris/metadata-specialists-share-their-challenges-defeats-and-triumphs/#matt
- 94. Publishing visualisations • How can you contextualise, explain any limitations of your visualisations? e.g. – provenance and qualities of original dataset; – what you needed to do to it to get it into software (how transformed, how cleaned); – what's left out of the visualisation, and why?
- 95. Best practice for design • How effectively does the visualisation support cognitive tasks? • The most important and frequent visual queries/pattern finding should be supported with the most visually distinct objects • Question: which examples did this well?
- 96. Do you really need a visualisation? • Use tables when: – doc will be used to look up individual values – to compare individual values – precise values are required – the quantitative info to be communicated involves more than one unit of measure • Use graphs when: – the message is contained in the shape of the values – the document will be used to reveal relationships among values
- 97. Don’t Do try this at home
- 98. Tools that don't require programming • Excel • Google Fusion Tables, Google Drive • Viewshare • Tableau Public NB: be careful about sensitive data on cloud platforms
- 99. Thank you! http://bit.ly/2kYtGx4 Mia Ridge @mia_out Digital Curator, British Library CHASE Arts and Humanities in the Digital Age, February 2017