The Social Life Of Visualization Web Directions Oct 2009
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The Social Life Of Visualization Web Directions Oct 2009
- 1. Jeremy Yuille @overlobe
- 2. Jeremy Yuille @overlobe
- 3. data
- 4. data
- 5. sensemaking
- 7. data ++
- 8. social objects Hugh MacLeod http://gapingvoid.com/2007/12/31/social-objects-for-beginners/ (via @mediajunkie)
- 10. compared
- 11. frequency
- 12. social gravity Jyri Engeström http://www.zengestrom.com/blog/2007/08/what-makes-a-go.html
- 13. design patterns
- 14. design patterns
- 16. mapping help people choose the most appropriate way to visualize a dataset.
- 17. mapping perceptual edge Graph Selection Matrix Time Series Ranking Part-to-Whole Deviation Distribution Correlation Nominal Comparison p hi Values display how some- Values are ordered Values represent The difference between Counts of values per interval Comparison of two paired A simple comparison of ns thing changed through by size (descending parts (ratios) of a two sets of values (for along a quantitative scale sets of values (for example, values for a set of unor- tio time (yearly, monthly, etc.) or ascending) whole (for example, example, the variance from lowest to highest (for the heights and weights of dered items (for example, a regional portions of between actual and example, counts of people in several people) to deter- products or regions) el total sales) budgeted expenses) an organization by age mine if there is a relation- R Graph intervals of 10 years each) ship between them Yes (to feature individual Yes (quantitative Yes (quantitative Yes (quantitative scale Yes (quantitative scale must Yes (quantitative scale Bar Graph values and support their scale must begin at scale must begin at must begin at zero) begin at zero) must begin at zero) (vertical) comparisons; quantitative zero) zero) scale must begin at zero) Yes (quantitative Yes (quantitative Yes (quantitative scale Yes (quantitative scale must Yes (quantitative scale Bar Graph scale must begin at scale must begin at must begin at zero) begin at zero) must begin at zero) (horizontal) zero) zero) Yes (to feature overall Yes (only when also Yes (to feature the overall trends and patterns and featuring a time series shape of the distribution) Line Graph support their comparisons) or single distribution) Yes (when you do not Yes Yes Dot Plot have a value for every (vertical) interval of time) Yes Yes Dot Plot (horizontal) Strip Plot Yes (when you want to see (single) each value) Yes (only when also fea- Yes (when comparing multiple Strip Plot turing distributions) distributions and you want (multiple) to see each value) Yes Scatter Plot Yes (only when also fea- Yes (when comparing multiple Box Plot turing distributions) distributions) (vertical) Yes (when comparing multiple Box Plot distributions) (horizontal) www.PerceptualEdge.com (Derived from the book Show Me the Numbers, Stephen Few, Analytics Press, 2004) Copyright © Stephen Few 2009 Stephen Few Show Me the Numbers http://www.perceptualedge.com
- 18. mapping Dan Roam The Back of the Napkin http://www.thebackofthenapkin.com/
- 19. decoration help people ‘decorate’ a visualization to better communicate its relevance and context
- 20. decoration
- 21. tweakability instead of making the visualization a snapshot, make it an interface that lets a user playfully explore the data.
- 22. tweakability
- 23. annotation let people annotate the visualization in a consistent and coherent manner
- 24. annotation
- 25. snapshot let people store and retrieve configurations of an interactive visualization
- 26. snapshot
- 28. thanks
Editor's Notes
- We need some way to make sense of all this data
- Online Data Visualization is “an idea whose time has come” due to a confluence of Readily available data (who doesn’t create data these days?) Increasing standardization of formats (XBRL etc) Flexible digital infrastructure (the pipes are connected) Re-usable visualization platforms & elements (widgets, timelines, online spreadsheets etc) Most importantly, a growing acceptance (and, in turn, demand) for online data visualization
- Data Visualization CAN help to make sense of the relationships between data
- Data Visualization CAN help to communicate complex concepts
- Access to data is going through an unprecedented phase of growth and standardization Online Data Visualization is “an idea whose time has come” due to a confluence of Readily available data (who doesn’t create data these days?) Increasing standardization of formats (XBRL etc) Flexible digital infrastructure (the pipes are connected) Re-usable visualization platforms & elements (widgets, timelines, online spreadsheets etc) Most importantly, a growing acceptance (and, in turn, demand) for online data visualization
- Social networks form around Social Objects, not the other way around Hugh MacLeod http://gapingvoid.com/2007/12/31/social-objects-for-beginners/(via @mediajunkie) Sensemaking
- How easily can it be sliced & diced? Broken down into atomic parts Jyri Engeström http://www.zengestrom.com/blog/2007/08/what-makes-a-go.html
- How easily can these parts and the wholes be compared with other objects? Jyri Engeström http://www.zengestrom.com/blog/2007/08/what-makes-a-go.html
- How frequently do people create them? Jyri Engeström http://www.zengestrom.com/blog/2007/08/what-makes-a-go.html
- How much social gravity do they have? handles for discussion Jyri Engeström http://www.zengestrom.com/blog/2007/08/what-makes-a-go.html
- Design patterns are another kind of social object. They also help us to frame a situation, to have foundational approaches to common messy problems
- Design patterns are another kind of social object. They also help us to frame a situation, to have foundational approaches to common messy problems
- WHen you put visualization inside a social system, you get an interesting circuit of learning and collaboration We used some design patterns to help describe how this circuit can be implemented...
- One of the most common problems that users experience when they present a dataset as a visualization is that they don’t always know the best visualization technique to use that fits with their data that they are presenting and achieves their communication or analysis goals. Users need to learn the inbuilt strengths and limitations of different visualization techniques and how these might fit onto the data they are seeking to present. Use when People need to choose the most appropriate way to visualize a dataset. Solution Help the person determine their analysis or communication goals and then suggest a visualization approach that maps most closely onto their stated objectives and is appropriate for their dataset. Many Eyes provides many different visualisation approaches and groups them by headings such as ‘analyse’ and ‘comparison’. Then next to each type of visualisation approach is a description of what that will highlight in the data set Why This chart suggestions flow chart breaks a wide range of visualisations into different communication outcomes, and variables of those outcomes Rather than forcing people to concentrate on learning the merits of different visualization approaches (which can seem esoteric), guiding them through their communication and analysis goals helps people to focus on what they already know about their data and context they want to present it in. How Attempt to determine the communication or analysis goals the person has for their data visualization, including: who they will be sharing the visualization with what kind of data they will be visualizing what outcomes they want the visualization to create Based on these factors, suggest a visualization approach for the data, explaining why that approach is best suited to their goals. Also present a range of other visualization approaches to the person, stressing their individual strengths and weaknesses. Issues This requires users to have a good understanding of the original data to be able to choose an appropriate visualization approach that communicates the dataset in the visual medium. An alternative approach by Many Eyes Wikified automatically chooses the visualization approach by performing a textual analysis on the dataset and choosing the best approach based on keywords (e.g. world uses a world map – heat map visualization), thus eliminating this problem.
- People need to attach visual meaning and identity to a visualization so that it can exist within an object centered social space and its meaning can be quickly transferred to others. Use when Creating a visualization within a social space for the purposes of attaching an identity and communicating the meaning of the object in a visual way. Solution Let people integrate imagery and other media into their visualization to better communicate that visualization’s relevance and context. Swivel queries Flickr with the chosen title of the visualization to provide images that can be used as a background for the visualization Why Decorating a visual representation provides it with an identity in much the same way that an avatar provides a user with an identity within a social network. This provides extra information about the visualization to other users, and contextualizes its place within a social space. In turn, this objectifies the visualization and allows it to exist on its own within the social environment. It also reduces the cognitive load on other users, and allows the inherent meaning in the visualization to be communicated and consequently transferred to the community with greater ease. How Integrate with the search APIs of user generated content communities to access images and media that relate to the content of the visualization. Issues Assigning absolute meaning to media can be tricky, and often fails to communicate effectively across different cultures. People can ‘read’ images and media very differently.
- Users need a way of shifting and reformatting a data visualization so that they can make sense of the whole data set by understanding how it responds to dynamic changes Use when One or more of the visualization parameters is variable (eg profit margin, unit cost) One or more of the visualization parameters is ordered (eg time, scale, amount, location) Solution Instead of making the visualization a snapshot, make it an interface that lets a user playfully explore the data. Gapminder enables a user to change or swap axes to look for correlations, as well as tweak other aspects of complex datasets. Create ways for users to change how a dataset is represented in a visualization allowing the impact of any changes they make to be immediately reflected. With ordered data, allow users to sort the data. eg: by labels, values and data order. Give users the ability to reconfigure a visualization schema. eg: swap the X and Y axis on a two dimensional graph. Pay attention to usability when designing visualization interfaces, eg: clearly communicate which parameter is selected, and what visualization elements it affects. Why Being able to tweak a parameter value and see how it affects a visualization helps communicate the relationship that parameter has to the whole visual analysis. This can help people see trends and make sense of complex datasets more quickly than with static visualizations. How Build controls into the interface that enable users to perform actions such as resorting the data, excluding certain parts of the data, or changing a variable that reflects the outcome of the data. This can be done through the use of drop down menus, radio buttons, check boxes and sliders.
- People need to comment on, or draw attention to specific elements of a visualization without compromising legibility of that visualization. Use when Wanting to promote discussion of visualization details and sub-elements. Solution Give people the ability to make annotations that are consistent and are not disruptive in any way to the underlying visualisation. Wikinvest allows users to annotate a company’s share price performance with non-disruptive annotations. Why Being able to create non-disruptive annotations adds knowledge to the visualization, the use of non-disruptive annotations means that all members of the community are talking in the same visual language which makes community sensemaking an easier process. How Instead of giving people a set of drawing, arrow and box tools as can be found in some desktop software, provide them with a single method of annotating a visualization that is in keeping with the visualization approach used (eg. highlight bars in a bar chart, show the height of ranges in a flow graph).
- When people can interact with the parameters of a visualization, they need to be able to store ‘snapshots’ of the visualization in order to communicate their understanding of a specific visualization configuration. Use when Interactive visualization is used to support discussion of a dataset. Solution Allow people to store and retrieve configurations of a data visualization. Many Eyes allows a snapshot of the current visualization state to be saved, and attached to a separate text based comment. Why Being able to see what another person saw is an important way of understanding what they are trying to communicate. Collecting snapshots along with discussion is a good way to illustrate the evolution of understanding around a dataset. How When commenting on a data visualization, attach a ‘snapshot’ of what the visualization currently looks like to the comment. When selecting a comment, configure the visualization to reflect the ‘snapshot’ associated with that comment. Issues Snapshots do not provide a good overview of the insight that a community has extracted from a visualisation. It is necessary to look through each snapshot and comment to get a sense of what has transpired.
- When people can interact with the parameters of a visualization, they need to be able to store ‘snapshots’ of the visualization in order to communicate their understanding of a specific visualization configuration. Use when Interactive visualization is used to support discussion of a dataset. Solution Allow people to store and retrieve configurations of a data visualization. Many Eyes allows a snapshot of the current visualization state to be saved, and attached to a separate text based comment. Why Being able to see what another person saw is an important way of understanding what they are trying to communicate. Collecting snapshots along with discussion is a good way to illustrate the evolution of understanding around a dataset. How When commenting on a data visualization, attach a ‘snapshot’ of what the visualization currently looks like to the comment. When selecting a comment, configure the visualization to reflect the ‘snapshot’ associated with that comment. Issues Snapshots do not provide a good overview of the insight that a community has extracted from a visualisation. It is necessary to look through each snapshot and comment to get a sense of what has transpired.
- these patterns are all documented (with video examples) at http://socialvizpatterns.info
- this presentation would not have been possible without fantastic collaborators: Hugh Macdonald @insanitycured Reuben Stanton @absent Nifeli Stewart Pete Williams @rexster & Bevan MacLeod at Deloitte Digital and the support of ACID, the Australasian CRC for Interaction Design http://acid.net.au RMIT University http://rmit.edu.au