Timeline Storyteller
The Design & Deployment of an Interactive Authoring Tool For Expressive Timeline Narratives
Matthew Brehmer · Microsoft Research · @mattbrehmer
In collaboration with Bongshin Lee, Nathalie Henry Riche, David Tittsworth, Kate Lytvynets, Darren Edge, and Christopher White.
The 2019 Computation + Journalism Symposium · slides: aka.ms/cj19
The Daily Routines of Famous Creative People
Story inspired by infographics by Podio and info we trust .
Data source: Daily Rituals: How Artists Work by Mason Currey (2013)
I want to tell you a story about the daily routines of famous creative people.
MAX : What you see here is a set of radial timelines depicting a typical 24 hours in the lives of 26 writers, artists, composers, and the like.
When they work, eat, sleep, exercise, and do other activities.
NEXT : Have you ever wondered: when do creative people create? Or are all creative people similar in this regard?
It turns out that some creative people prefer to work in short, focused bursts, like Darwin or Kant, while people like Murakami and Voltaire get up early and work for long uninterrupted periods of time.
Meanwhile, you have people like Kafka who work all night long.
NEXT : Now I want to tell you about the potential relationship between sleep and creativity, and possibly to dispel any potential misconceptions that creative people have irregular schedules.
Most people here tend to sleep at night, with notable exceptions like Balzac who would turn in at 6pm, and Kafka, who just seemed to keep odd hours.
What about variation and creativity? Let me show you how often these creative people change activities throughout their day.
NEXT : This 24-hour clock isn't the best way to convey the number or heterogeneity of activities, so I'm going to transition to a just show you a sequence of activities without any duration, effectively resulting in a bar chart wrapped radially.
NEXT : To show who varied the most and least among these people, a linear representation is perhaps best, where you can see that Darwin has the most varied day of all the people here
while Murakami prefers less variation.
NEXT : Finally, I'll restore the duration of events so that you can compare these timelines just by scanning up and down, where you might spot times where some routines appear to be in sync with one another, such as when people work or sleep at the same time.
And now I ask you the audience to compare your own daily rhythm to these creative people.
When do you sleep, eat, or do you best creative work?
A Timeline Design Space
Timelines Revisited: A Design Space and Considerations for Expressive Storytelling . Brehmer , Lee, Bach, Henry Riche, and Munzner. In IEEE TVCG (2017).
Representation
Scale
Layout
Timeline Storyteller, the open-source tool I used to tell you this story, is a visual storytelling tool based on a design space, or a set of design choices for presenting timeline data that my colleagues and I proposed in an IEEE Transactions on Visualization and Computer Graphics paper published in 2017.
This timeline design space specifies many of the ways in which you can represent time, such as linearly, radially, or by using spirals, grids such as calendars, or along a curve
And you can combine these representations with different time scales: you could use an absolute or relative chronological scale, a logarithmic scale, or a purely sequential scale.
And then there's "Layout", or how to draw one or more timelines within a page or display:
This has to do with deciding between a single timeline, faceting into multiple timelines (like how I did for each creative person),
Or wrapping a single timeline into meaningful segments of time, like a decade or a century.
Expressive Storytelling with Timelines
Timelines Revisited: A Design Space and Considerations for Expressive Storytelling . Brehmer , Lee, Bach, Henry Riche, and Munzner. In IEEE TVCG (2017).
timelinesrevisited.github.io
Provide alternative representations for time.
Provide alternative time scales .
Anticipate chronological or non-chronological narratives.
Incrementally reveal visual elements, selectively highlighting and annotating to direct attention.
In this TVCG paper we make the case that timeline design tools should provide alternative representations of time and alternative time scales.
Additionally, we advocated for the ability to tell non-chronological narratives: as many existing timeline presentation tools tend towards a chronological narrative:
In that either the whole timeline is shown as a static image, so readers are likely to begin at the start of the timeline, or events are revealed in chronological order, one event at a time as a series of slides.
For some stories, a chronological introduction of events makes total sense, while for others it does not,
Like how in my story about the routines of creative people, I revealed and highlighted aspects of those timelines in an order that wasn't chronological starting at midnight.
To achieve this expressive narrative design, designers should make use of animation, highlighting, and annotation to incrementally reveal parts of a narrative (that may not be chronological), to allow the viewer to make new comparisons.
The Authoring Interface of Timeline Storyteller
Web version imports CSV, JSON, GSheet. Power BI version imports various data formats.
Web version exports PNG, SVG, GIF, JSON spec. Power BI version exports PBIX, iFrame.
Timeline Storyteller is the realization of the design space and the considerations for expressive storytelling that we put forward in our TVCG paper
I'll take a moment now to show you the authoring interface that allowed me to create what I showed you earlier.
You can upload event data in a variety of formats.
And you can export content either as images, vector graphics, animated GIFs, or you can embed iframes of animated multi-scene stories.
Evaluating Timeline Storyteller
A controlled laboratory study to assess expressivity seemed to be inappropriate .
How do people use it with their own data ?
How does the content they produce reflect our timeline design space ?
Since its release in mid 2017, I've been thinking about ways to evaluate Timeline Storyteller and to better understand how people are using it.
For an expressive information design tool like Timeline Storyteller, a controlled user study in a lab measuring task completion time and error didn't seem appropriate.
I was instead interested in finding out how people would express themselves with their own data and on their own time,
And to determine if my timeline design space and the ideas put forward in the prior TVCG paper would be reflected in what people make.
Promoting Timeline Storyteller to Practitioners
Demos / talks at the Tapestry Conference , OpenVisConf , and the Dublin Data Summit in 2017.
Demo by Microsoft's Data Journalism Team at the 2017 Future of Storytelling Summit .
Posts on the official Power BI Blog , tutorial + interview for the Power BI YouTube channel .
Demo by a customer during the opening keynote of the 2017 Data Insights Summit :
Now in order to study how people use a deployed tool, you need to promote it to practitioners,
So I demonstrated Timeline Storyteller at renowned visualization practitioner conferences like Tapestry and OpenVisConf.
My colleagues also contributed to this effort to attract users with their own demonstrations and presentations, including demos by Microsoft's Data Journalism Team at the Future of Storytelling Summit in 2017.
I blogged about it on the official Power BI blog,
And along with the Microsoft Data Journalism team, I co-produced an interview and tutorial video for the Power BI YouTube channel.
And finally Power BI featured a demo of Timeline Storyteller in the opening keynote of the 2017 Data Insights Summit, a major Microsoft customer conference.
This was in front of about three thousand people and recorded online, and for this demo, Power BI asked a customer to demo it using data from his organization,
Which in this case was the UK National Trust, a heritage foundation that maintains historic landmarks.
Timeline Storyteller: Collecting Usage Data
Exported content from the web version in mid 2017.
Entries from a Storytelling Contest with the Power BI user community in late 2017,
coordinated by the Microsoft Data Journalism Team .
Download metrics of the Power BI desktop version:
Over 36,000 downloads of the Power BI version as of January 2019.
Following the promotional effort, I set about collecting data on how people were using Timeline Storyteller.
Which included collecting the content that people exported from the tool, as well as collecting entries from a storytelling contest within the Power BI community coordinated by Microsoft's Data Journalism Team,
And finally we collected usage statistics from the Power BI version.
Since Power BI is a desktop product and has different protocols for data collection and retention than Microsoft Research,
I don't know as much about how people use it unless they contact us directly or post content publicly online.
However, I do know that by the end of 2018, the Timeline Storyteller component for Power BI had been downloaded over 36,000 times.
Timeline Storyteller: Content Analysis
223 unique items of exported content from the web version (subject to author consent).
The corpus spanned the timeline design space - with a couple of exceptions.
The Linear representation and Chronological time scale were most common.
I saved copies of content exported from the web version of Timeline Storyteller over the course of about 9 months, subject to the approval of content authors.
After reviewing all of this content and discarding duplicate versions of content, I identified 223 unique artefacts generated with Timeline Storyteller, which included static images, animated GIFs, and multi-scene stories.
And by qualitatively analyzing this content, I found was that nearly every timeline design choice was represented in this corpus, though most of this corpus made use of the familiar linear representation and chronological time scale.
I didn't see any use of the calendar grid or the logarithmic time scale.
And if you do a Google image search for Timeline Storyteller and look at the content produced by people other than myself,
You'll see the prevalence of the linear representation and chronological time scale reflected there.
Timeline Storyteller: Content Analysis (cont.)
Example entries from the Power BI user community storytelling contest :
Tropical Cyclones by Manga Solutions.
| TV Network Ratings by Pragmatic Works.
With regards to the contest,
Where participants would post a timeline story created with Timeline Storyteller to the Power BI community forum.
Here are a couple of screenshots form contest entries,
The one on the left told a story about tropical cyclones, incorporating faceted spiral timelines and a custom color palette mapped to cyclone severity,
While the one on the right incrementally revealed the history of the highest rated television shows over the course of past few decades,
And this story was richly annotated and hit several points in our design space.
Timeline Storyteller: Conclusions & Opportunities
No prior interactive tools for presenting expressive timeline narratives .
The first to incorporate multi-scene stories with multiple visual representation choices .
Incrementally reveal + transform ; selectively highlight + annotate ; applicable to other data types.
Recommend design choices and annotations based on properties of the dataset.
In summary, Timeline Storyteller fills a gap in that no existing tool allows a non-programmer to present a visually expressive timeline narrative that may or may not be chronological.
It's really the first to incorporate this animated multi-scene PowerPoint-like story format along with multiple visual representation choices;
And I believe that many aspects of its design can be applied to other forms of data, such as its ability to incrementally reveal and selectively highlight content.
We're still learning about how it's being used in the wild,
As our limited evaluation of content analysis and usage only revealed so much.
And while the content produced does reflect our timeline design space to an extent,
Seeing this content made me realize the opportunity for providing overt recommendations of design choices, captions, and annotations more overtly within the application based on properties of the dataset, such as the temporal extent and distribution of the events.