Below we discuss further options to broaden our design space and elaborate on how interactive word-scale visualizations fit into the broader space of document-centric and visualization-centric analysis.

#### 4.3.4.1 Broadening the Design Space

The design space we explored focuses primarily on interaction techniques that support what Heer and Shneiderman [108] call “view manipulations.” Yet, the layouts and arrangements created with our interaction techniques also serve as a starting point for tasks in their “data & view specification” category (which includes activities such as filtering or sorting) and their “process and provenance” category (with activities such as annotation or sharing for collaboration). For example, gathered overviews simplify the task of filtering and sharing these overviews can aid collaboration.

Our current design space includes several interaction techniques that are useful for comparing groups of word-scale visualizations but we have not yet deeply explored operations that filter based on the displayed data or that change encoding properties of word-scale visualizations—which Yi et al. [224] also identify as important for visualization. One possible way of supporting operations like filtering is to treat word-scale visualizations as tokens that can be dynamically manipulated, shifted, and organized into groups, stacks, and piles. Using physically-inspired and force-directed interactions like those in “Kinetica” [178] or piling and sorting techniques like in “Small MultiPiles” [7] one could enable more nuanced and creative ways to explore data embedded in documents or even manipulate the document itself.

The techniques we implemented are mostly initiated from the menu or from a single word-scale visualization. However, these long horizontal menus are limiting and can hide important text, making alternatives such as pie menus [37] an appealing choice. Another promising interaction technique is Crossets [168] which could transform interactions like sorting into direct manipulations using the word-scale visualizations themselves. However, these kinds of techniques call for word-scale visualization designs which strongly suggest to readers that these interactions are possible [18, 23].

Finally, our prototypes examine view manipulation interactions with text documents including economic news articles, history texts, and research reports. However, word-scale visualizations in interactive applications like Hoffswell, Satyanarayan, and Heer [109]’s code editor present new opportunities and mediated complexities. In these contexts, both visualizations and text may appear and disappear dynamically. Moreover, applications for source code and larger document corpora may need to gather or connect word-scale visualizations from across multiple documents.
4.3.5 *Document-Centric to Visualization-Centric Analysis*

View manipulation interactions can help bridge the gap between document-centric analysis—in which the text is the focus of attention—and visualization-centric analysis—in which visual representations of data derived from the text are the focus [93]. Fully document-centric text analysis tools display the text itself, often with simple highlighting or annotations added. Meanwhile, visualization-centric approaches, such as PhraseNets [206] or Wordle [209] focus almost exclusively on the text as data, displaying entities and their relationships divorced from the original documents. In the middle are hybrid techniques such as Jigsaw [195], DocuBurst [55], or Elastic Documents [8] that either offer side-by-side views of visualizations and original text or data, or allow a reader to switch between reading visualizations and source texts.

<figure><img src="image_84.png" /><figcaption>Figure 4.18: From left to right: Document with embedded word-scale visualizations; word-scale visualizations collected around an entity of interest with text visible; small multiples without text; single visualization.</figcaption></figure>

Helping readers transition from document-centric views to visualization-centric ones and back during the course of an analysis may lead to more effective analyses which incorporate both analytical findings from the data and context from the documents. Our view manipulation interactions aid transitions between both types of views by introducing a number of interesting intermediate stages (Figure 4.18). A document with word-scale visualizations represents one step beyond a purely document-centric view. While the text remains the focus, the embedded visualizations reveal additional data in-context. Small multiples visualizations created by using a *gathering* interaction represent a pivot point along this spectrum and may serve as a transition between document- and visualization-centric modes. If the data is of greater interest to readers, they may then hide the text completely, allowing the small multiples layout to transform into a more general purpose text analysis environment, as in Van den Elzen's and Van Wijk's "Small Multiples, Large Singles" [70]. However, when the text again becomes important, this process could be reversed, fluidly re-introducing the visualizations back into the document. As such, interactive word-scale visualizations can also let readers shift between author-driven and reader-driven narrative modes as noted in the data storytelling literature [185]. Author-driven analyses require a
set reading order while reader-driven analyses allow free exploration
of the data.

4.4 WHAT DID WE LEARN ABOUT MICRO VISUALIZATIONS?

The content of the three publications I summarized here show the im-
pact of the surrounding text on the design of word-scale visualizations,
their placement, and their interactions. Text increases mediated com-
plexity but also poses opportunities in that it can be used as part of
the design, for example for legends and explanations or as a reference
point. The micro visualizations we created and studied in our work on
word-scale visualizations were much smaller than those we studied
in our work on data glyphs. Vertical height, in particular, was mostly
bound to font height or the inter-line space. Yet, we saw a multitude
of design ideas emerge in this context. The study with designers, in
particular, showed the opportunities and wide variety of possible data
encodings that can serve as inspiration for micro visualizations in
other contexts. These encodings often included multiple marks and
data encodings making them appear relatively complex externally.
Our work on interaction shows opportunities for small visualizations
in future interactive reading environments. It would be interesting to
consider how to expand our interactions with word-scale visualiza-
tions to small multiple micro visualization in general; for example,
regarding the comparison of glyphs in a small multiples setting.

Across all of our work we saw the need to better understand the
readability of the micro visualizations themselves but in particular also
the impact of word-scale visualizations on document readability. More
detailed eye-tracking and readability studies could help characterize
the trade-offs between different designs, interactions, and placement
options.
My current work focuses largely on micro visualizations in mobile context with a focus on fitness trackers and smartwatches. This line of work began as joint work with my former postdoc Tanja Blascheck, and is now continuing through a joint ANR-DFG French-German research grant called MicroVis. We focused our work both on issues related to mediated complexity that arises from the mobile context of micro visualizations on wearable devices—as well as on studying how designers currently solve external complexity issues in micro visualization design for smartwatches and fitness trackers.

5.1 DESIGNING SMARTWATCH VISUALIZATIONS IN CONTEXT

As part of a Dagstuhl seminar on Mobile Data Visualization, Sheelagh Carpendale, myself, and Charles Perin discussed methods to design mobile visualizations in-context. With students Foroozan Daneshzand, Alaul Islam, Katherine Currier, Peter Buk, Victor Cheung, Lien Quach, Laton Vermette we engaged in design exercises that we wrote down as part of a book chapter on mobile visualization design. For this chapter, Tanja Blascheck, Alaul Islam, and I together with Pantelis Antoniadis, Cristina Morariu, and Anne Reuter from the University of Stuttgart, engaged in a design exercise as part of a sightseeing activity at Stuttgart city center that I summarize here together with a short description of the general methodology. Any mention of “we/our” refers to this set of collaborators.

The goal of our Dagstuhl discussion and design activities was to
offer a simple, generative process that would work to envision new
ideas for mobile visualization applications in the context of use of
the mobile devices that would later show them. We wanted to illus-
trate our experiences with the use of this design methodology and
show how it allows to consider both the technological differences and
the usage differences of mobile contexts. Concerning technological
differences, visualizations for mobile devices can take advantage of
novel input modalities that do not exist on desktops: accelerometers,
gyroscopes, or personal health related sensors for heart-rate, oxymetry,
skin temperature, etc. Through sensors, mobile devices have direct
access to data and can provide quick contextual information to view-
ers. Most carry-able and wearable devices also have smaller screens
or screens that have completely different form factors than desktop
or laptop screens. This opens up opportunities to develop novel dedi-
cated visualizations rather than trying to make existing representation
techniques fit. Concerning mobile usage contexts, people’s motiva-
tion for using mobile visualizations is often much unlike those for
stationary office workers sitting in front of larger desktop or laptop
screens. In mobile contexts, for example, people: (1) may want to gain
better awareness and understanding of their surroundings and current
situation, (2) integrate this situational information into their current
activities, and (3) understand, share, or analyze data in non-office
surroundings with motion, uncontrolled lighting, or noise.

To let go of the limitations that come with designing by translating
existing visualizations and adapting them to mobile scenarios, we
have devised and explored a flexible design process. This design
process can help us to think of mobile visualizations by considering
specific contexts of use, mobile-specific tasks, and personal use cases.
Specifically, the design process involves stepping into specific usage
contexts and tasks, then taking moments to reflect on the current
situation and information needs, ideating design ideas, and reflecting
on them with others. The goal of the methodology is to create a rich set
of different ideas in context of a specific use case. Assessing the "value"
of each individual idea, refining it, selecting or discarding it, is a task
intrinsic to the motivation of each idea generating group that uses
the methodology. This section, therefore, does not discuss methods to
assess the novelty, effectiveness, or potential success of an idea. We
also do not claim that the methodology produces "better" ideas than
other methodologies, if that is even something that can ever be claimed
about an ideation methodology. Instead, we offer a methodology that
we explored and found useful for generating rich ideas for mobile
visualizations that communicate data visually. We detail how the
design methodology works in general, how three different design
groups adapted it, and provide examples of the richness of the ideas
that emerged.

The basic ideation methodology, which we used, was first explored
and then published as a workshop paper [56] with a focus on in
situ journaling by a single person. Here, we give more details on the
method, relax the frequency of note taking and sketching, and give
evidence about how the method can be adjusted and appropriated
to different scenarios. Bodystorming [163] is a technique similar to
ours as it focuses on design sessions in the intended context of use
coupled with discussions and further brainstorming on-site. It has
been promoted for the ideation of ubiquitous computing interfaces but
follows a different preparation phase as it gives participants specific
design questions to target.

5.1.1 *Mobile Visualization Ideation Methodology*

Here, we describe the general ideation methodology that can be fol-
lowed and adapted to conceive new designs for specific mobile visual-
ization contexts. We have successfully applied this methodology in a variety of mobile visualization scenarios, with small modifications.

In general, the ideation activity takes 1-3 hours. It is meant to be done as a paired activity to have another person to share the experience with and discuss ideas with (cf. Figure 5.1). Together both partners *choose an activity* (for example, getting food on campus, going to the mall, going to the gym, going to the library, going home) that is agreeable to both partners as well as the mobile context for which they would like to design (phones, smartwatches, fitness bracelets, etc.). Next, both partners choose and *prepare note taking material* and decide on a note taking procedure. Note taking materials can consist of digital devices, mobile device props made out of other material such as paper or cardboard, or simple notebooks. Once materials and situation are ready, both partners should use a note taking procedure that works for their

scenario. Typically, both partners start with their activity and stop after an agreed-upon time interval (*choose note taking interval*), with every 30 minutes being a good first estimate. During every activity gap each partner individually evaluates their information needs in the current situation and sketches a visualization that would address these needs in the current situation for the chosen mobile device (*ideate*). Notes should be added to the sketches so ideas are clearly communicated for later re-assessment of the sketches. After the sketching time *both partners discuss* their ideas and add comments, adjustments, or variations to their notes and sketches. Then, partners continue with the activity for the next time interval and repeat the previous two steps. It is ideal to try the activity at least four times, or more as needed. After the end of the exercise, partners meet as a group to go over all their sketches, generate affinity diagrams, and choose the most promising ideas to iterate on further.

<figure><img src="image_85.png" /><figcaption>Figure 5.1: General steps of the ideation method.</figcaption></figure>

### 5.1.2 Ideation Activity

Three different groups used the basic ideation methodology outlined above. The original bookchapter illustrates how one can adapt and
adjust the methodology to specific ideation scenarios. Considering that one of the primary motivations of this ideation methodology is to leverage the reality of one's current situation, it seems appropriate to make adjustments as one's context changes. The groups involved professors, researchers, and students at Simon Frasier University (Group 1), the University of Victoria (Group 2), and the University of Stuttgart, Inria, and University of Paris-Saclay (Group 3). Here, I only report on the activities of Group 3 that I was personally involved in.

The examples reported below were created by a group of six people while sightseeing in Stuttgart, Germany. In contrast to the initial instructions, the group chose to stay together throughout the activity and do the design exercises in changing pairs. After each activity the pairs discussed their designs and ideas together randomly, some pairs sketched together, some apart, some sketched just one design and some made multiple sketches. The design exercise focused on smartwatch applications and involved a physical paper prop in the shape of a smartwatch. The group conducted the ideation activity at various locations: at a market hall, the town hall with a famous paternoster elevator, twice in a museum with a historic clock collection, and during lunch. After the ideation sessions, three members of the group met to group, categorize, and discuss the different ideas. During the meeting they wrote down observations, grouped sketches, redrew and combined ideas, as well as discussed questions that came up. The following idea descriptions are grouped by the locations where the smartwatch visualizations were thought of.

Stuttgart Market Hall

<figure><img src="image_86.png" /></figure>

We created eight sketches, which we categorized into three groups. One group of four sketches was about apps that would help with shopping, such as a shopping list, a budget manager, or a product info display. One sketch described an app to find sights in proximity to the wearer's current location. Three sketches showed facts about the current sight including opening or busy time periods, ratings, or payment options. The left image shows a budget manager for souvenirs, money already spent, and how much a current item of interest costs. The middle image is a smartwatch face with an abstracted map that shows nearby sights. Each icon can be touched for more information. The right smartwatch face gives detailed information about the current sight being visited. The two inner rings show busy times while the wristband (not visible) shows additional information and ratings.
## Town Hall—Riding the Paternoster

<figure><img src="image_87.png" /></figure>

At Stuttgart's main town hall we rode a publicly accessible paternoster elevator and collected seven sketches in three different categories. Two focused on a smartwatch app related to elevator riding, with information about which floor one was on, which services are available on the floor, or also the position and waiting time for other elevators. One participant drew an app which would capture his excitement throughout the sightseeing trip—showing a spike during the visit at the paternoster. The outside ring color represents the average daily excitement level (left image). Three apps were related to an imagined administrative visit to the town hall (right image). The purpose of the visit, place and time of the appointment are shown, as well as an average wait time and an indication about how many people (17) are in front in line. All three were focused on way finding in the rather large administrative building. One app focused on also showing waiting times for certain services, and one visiting times for a local exhibition.

## Landesmuseum Stuttgart—Clock Exhibition

<figure><img src="image_88.png" /></figure>

Next, we went to a local museum that featured a historic clock and scientific measurements exhibition. Here, we held two ideation sessions that resulted in 12 different sketches. Three sketches contained an app that would help memory keeping of exhibition pieces (left image). Two further sketches included floor plans of the museum with tracking of which rooms one had already visited (second image). Three sketches showed apps that would allow to have a closer look or get general information about exhibition pieces close by (third image). Two sketches (right image) were concerned with giving an overview, recommendation, and ranking of exhibition pieces in the museum as a guide on what to view next. As we were getting closer to lunch and the end of our sightseeing activities two sketches showed information about upcoming events and the time left for the museum visit.
Lunch

<figure><img src="image_89.png" /></figure>

To conclude our ideation activity we went for a joint lunch in a local café and after ordering foods and drinks, we did one more sketching session that resulted in nine sketches. Three sketches were concerned with the day as a whole. Two displayed a history of activities throughout the day and one focused on showing the weather to inform future sightseeing activities. One app showed an overview of the sightseeing activities of the day with measures of knowledge or calorie gain and burn (left image). Six sketches were related to the restaurant experience with four focusing on apps that would help to find a restaurant based on price, type of food, or ratings (middle image). One app was related to choosing a menu item based on customer reviews, for example, which dessert other people spoke positively about on public ratings (right image). One app concentrated on more detailed information inside the restaurant such as waiting times, the table one was assigned, restroom information, or other people one could meet based on social media connections.

5.1.3 *Discussion*

Sightseeing, an inherently mobile activity, was a rich context for the ideation activity. The large group context and extended activity allowed the team to collect a large number of sketches. Going through these sketches with a few team members and grouping the ideas into clusters also helped to uncover interesting questions for the smartwatch visualization context. For example, the group had previously not considered the importance of showing the time. Some participants consistently included a display of time, others did not. From the collected sketches some designs could be used as smartwatch faces (8 sketches) while others might be dedicated apps (28 sketches). One participant wearing a smartwatch with a square display also drew square designs, pointing to a possible limitation of having used a round paper prop. Potentially, 17 of the sketches would also work with a squared smartwatch, but the other half (19 sketches) were designed with a round watch in mind. In hindsight offering a larger variety of smartwatch-sized props might have been useful for coming up with designs that are less focused on a particular display format. However, using the paper prop also gave the opportunity to think into the future. As the wristband could be drawn on, several sketches included information displays on the wristbands even though there are currently no consumer watches with wristband displays.
Our motivation for this project was to start developing methodologies that will encourage us to design mobile visualizations directly by basing the design process on: 1) visualization needs that emerge while we are on-the-go and 2) visualization designs that were thought of directly for on-the-go technology.

We have explored just one context for smartwatches and a few other context for mobile phones: transit, shopping, and bicycling (only covered in the original bookchapter). There are countless other possible scenarios to be investigated: isolation, wilderness, and sporting events to name just a few.

In reflecting on all of our activities we suggested that several quite consistent aspects across all the trials led to a successful design experience with rich ideas:

* Being in-situ: one important part of this methodology is that we tackled the idea at a location of possible use, with the individuals doing the ideation being actually physically present at the given location. While one participant (whose results are only covered in the original bookchapter) did successfully use imagining of the being in location, we suspect that needing this vivid imagination may not work as well for all people.

* Being at the moment: this idea is essentially a time-wise 'in situ.' It is our impression that this immediacy was useful in triggering ideas, but as one of our participants showed, for some re-imagining a very recent past may also work well.

* The repetition: while the idea of trying to think of a new idea every so many minutes did not appear to be necessary to be rigidly applied, notion of repetition seemed to be generative. That is, getting one idea and then getting another and then another and so on, seemed to be freeing in itself. In a way this took the pressure away. A person did not have to get the perfect idea first, they could just keep going and also re-apply previous ideas to new situations and places.

* The props: the way different individuals dealt with the device trigger varied from: looking at a physical device (phone or watch); imagining a physical device because current circumstance made pulling one out embarrassing; having a drawing or facsimile of a physical device. All three of these were successful and hint at possibilities of designing for novel devises by being able to draw one or create a mock-up.

* The notes and sketches: we made strong suggestions that note taking and sketching were done immediately, however, in reality this was not always possible. While it may be possible that some nuances were lost, on the whole post hoc notes were also successful.
5.1.4 *Summary*

In take-away we suggest trying this ideation approach bearing primar-
ily in mind: being in situ; being at the moment; and having or creating
a device mock-up. We do hope that some of our readers will try these
activities. We have found them to be delightfully effective. After we
started, ideas just seemed to flow forth. In addition, it seems that a
great deal of flexibility can be applied to this methodology without
losing its generative power. Important future work involves adjusting
or inventing new ideation methodologies that can help tease out new
ways to generate ideas that also leverage more of the unique features
of common mobile devices, such as their input, output, and sensory
capabilities.
<figure><img src="image_90.png" /><figcaption>Figure 5.2: Smartwatch face examples (from Facer [141]) with increasing amounts of data items and representation types. From left to right: Material Volcano (BlueIceshard), Pie Charts II (Sunny Liao), Minimal Colors H (AK Watch), and Earthshade (Brad C). The graph on the right shows common pairs of data types displayed on the watch faces our 237 survey participants used. Circle colors correspond to three data categories: Health & Fitness, Weather & Planetary, and Device & Location.</figcaption></figure>

## 5.2 SMARTWATCH VISUALIZATION

While it was fruitful to investigate ways to generate completely new applications for smartwatches it is important to understand the current use of these representation types on smartwatches and preferences of smartwatch wearers. Led by my PhD student Alaul Islam and in collaboration with Anastasia Bezerianos, Bongshin Lee, Tanja Blascheck, and myself, we published the findings of a survey with 237 smartwatch wearers as a short paper at IEEE VIS 2020 [113]. I summarize this work here. We assessed in particular the types of data and representations displayed on respondents' watch faces. Supplementary material is available at https://osf.io/nwy2r/ and any mention of 'we/our' in this section refers to this set of co-authors.

In this work, we focused on the use of visualizations on watch faces, which are the first screen or home screen wearers see when glancing at or turning on their watch [5, 230]. These watch faces are typically small, have a resolution between 128-480 px per side with a viewable area of around 30-40mm [15] and show the current time together with several data types, such as step count, location, and weather information. Watch faces are often customizable, allowing wearers to choose the data they want to see regularly and at a glance. Given the large variety of data available to display on smartwatches, we were particularly interested to answer the following research questions:

* Q1: Which data types do people show on their watch faces?

* Q2: In which form is the data currently represented?

* Q3: What more can we visualize?

To answer these questions, we first conducted an online survey with smartwatch wearers, then complemented these results with an online search and analysis of smartwatch face examples, as well as an analysis of the technical capabilities of the watches our participants reported wearing.
<table><caption>Table 5.1: Categories of data types shown on watch faces.</caption><thead><tr><td>Category</td><td>Data type</td></tr></thead><tbody><tr><td>Health & Fitness</td><td>Heart rate/ECG waveform, step count, sleep related info (e.g., quality, duration), distance traveled, calories burned, floors/stairs climbed, and blood pressure</td></tr><tr><td>Weather & Planetary</td><td>Weather info (e.g., sky condition), temperature, wind speed/direction, moon phase, humidity, and sunset/-sunrise time</td></tr><tr><td>Device & Location</td><td>Watch battery level, phone battery level, bluetooth, wifi, and location name</td></tr><tr><td>Other</td><td>Data and representation type not in our list (open textfield)</td></tr></tbody></table>

### 5.2.1 Methodology

Here, I present a highly condensed description of the survey but both the original paper and OSF repository provide details needed for replication or reproduction.

Inspired by related work [210] and a commercial smartwatch face app [141], we asked participants about three categories of data on their watchfaces: health & fitness related data, weather & planetary data, and device- & location-related data. For each kind of data we asked participants to tell us if and *how* the data was shown on their watch face. We provided participants with five possible representation types accompanied by a text description. These categories were based on a review about how numerical or categorical data are displayed on more than 500 watch faces that we collected from the Facer app and internet searches.

### 5.2.2 Analysis & Results

The majority of participants reported wearing a smartwatch with a round display (□ 150x), followed by a square (□ 68x), and rectangular display (□ 17x). Two participants reported having Squaricle / Rounded square types. Participants' smartwatches came from 20 different brands with Apple (□ 76x), Fossil (□ 51x), Samsung (□ 36x), Garmin (□ 17x), and Huawei (□ 12x) being the top five brands (80% of our respondents).
<figure><img src="image_91.png" /><figcaption>Figure 5.3: Number of data items present on a respondent's watch face.</figcaption></figure>

<figure><img src="image_92.png" /><figcaption>Figure 5.4: Distribution of data types participants displayed and saw on their watch faces (left); aggregated by categories on the right.</figcaption></figure>

### 5.2.3 Q1: Which data types do people show on their watch faces?

We were first interested to see whether people had configured their watch faces to show a large amount or only a few data items. On average, participants reported showing a median of 5 different data items on their watch faces. Figure 5.3 shows that having 3, 4, or 5 data items were the most common answers.

Next, we wanted to learn which data types were the most commonly displayed (Figure 5.4). From the three categories we asked about, health-fitness related data were the most commonly reported (530×). The most common data type in this category was step count (the third most common overall, 147×). Temperature was the most frequent weather & planetary data type (the second most common overall, 158×). For device-location related data, watch battery level (165×) was
<figure><img src="image_93.png" /><figcaption>Figure 5.5: Average number of representation types for each participant.</figcaption></figure>

the most displayed and also the most common overall. The most common additional data types mentioned in free-text responses were: *standing up count* (43×) and *exercise/body movement time* (24×).

Next, we wanted to learn about individual watch faces. We analyzed, which categories were most common per watch face and which data types often appeared together. On average, most of the data shown on an individual watch face came from the **health & fitness** category. Participants reported seeing on average: 2.24 **health & fitness** (Mdn = 2, 95% CI [1.98, 2.48]), 1.89 **weather & planetary** (Mdn = 2, 95% CI [1.69, 2.08]) and 1.52 **device-location** related data (Mdn = 1, 95% CI [1.35, 1.7]) on their watch face.

To know more about which types of data are commonly shown together, we performed a co-occurrence analysis of data types participants saw on their watch faces. The graph in Figure 5.2 shows combinations of two kinds of data that can be found on at least 25% of our respondents' watch faces. The thicker the link, the more frequent the data pair appeared on people's watch faces. Circle size corresponds to how often participants reported seeing this data type. Circle color corresponds to the data type category. Only connections that appeared more than 59 (≈ 237 / 4) times are shown.

### 5.2.4 Q2: In which form is the data currently represented?

Figure 5.5 shows the average number of representation types each participant had on their watch face. **Icon+Text**<sup>68</sup> was the most common representation type, used to display on average two kinds of data types on each watch face (M = 2.05, 95% CI: [1.78, 2.32]). The next most common were **Text Only**<sup>68 bpm</sup> (M = 1.38, 95% CI: [1.13, 1.66]), and **Icon Only**<sup>68</sup> (M = 1.11, 95% CI: [0.93, 1.3]). Representations using visualizations were less common. **Chart+Text**<sup>68 bpm</sup> (M = 0.82, 95% CI: [0.64, 1.03]) and **Chart Only**<sup>68</sup> (M = 0.28, 95% CI: [0.2, 0.37]) appeared less than once per watch face on average.

In Figure 5.6 we can see how many participants showed each data type with each representation type. Data types most commonly displayed with either **Chart Only**<sup>68</sup> or **Chart+Text**<sup>68 bpm</sup> were *calories burned* (14 + 30 = 44×), *step count* (10 + 32 = 42×), and *watch battery levels* (14 + 28 = 42×).
<figure><img src="image_94.png" /><figcaption>Figure 5.6: Representation types reported for different data types.</figcaption></figure>

**Complementary search of representation types.** Surprised by the high number of icons reported, we decided to investigate further how different information can be displayed on watch faces. We conducted an extensive image search, during which we looked for examples of each representation type in current use. We looked at popular watch brands' websites, searched the internet for images (keywords: smartwatch face, popular smartwatch, smartwatch, etc.), and looked at examples from the Facer watch face creation and distribution app. Table 5.2 shows exemplary graphics for each kind of data × representation type combination, redrawn for image clarity. We found only few examples online of data types represented by an **Icon Only** ♡ display. Yet, Figure 5.6 shows that participants reported seeing **Icon Only** ♡ representations for almost every data with on average around one **Icon Only** ♡ display per smartwatch face. We discuss this discrepancy further in Section 5.2.6.

### 5.2.5 Q3: What more can we visualize?

**Complementary investigation of device capabilities.** To find untapped opportunities for visual representations, we looked at technical details for the 54 smartwatch models (from the 20 brands) our participants wore. We found that all smartwatches had fitness or activity tracking as a core feature, including measuring and display of body movement, steps, sleep patterns, or dedicated exercise tracking. The smartwatches our participants used also carried a wide variety of
<table><caption>Table 5.2: Redrawn example representations from real smartwatch faces. Text color corresponds to the data type category. Bluetooth and wifi only text and only icon change color based on on/off status.</caption><thead><tr><th>Data Types</th><th>Only Text</th><th>Only Icon</th><th>Icon+Text</th><th>Only Chart</th><th>Text+-Chart</th></tr></thead><tbody><tr><td>Heart rate / ECG waveform</td><td>68 bpm</td><td>♥</td><td>♥ 68</td><td>HEART RATE</td><td>11:30PM-7:30AM 8h30m</td></tr><tr><td>Step count</td><td>3168 steps</td><td></td><td>3168</td><td></td><td>1596</td></tr><tr><td>Sleep related info</td><td>1h 13m REM<br>4h 11m light</td><td></td><td>Zzz 6h 53m</td><td></td><td>11:30PM-7:30AM 8h30m</td></tr><tr><td>Distance traveled</td><td>1.19 Miles<br>DISTANCE</td><td></td><td>1 Mile</td><td></td><td>182</td></tr><tr><td>Calories burned</td><td>64 Cal</td><td></td><td>1,603</td><td></td><td>396</td></tr><tr><td>Floors/Stairs climbed</td><td>31 floors</td><td></td><td>13</td><td></td><td>10</td></tr><tr><td>Blood pressure</td><td>sys 120/dia 81</td><td></td><td>126/78</td><td>ELEVATION PROFILE</td><td>1:146 496</td></tr><tr><td>Weather info</td><td>PARTLY CLOUDY</td><td>天下阴雨</td><td>50% 90%</td><td></td><td>1:44</td></tr><tr><td>Wind speed/direction</td><td>Wind ESE at 3mph</td><td></td><td>West</td><td></td><td>WIND SPEED</td></tr><tr><td>Temperature</td><td>31°C</td><td></td><td>14C</td><td></td><td>Temp 17</td></tr><tr><td>Sunset/Sunrise time</td><td>6:34 PM SUNSET<br>7:14 AM SUNRISE</td><td></td><td>↑ 7:14 am<br>↓ 6:34 pm</td><td></td><td>5h 12min<br>7:14 am 6:34 pm</td></tr><tr><td>Moon phase</td><td>Moon Age: 25.43 Days</td><td></td><td>25.45<br>17% Moon</td><td></td><td>Moon 25.7</td></tr><tr><td>Humidity</td><td>40%<br>HUMIDITY</td><td></td><td>42%</td><td></td><td>HUMIDITY 40%</td></tr><tr><td>Bluetooth</td><td>BLUETOOTH</td><td>Bluetooth icon</td><td></td><td>())</td><td></td></tr><tr><td>Phone battery level</td><td>Mob 85%</td><td></td><td>100%</td><td></td><td>50%<br>phone</td></tr><tr><td>Location name</td><td>Paris</td><td></td><td>Normandy</td><td></td><td>30° 10' 04"N 77° 47' 58"W</td></tr><tr><td>Wifi</td><td>wifi</td><td></td><td>3 bars</td><td></td><td>wifi 0%</td></tr><tr><td>Watch battery level</td><td>WATCH 44%</td><td></td><td>88%</td><td>Watch</td><td>50%<br>watch</td></tr></tbody></table>

sensors [121]: activity sensors such as accelerometers (53 models) and gyroscopes (46 models); physiological sensors such as heart rate sensors (47 models); and environmental sensors such as barometric altimeters (38 models). Many smartwatches allowed for at least blue-tooth (54 models) or wifi (43 models) connectivity. By tracking which types of sensors were available on people's smartwatches, we derived the types of data their watches could track and participants could see on their watch faces (Figure 5.7).
<figure><img src="image_95.png" /><figcaption>Figure 5.7: Difference between # of watches that tracked each data type and how many participants actually saw it on their watch face.</figcaption></figure>

There naturally is a mismatch between what our participants could see and what they did see: watch faces do not show all available data. Nevertheless, this mismatch varies. For example, from health & fitness data that almost all devices track, roughly 62.03% of participants saw step counts, but this percentage was lower for heart rate (45.61%), or calories burned (43.88%), and dropped drastically for distance traveled (34.65%), floors count (22.97%), sleep (14.54%), and blood pressure (13.48%). This list of commonly tracked data that is under-represented can serve as a starting point for visualization designers. For example, in past work led by my Master's student Ranjini Aravind [6] we found that smartwatch wearers would have liked to see sleep data but a display on their fitness tracker was not available to them.

### 5.2.6 Discussion and Future Work

It is challenging to determine a right vocabulary for wide-audience surveys. In our case, while we found few examples of icon only displays, participants often reported this type of representation. One possibility for these responses might be confusion about what constitutes "data." In the survey instructions, we informed participants that we only cared about information in the form of numbers or categories, such as step count (numerical) or weather condition (categorical). We also asked participants not to consider graphics such as settings, calendar, or music app icons because they do not represent numerical or categorical information; and gave examples of graphics we cared and did not care about. Yet, participants might not have read the instructions carefully and included responses about graphical icons that do not change based on data. A second possibility for the larger frequency of ❧ Icon Only ❧ responses might be attributed to typical Icon+Text ❧
displays that due to missing or currently inaccessible data result in an icon-only representation (e.g., 💗: heart icon with currently blank text). For our analysis reported in Figure 5.7 we had to sometimes infer based on sensors whether a certain derived value such as *calories burned* would be available on a watch. The supplementary material makes our inferences transparent.

A wide variety of data types is available for our participants' watch faces. The list of frequently presented data types provides starting points for creating visual representations that could be valuable to a broad range of viewers. In addition, when designing perceptual studies in the future, it might be useful to take into account participants' familiarity with this data type.

Our participants had five data items on average on their watch faces. As five is a relatively large number for a small smartwatch display, an open research question is how to help people cope with such a dense data display. Given our analysis of common co-occurrences (especially within the categories) (Figure 5.2-right), it may be useful to consider combining them into joint representations.

Our survey results indicate that visualizations are still not as common as other representations such as text, even though they can be used to represent some of the most commonly displayed data (e.g., *step counts* and *battery levels*). Our online search of technical capabilities of smartwatches also indicates that much of the data tracked wearers do not see. This includes some *health & fitness* data that most devices track (e.g., *calories*, *distance*, *sleep* and *blood pressure* data). Whether these are explicit customization choices due to specific tasks they want to carry out, or due to a choice the default displays promote for the smartwatch face, remains an open question. Further research needs to investigate representation choices, to determine if the wider adoption of visualizations is a question of preference, tasks, a lack of exposure, and if it requires us to rethink visual encodings for smartwatches. In addition, future work needs to establish at which level of granularity information should be displayed. For example, are exact wind speeds important or are broad categories (stormy, light breeze, no wind) enough; presentation types would change based on this decision.

In summary, our work contributes to the understanding of the current real-world use of representation types on smartwatches and additional findings that can inform and inspire the visualization community to pursue smartwatch visualization.
<figure><img src="image_96.png" /><figcaption>Figure 5.8: Left: Example images of the stimuli used in the two perception studies. We tested three *chart types* (Bar, Donut, and Radial) with three *data sizes* (7, 12, and 24) on a smartwatch. When printed without rescaling the image, the sizes correspond to the size of the stimuli as shown on our smartwatch (28.73 mm × 28.73 mm). Right: One stimulus shown on the smartwatch.</figcaption></figure>

5.3 GLANCEABLE VISUALIZATION: STUDIES OF DATA COMPARISON PERFORMANCE ON SMARTWATCHES

In 2018, I worked with Tanja Blascheck, Lonni Besançon, Anastasia Bezerianos, and Bongshin Lee on a specific aspect of mediated complexity related to smartwatch visualization: the glanceability of data representations on smartwatches. We published an IEEE InfoVis paper on the topic in which we report on two studies that assessed how quickly people can perform a simple data comparison task for small-scale visualizations on a smartwatch [15]. For a bookchapter [14] in the Mobile Data Visualization book [134] Tanja Blascheck, Frank Bentley, Eun Kyoung Choe, Tom Horak, and I illustrated glanceability as a crucial requirement for several types of mobile visualizations. For example, in a difficult terrain, a runner can only check a smartwatch for elevation or heart rate data for a few hundred milliseconds before the eyes need to refocus on the trail ahead. Such quick information needs differ from those in traditional visualizations that are meant for deep analysis and interaction with possibly large and complex datasets. We described characteristics of glanceable visualizations in terms of presence and access, simplicity and understandability, and suitability and purpose. In this section, I report about the 2018 study on glanceable visualizations and thus, any use of 'we/our' refers to the co-authors of this study.

Visualizations, by design, allow to effectively convey information and reduce reading time [38]. Therefore, they may be able to convey a large amount of information during brief glances at a smartwatch. Many possible application contexts exist, in which visualizations can help to provide detailed information during quick glances. For ex-
<figure><img src="image_97.png" /><figcaption>Figure 5.9: Left: A participant, wearing helmet and smartwatch both with markers attached, is reading a text displayed on the smartwatch while sitting. Right: Measurements taken during the pre-study.</figcaption></figure>

ample, in the context of personal data tracking, visualizations on
smartwatches can show workout progress, can be a fitness and health
indicator for runners, or show elevation profiles during a hike in
the mountains. Neither the related work on micro visualizations as
data glyphs nor word-scale visualizations has developed dedicated
guidelines about how to design for quick glances at a visualization.
In our 2018 paper we presented the design and results of two per-
ceptual studies conducted to assess how quickly viewers can read
information from small-scale visualizations shown on a smartwatch.
We considered not only the mediated complexity of the quick-glance
viewing scenario but also external complexity of visualization design
by varying the number of data items being displayed. Next, I present
a condensed version of the study and its results and end with future
research questions and open challenges.

5.3.1 *Pre-Study: Position and Orientation of Wristworn Smartwatches for Reading Tasks*

In a pre-study, we investigated at which viewing angle and distances
smartwatches are commonly held while people are reading infor-
mation on them. The full details of this study can be found in our
previous workshop paper [16]. Here, we summarize the most impor-
tant findings.

During the study, participants wore the same smartwatch used in
the main study, but with attached tracking markers. They also wore
a firmly attached bike helmet with tracking markers (cf. Figure 5.9,
left). Participants read 20 sentences per condition on the watch. While
participants were reading, we captured position and orientation for
helmet and smartwatch using a 6-camera 3D real-time Vicon tracking
system. The Vicon tracking system logged for both the helmet and the
smartwatch: a timestamp, an x-, y-, and z-coordinate for the position,
and the orientation of the object as a quaternion (qx, qy, qz, qw).

From this data, we calculated: (1) *Pitch angle*, the angle between the watch's normal and the floor's normal (angle α in Figure 5.9, right); (2) *Smartwatch distance*, the distance from the center between both eyes to the smartwatch's center, corresponding to the length of the *line of sight* (LOS) in Figure 5.9, right; (3) *LOS offset*, the angle between LOS and the inverted smartwatch face's normal (=angle β in Figure 5.9, right).

The results showed that average pitch angles were 48° (SD = 15°) when sitting, and 52° (SD = 13°) when standing. This leads to an average pitch angle of 50° (SD = 14°) for both conditions combined. For calculating the LOS offset, we removed 23 (4%) out of 480 trials as outlier trials, i. e., all trials beyond 2 SD per participant. We found that neither sitting nor standing participants' LOS aligned with the smartwatch face's normal, indicating a slightly tilted view. The LOS offset was 11° (SD = 8°) when sitting and 9° (SD = 6°) when standing. This gives us an average angle of 10° (SD = 8°) between the two conditions. Finally, the watch distance was 27.6 cm (SD = 3 cm) when sitting, and 28 cm (SD = 5 cm) when standing. The average distance between the two conditions is 28 cm (SD = 5 cm).

5.3.2 *Two Studies on Minimal Task Times*

With the results from our pre-study, we set up two controlled exper-
iments to find a minimum *time threshold* participants would need to
conduct a simple data comparison task on different small-scale visu-
alizations on a smartwatch. We investigated three *chart types* (Bar 📈, Donut 🧗, Radial 📊) and three *data sizes* (7, 12, and 24).

5.3.2.1 Study Design - Condensed Version

In both studies, we used a two-alternative forced choice (2AFC) design [97, 122], a popular technique in psychophysics to assess the perception of visual stimuli. This technique generally involves showing two alternative options per trial and asking participants to select one of the two options based on a given task. In our case, each stimulus involved a *chart type* × *data size* combination. On each chart, we highlighted two target data values using black dots. Participants' task was to select the larger of the two targets. We showed each stimulus for a specific *stimulus exposure duration* to be able to study the average task duration participants need to perform this task. The stimulus exposure duration was adapted w.r.t. participants' responses—it was made shorter after three correct responses and longer after an error. Figure 5.10 gives an overview of the procedure of one study session
<figure><img src="image_98.png" /><figcaption>Figure 5.10: Procedure of one study session. After the introduction, participants performed nine staircases (for 3 *chart types* × 3 *data sizes*). Between each *chart type* we asked participants about their strategy. The study ended with a debriefing session.</figcaption></figure>

<figure><img src="image_99.png" /><figcaption>Figure 5.11: Procedure for one trial in the study. After a participant's response, we show feedback on the correctness of the answer. After 1s, the next stimulus is shown followed by four intervening images and then a black screen with a prompt to enter a response.</figcaption></figure>

while Figure 5.11 shows the procedure for one trial. More details can be found in the original paper. The difference between both studies concerned primarily how we chose target differences. In Study 1 these were pre-set and randomly chosen in Study 2.

We used a Sony SmartWatch 3 with an Android Wear 2.8.0 operating system. The smartwatch has a viewable screen area of 28.73 mm × 28.73 mm and a screen resolution of 320 px × 320 px (= a pixel size of 0.089 mm). Figure 5.8 (right) shows an image of the smartwatch with a Bar 📴 stimulus. With an average viewing distance of 28 cm the visualization covered about 4° of visual angle, so the visualizations were on the larger end of the micro visualization spectrum.

The measure in our study was the *time threshold* for each staircase. Given our study design, this threshold should represent ~91% correct responses for the particular combination of *chart type* × *data size* [84].

We recruited 18 participants (7 female, 11 male; 10 researchers, 8 students), with an average age of 30 years (SD = 7.7) for an in-person lab study for Study 1, and another 18 new participants (7 female, 11 male; 7 researchers, 3 students), with an average age of 35 years (SD = 13) for Study 2.
<table><caption>Table 5.3: Top: Mean thresholds (T) and confidence intervals (CI) across all sizes and for each *data size* given in milliseconds. Bottom: Pair-wise comparisons (Δ T) between *chart types* (CIs were adjusted for 3 pairwise comparisons with Bonferroni correction), and for each *data size* (CIs adjusted for 9 pairwise comparisons).</caption><thead><tr><th></th><th colspan="2">ALL SIZES</th><th colspan="2">7</th><th colspan="2">12</th><th colspan="2">24</th></tr><tr><th></th><th>T</th><th>CI</th><th>T</th><th>CI</th><th>T</th><th>CI</th><th>T</th><th>CI</th></tr></thead><tbody><tr><td>Bar</td><td>245 [221,272]</td><td></td><td>168 [155,181]</td><td>208 [187,240]</td><td></td><td></td><td>360 [307,432]</td><td></td></tr><tr><td>Donut</td><td>159 [155,164]</td><td></td><td>157 [147,165]</td><td>158 [154,164]</td><td></td><td></td><td>162 [158,168]</td><td></td></tr><tr><td>Radial</td><td>1548 [1116,2030]</td><td></td><td>286 [228,422]</td><td>766 [415,1519]</td><td></td><td></td><td>3593 [2515,4679]</td><td></td></tr></tbody></table><table><thead><tr><th></th><th>Δ T</th><th>CI</th><th>Δ T</th><th>CI</th><th>Δ T</th><th>CI</th><th>Δ T</th><th>CI</th></tr></thead><tbody><tr><td>Bar-Donut</td><td>86 [60,117]</td><td></td><td>10 [-2,29]</td><td></td><td>50 [19,95]</td><td></td><td>197 [128,290]</td><td></td></tr><tr><td>Radial-Donut</td><td>1389 [863,1979]</td><td></td><td>129 [60,370]</td><td></td><td>608 [187,1984]</td><td></td><td>3430 [1953,4950]</td><td></td></tr><tr><td>Radial-Bar</td><td>1303 [783, 1899]</td><td></td><td>118 [45,360]</td><td></td><td>558 [127,1974]</td><td></td><td>3233 [1765,4745]</td><td></td></tr></tbody></table>

5.3.3 Results—Condensed and Summarized

We analyze, report, and interpret all our inferential statistics using interval estimation [62]. We report sample means of thresholds and 95% confidence intervals (CIs). We can be 95% confident that this interval includes the population mean. These results are highly representative of the plausible values of the true population mean, and the approach supports future replication efforts.

We set out to assess how quickly people can perform a simple data comparison task for small-scale visualizations on a smartwatch. Table 5.3 and Table 5.4 give an overview of the thresholds we collected for both studies. In our first study that required the comparison of two visual marks, which differed by 25%, participants were able to reach time thresholds as low as 160–210 ms for 7 and 12 data values for bar charts and donut charts. These techniques also scaled well to 24 data values, average times around 360 ms and 163 ms, respectively. Whereas the performance of radial bar charts dropped considerably from 286 ms for 7 data values to 766 ms for 12 and to 3600 ms for 24 data values. The same trends were also present in our second study, in which differences between marks were randomly generated. However, in the second study the threshold values went up by around 50 ms on average for bar charts and donut charts, and more substantially for radial bar charts by approximately 200 ms. Because of their overall poor performance, radial bar charts seem unsuitable for smartwatch applications that require quick comparisons if the number of data values is higher than seven.

These results give first evidence to answer our main research question. For a small number of data values, participants could estimate if
one data value is higher than the other one for all three *chart types* (with 70–92% accuracy) in times between 160 ms–560 ms. For bar charts and donut charts in particular, they could reliably perform this task when dealing with 24 data values, on average in ~450 ms using bar charts, and impressively in ~270 ms using donut charts. These thresholds are slightly above the 200 ms that the scene perception literature considers as a “glance” [162]. Even the threshold of the slowest technique with large data values (radial bar chart, with ~3900 ms) was as low as or lower than times reported in smartwatch usage studies [172, 210].

Where the differences between the three charts come from is an interesting question. Our design of the visual stimuli influenced our results in two ways. First, we used a minimum number of 7 data values (with at least 5 distractors) for the study stimuli, ruling out that participants could memorize the individual data items for a single chart [231]. Second, in terms of visual processing, we speculate that different visual routines [205] were involved. In particular, the *chart type* varied how participants had to associate the location of the black dots with the values to be compared. In our donut design, dots were placed at a 50% reference point. In bar and radial charts, they were placed at the beginning of the bar (i. e., aligned to the axis), to mimic label placement and to follow previous studies in the visualization literature [201]. For these two charts a (slower) spatial shift of attention may have been necessary [173] because participants had to trace a much longer distance. If the dots had been placed at the top of the bars in both types of bar charts the results may have been much faster, in particular for the radial bar chart. However, bar chart labels are more common at the bottom of the bars. The dot placement likely plays a role in the ease and speed of association, and it is possibly related to previous findings [201] on the dot location influencing the correctness of bar height estimation (dots in the middle made comparisons more correct).

In addition to the dot location, the variance of colors and lengths of the non-target bars likely also slowed the search for the endpoint [102]. Generally, conditions that demanded more attentional shifts, involved more complex target-to-endpoint (dot to value) association, yielded more imprecise spatial selection, and were more susceptible to surrounding variance resulted in higher response times. In addition, the strategies participants described suggest that participants did not necessarily perform the task on both marked targets. Instead they regularly performed an estimation of the size of one target and then made a guess, without consulting the other target. Given these strategies, the low thresholds our participants reached, in some cases hitting the minimum threshold of 100 ms without errors, are perhaps not surprising. Yet, this fast strategy can also be applicable in real life smartwatch use cases, for example, quickly glancing at one's physical activity on two
<table><caption>Table 5.4: Follow-up study with randomized data. Top: Mean thresholds (T) and confidence intervals (CI) across all sizes and for each *data size* given in milliseconds. Bottom: Pair-wise comparisons (ΔT) between *chart types* (CIs were adjusted for 3 pairwise comparisons with Bonferroni correction), and for each *data size* (CIs adjusted for 9 pairwise comparisons).</caption><thead><tr><th></th><th colspan="2">ALL SIZES</th><th colspan="2">7</th><th colspan="2">12</th><th colspan="2">24</th></tr><tr><th></th><th>T</th><th>CI</th><th>T</th><th>CI</th><th>T</th><th>CI</th><th>T</th><th>CI</th></tr></thead><tbody><tr><td>Bar</td><td>285</td><td>[252,333]</td><td>181</td><td>[167,209]</td><td>232</td><td>[204,276]</td><td>443</td><td>[376,538]</td></tr><tr><td>Donut</td><td>216</td><td>[180,337]</td><td>178</td><td>[162,231]</td><td>202</td><td>[175,262]</td><td>267</td><td>[197,507]</td></tr><tr><td>Radial</td><td>1772</td><td>[1513,2017]</td><td>560</td><td>[413,784]</td><td>853</td><td>[696,1151]</td><td>3905</td><td>[3273,4439]</td></tr></tbody></table><table><thead><tr><th></th><th>ΔT</th><th>CI</th><th>ΔT</th><th>CI</th><th>ΔT</th><th>CI</th><th>ΔT</th><th>CI</th></tr></thead><tbody><tr><td>Bar-Donut</td><td>69</td><td>[-6,114]</td><td>2</td><td>[-22,24]</td><td>30</td><td>[-14,83]</td><td>176</td><td>[-28,296]</td></tr><tr><td>Radial-Donut</td><td>1556</td><td>[1257,1814]</td><td>381</td><td>[199,696]</td><td>650</td><td>[457,1081]</td><td>3638</td><td>[2808,4295]</td></tr><tr><td>Radial-Bar</td><td>1487</td><td>[1173, 1756]</td><td>379</td><td>[195,717]</td><td>620</td><td>[428,1021]</td><td>3461</td><td>[2598,4201]</td></tr></tbody></table>

different days (Monday vs. Wednesday) or to an average bar drawn on the side of daily activity data.

### 5.3.4 *Design Considerations*

Situations in which visualizations need to be looked at quickly are common on smartwatches. For example, visualizations can convey several data values as part of a notification. Our study results showed that the heights of individual bars or donut sectors up to 24 data values can be assessed within a few hundred milliseconds. Radial bars up to 7 data values can also be assessed quickly. Radial bar charts of higher data values had much larger thresholds and varied widely across participants. Furthermore, they were the least preferred and gathered the lowest confidence scores. Another disadvantage of radial bar charts is the available bar width. Due to its encoding, the bars in radial bar charts are roughly half the size of a bar chart for the same number of items, making any discrimination task more challenging.

The results acquired from our studies also highlight that both bar charts and donut charts provide similar results. For 7 and 12 data values, the differences are under 100 ms while for 24 it is under 200 ms. These relatively small time differences lead us to recommend that when creating visualizations, designers can use them almost interchangeably. Given the similar performance of donut and pie charts found in previous studies [193], pie charts may also be possible candidates.

Nevertheless, the stable performance of donut charts across all *data sizes* (going from 7 to 24 data values only increases the threshold by
<100 ms on average) may indicate that this visualization could scale to more than 24 data values for the particular task. If designers are considering even larger *data sizes*, then this visualization could be considered—keeping in mind the readability of the chart’s labels and segment color may be impacted with more data values.

It is not straightforward to speculate on the generalizability of our results to larger display sizes. The charts we chose, their design (e. g., no labels, axes, grid lines, tick marks), as well as the number of data items we tested (7, 12, 24) were influenced by our smartwatch usage scenario. It is even possible that our small display size is beneficial for this type of task, as the entire visualization covers on average a small visual angle (with targets still being in the central vision field) and can be seen with little to no eye movement. Nevertheless, further research is required to verify this. In addition, the task that requires performing comparisons, may be more important to smartwatch or smartphone usage, during which people often quickly glance at their devices.

In a later paper we replicated this study [17] on a laptop computer to see whether we could simplify our technical setup by showing smartwatch-sized stimuli on a larger screen. We also tested larger stimuli to see whether the thresholds were influenced by display size. In general showing the smartwatch-sized stimuli on a laptop instead of an actual smartwatch led to similar overall trends. We did not find evidence of a difference between Bar 📉 and Donut O️ except for 24 *data values* where Bar 📉 was slightly slower than Donut O️. All threshold averages were slightly higher for the smartwatch-sized stimuli on the laptop screen compared to the original smartwatch study; in the order of 100 ms for Bar 📉 and Donut O️ but in the order of seconds for Radial G. Based on previous work, we also expected to see a difference for the larger stimulus size. Yet, for our simple data comparison task we found no clear evidence that the size of the stimulus had an effect on the answer thresholds. Both Bar 📉 as well as Donut O️ could still be read within less than 360 ms. We observed the same trends as in the original smartwatch study (Bar 📉 and Donut O️ outperform Radial G). In contrast to previous studies [107, 159]—who found that completion time increased as chart size increased—we saw an overall decline in completion time for the larger stimuli. This effect needs to be studied further. In our study, in contrast to previous work, participants did not explicitly have to choose their own error vs. completion time tradeoff as each trial had a pre-determined completion time, which perhaps played a role.

Comparing the rankings, the Bar 📉 was mostly ranked first, followed by the Donut O️ and last Radial G for both preference and confidence across both *stimuli sizes*. This result was not the same as in the smartwatch study where the donut was preferred and participants felt more confident with it. This could indicate that for a smartwatch
people prefer a different type of chart than for a laptop computer. It could also be that familiarity with Donut ○ was rated a bit lower in our replication study (M = 3.5, SD = 1.6) than in the smartwatch study (M = 4.28, SD = 1.13), however, the difference was minimal.

5.4 WHAT DID WE LEARN ABOUT MICRO VISUALIZATIONS?

Research on visualizations for smartwatches is still at the beginning. As such, my past work has involved a variety of questions and research methodologies involving thinking about design methods, to understanding current practices, and performing first evaluations that considered both issues of mediated and external complexities of micro visualizations for smartwatches. We learned that context-specific smartwatch visualizations can be effectively envisioned with an ideation methodology that involves being in context and thinking about one's own information needs. In terms of current use, we saw that there are plenty of untapped opportunities for micro visualization on smartwatch faces—but also that many research questions are open as to how effective and preferred replacing current text or text+icon representations with visualizations would be. Generally, current smartwatch visualizations have a low external complexity in terms of number of dimensions or data values present. Studying more externally complex visualizations for smartwatch faces would be interesting. In our glanceability study, we specifically targeted the mediated complexity issue of the mobile aspect of smartwatch visualizations. We saw that simple representations in the form of bars and donuts could be quickly assessed for value comparison tasks—even when up to 21 data values were present. Radial bar charts were most impacted by increasing the number of data items.

On the topic my students and collaborators currently have several projects ongoing. For example, we are working on studies that tested thresholds for different tasks and multiple much smaller visualizations than we tested in the first glanceability study. With my PhD student Lijie Yao, we are also exploring the mediated complexity of display movement and how it affects people's perception of data encodings. My PhD student Alaul Islam is currently working on a data-driven project where we explore the visualization of sleep data on smartwatches in more depth.
This manuscript started with a definition and with an overview of past research on micro visualization. Next, I covered my own work on glyphs, word-scale visualizations, and micro visualizations for mobile, and in particular wearable, devices. My past research projects have in common that they focused on small-scale data representations that were meant to be seen at a visual angles of <5°. Each of the covered research projects had a specific focus and covered different aspects of micro visualization complexity. Yet, many questions in this space remain unanswered, some even unaddressed. One of the difficulties with studying the perception of data visualizations is the many possible combinations of data, tasks, or encodings one could test. Opportunities for new insight seem overwhelming. For this reason we began to conduct literature reviews on past data glyph studies and why I applaud efforts such as Beck and Weiskopf [12]'s overview on how word-scale graphics have been used in scientific texts. In our overview on the smartwatch faces wearers currently use, we made similar progress towards understanding which type and how data is currently represented on smartwatches. Future studies in this space of micro visualizations can build on these findings and ground tasks and data choices in existing work and practice.

What we learned from our quantitative studies broadly was that with certain encodings people were quite effective and fast in completing specific tasks with micro visualizations. Yet, encodings need to be carefully chosen. This is the case for glyphs as well as micro visualizations on smartwatches (I have not yet conducted perception studies on word-scale visualizations). Next, I cover some of the work that I consider useful to focus on in the future.

## 6.1 THE ROLE OF CONTEXT

The context of a visualization's use impacts how we need to design and interact with a visualization. For example, in our work on word-scale visualizations the text was a factor that increased mediated complexity as we had to design interactions specific to the embedding of the visualization in text. We also saw how designers created text-to-visualization connections that would not have been necessary in other settings.

One contextual factor I am currently interested in is motion of mobile micro visualizations, that is visualizations that are themselves moving. In an IEEE InfoVis poster [223] lead authored by PhD student
Lijie Yao (co-advised by myself and Anastasia Bezerianos), we covered a first design space for visualization in motion more broadly and plan to extend the work for micro visualizations on smartwatches, fitness trackers, or mobile phones that are frequently used on-the-go. For a future submission we are currently working on an extended design space for visualization in motion and two crowdsourcing experiments to understand how well viewers can read data from moving visualizations. Our work is motivated by the prevalence of visualizations in sports and video games where a static viewer reads data attached to moving characters, athletes, or equipment. Beyond this usage context, however, visualizations also frequently appear in contexts that involves motion of the viewer or both viewer and visualization. Imagine, for example, a runner as a moving observer who passes a static map-based visualization or who reads visualizations on a fitness band while bouncing up and down during a run. Our preliminary results show that people can complete simple magnitude estimation tasks on moving visualizations quite effectively. I expect that we will see more micro visualizations on TVs and larger displays in the gaming or sports analytics context where we need to understand the influence of movement as well as changing backgrounds and perspectives. Micro visualizations in augmented or virtual reality environments will face similar mediated complexity issues.

In the future, we will also see displays of different shapes emerge that may increase mediated complexity. In our ideation activity for smartwatch visualizations, for example, many participants drew visualizations on watch straps and first prototypes have emerged already [124]. Similarly, other types of embedded screens in clothing, curved mobile screens or wristbands will emerge and we have to understand how people perceive data when seen on these non-flat displays.

In addition, contexts with divided attention require further research attention. Here, viewers can only afford quick glances at visualizations. Similarly difficult situations arise where viewers might be focused on a visualization but are constantly interrupted. Visualizations in these settings are very difficult to evaluate and test and future work is needed, in particular as micro visualizations on smartphones or wearable devices commonly have to deal with this type of mediated complexity.

## 6.2 MICRO VISUALIZATION APPLICATION, DESIGN AND SCALABILITY

Some past work has, intuitively, recommended for small data representations to be simple in design and to encode only a few data values and/or dimensions. Studying encoding limits in more depth would be fruitful. While some of these studies mean going large—adding more dimensions, more data values, more encoding types—some studies
should go small by studying visualizations that become increasingly
small. Some of the past studies on visualization size (Section 2.4)
have consistently shown that people prefer larger visualizations but
we have also seen that certain tasks, with certain encodings, could
be quite effectively and correctly completed by participants. In our
current work we are looking at adding more visualizations on smart-
watch faces and testing how quickly people can perform tasks that
involve multiple much smaller visualizations than we did in our first
glanceability study. As such, there are several avenues of scalability to
explore: more data, smaller size, and more visualizations.

In terms of design, we have also began to consider the role of
small visualizations in embedded and situated scenarios. With Pierre
Dragicevic, Yvonne Jansen, and Martin Hachet—and in collaboration
with researchers from the University of Calgary—we are currently
working on an ANR funded project called EMBER to explore situated
visualizations for personal analytics. In a situated data visualization
[218], the data is directly visualized near the physical space, object, or
person it refers to. Situated data visualizations have many potential
benefits: they can surface information in the physical environment and
allow viewers to interpret data in-context; they can be tailored to high-
light spatial connections between data and the physical environment,
making it easier to make decisions and act on the physical world in
response to the insights gained; and they can embed data into physical
environments so that it remains visible over time, making it easier
to monitor changes, observe patterns over time and collaborate with
other people. Situated micro visualizations have, in particular, poten-
tial for empowering people to make effective use of their personal data
in a variety of application contexts and for performing personal analyt-
ics. For example, a person may attach small e-ink displays embedded
with sensors at various locations of their house or their workplace,
in order to better understand their use of space, of equipment, or of
energy resources. Alternatively, a person who wishes to exercise more
may use small GPS-enabled wearable displays to visualize their past
running performance in-place. Tiny data displays may also take the
form of “data jewellery” to help people share personal information in
social settings. Allowing viewers to interpret data in-context and take
action in response to it. Because they make it possible to explore and
act on data in relevant physical locations, they have the potential to
radically transform how we relate to data. They can help people lever-
age information about their own activities in order to adapt and tailor
their personal spaces, make more informed decisions, or improve their
well-being.
## 6.3 INTERACTION

Few interactions with micro visualizations have so far been imple-
mented in existing systems. Interactions in glyph small multiple set-
tings could include features similar to some of those we discussed
in our work on interactive word-scale visualizations. Expanding our
work on interactive word-scale visualizations to better understand the
role of data in interactive reading environments would also be useful
as people are spending a lot of time reading on mobile devices or
e-readers.

We also need to better understand the types of interactions that may
be useful to control smartwatch micro visualizations. At this point,
mostly simple swipes and taps are used to switch between different
representations or time intervals but more complex interactions have
been explored for touch interactions on desktop-sized [219] or tablet-
sized charts [180]. If they would be useful and how they can be
used when the “fat finger problem” becomes even more acute is an
interesting avenue for research.

More broadly, it will be important to study further what kind of
interactions people actually need or want to perform with micro
visualizations. For example, does it make sense to allow a runner to
interactively switch the types of data or the data granularity shown on
a smartwatch during a race? Similarly, the scalability of interactions
such as brushing and linking across many micro visualizations—or
even devices carrying micro visualizations—needs further attention.

6.4 CLOSING REMARKS

In this manuscript I only covered some of the research I conducted
since my PhD. I focused on how my work evolved towards the broader
topic of micro visualizations. I left many other topics uncovered that
would show my broad interest in different areas of visualization
research. In general, my research has often been motivated by possibil-
ities of new technologies—such as my work on smartwatchs/fitness
trackers or tabletop and wall displays. Sometimes, I have been mo-
tivated by a simple curiosity about data—such as for my work on
Bitcoin data visualizations or work on understanding the field of Visu-
alization more broadly through published papers or keywords used.
Sometimes, my work is motivated by a personal need, such my past
work on understanding the role and practice of evaluating visualiza-
tions. I am privileged in having a research position that allows me the
freedom to explore a variety of topics of interest and a set of amazing
collaborators and students who go and have gone on these research
adventures with me. I cannot thank them enough.
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CURRICULUM VITAE

Excluded for the online version of this manuscript. My CV is available upon request.
<figure><img src="image_100.png" /><figcaption>Figure 6.1: Timeline of my work on micro visualizations including timelines of co-advised or collaborating PhD students and advised PostDocs, publications, co-organized workshops, and leaves of absence.</figcaption></figure>
COLOPHON

This document was typeset using the typographical look-and-feel classicthesis developed by André Miede as well as Ivo Pletikosić.