Javad Sadeghi
email: jsadeghi@ucalgary.ca

Charles Perin
email: charles.perin@ucalgary.ca

Tamara Flemisch
email: tamara.flemisch@gmail.com

Mark Hancock
email: mark.hancock@uwaterloo.ca

Sheelagh Carpendale
email: sheelagh@ucalgary.ca

Flexible Trees: Sketching Tree Layouts

Keywords: Human-centered computing → Information visualization, Gestural input, Dendrograms, Cladograms, Visualization, trees, sketching, interaction, authoring, infographics

We introduce Flexible Trees, a sketch-based layout adjustment technique. Although numerous tree layout algorithms exist, these algorithms are usually bound to fit within standard shapes such as rectangles, circles and triangles. In order to provide the possibility of interactively customizing a tree layout, we offer a free-form sketchbased interaction through which one can re-define the boundary constraints for the tree layouts by combining ray-line intersection and line segment intersection. Flexible Trees offer topology preserving adjustments; can be used with a variety of tree layouts; and offer a simple way of authoring tree layouts for infographic purposes.

INTRODUCTION

We present Flexible Trees, a sketch-based technique for deforming tree layouts. Flexible Trees offer a new approach to creating emphasis and focus+context, and a simple way of creating aesthetically pleasing customized hierarchical data visualizations.

Hierarchical data are ubiquitous (e.g., family trees and file system directory trees), and hierarchical data visualizations have a long history. Tree layouts are certainly the most standard family of hierarchical data representations. They usually represent data entities as the nodes of a tree, and the relationships between these data entities as links, adjacency, or nesting [START_REF] Schulz | The design space of implicit hierarchy visualization: A survey[END_REF] (see [START_REF] Schulz | .net: A tree visualization reference[END_REF] for an overview). While these tree layouts provide a variety of representations, they are generally designed to fit into formal shapes such as rectangles, circles, and triangles; and they emphasize the root of the tree.

Constraining tree visualizations to pre-defined shapes makes these layouts ill-suited to narrative visualizations [START_REF] Segel | Narrative visualization: Telling stories with data[END_REF] and storytelling [START_REF] Lee | Sketchstory: Telling more engaging stories with data through freeform sketching[END_REF] where flexibility, authoring capabilities, and aesthetics are important. For example, one may want to illustrate a blog article with an aesthetically pleasing tree visualization that visually relates to the article; or may want to emphasize particular parts of the data, e.g., a sub-tree or a node. To empower non-experts in visualization, we designed a sketch-based interaction to deform tree layouts (see Figure 1) so that one can freeform layouts sketch directly instead of using buttons, menus and dragging operations.

We first discuss the literature related to Flexible Trees. Then we describe the Flexible Trees algorithm. Finally, we discuss the possibilities for authoring tree visualizations for infographic purposes.

RELATED WORK

Our work with Flexible Trees relates to research in the areas of tree layouts, sketch based interaction, and data story telling.

General background about trees

Tree layouts for visualizing hierarchical data can be implicit or explicit [START_REF] Schulz | The design space of implicit hierarchy visualization: A survey[END_REF]. A treemap [START_REF] Johnson | Tree-maps: a space-filling approach to the visualization of hierarchical information structures[END_REF], where each child node is nested inside its parent, is an implicit layout which specifies each node size according to the underlying data, and the size of a node depends on the size of its children. Implicit layouts are not amenable to freeform deformation as it would interfere with the data representation.

In contrast, explicit layouts such as Reingold and Tilford's trees [START_REF] Reingold | Tidier drawings of trees[END_REF] encode data relationship in the tree structure, which can be maintained when distorting the tree. However, such tree layouts are automatically computed and are not interactively adjustable. Flexible Trees offer layout adjustment and personalization through sketching.

Sketch-based Interaction

Hand-drawn sketching is known to be effective in promoting innovation, creativity, and thinking [START_REF] Buxton | Sketching User Experiences: Getting the Design Right and the Right Design[END_REF][START_REF] Greenberg | Sketching user experiences: The workbook[END_REF]. Studies have investigated how people manipulate representations, e.g., observing how people spatially arrange nodes and links [START_REF] Van Ham | Perceptual organization in usergenerated graph layouts[END_REF]. These studies showed that pre-defined layouts are not in accordance with layouts people draw.

Researchers have considered sketch-based interaction [START_REF] Sutherland | Sketch pad a man-machine graphical communication system[END_REF] for visualization [START_REF] Lee | Sketchstory: Telling more engaging stories with data through freeform sketching[END_REF][START_REF] Walny | Understanding pen and touch interaction for data exploration on interactive whiteboards[END_REF] as pre-defined visualizations can limit people in expressing their thinking about data [START_REF] Walny | An Exploratory Study of Data Sketching for Visual Representation[END_REF]. Also, NapkinVis [START_REF] Chao | Napkinvis: Rapid pen-centric authoring of improvisational visualizations[END_REF] and SketchVis [START_REF] Browne | Data analysis on interactive whiteboards through sketch-based interaction[END_REF] investigate sketching simple visualizations. However, we found no work about sketching tree layouts.

Authors' version

Data Story Telling

The visualization community is recognizing the importance of narrative visualization [START_REF] Segel | Narrative visualization: Telling stories with data[END_REF], and of telling data stories [START_REF] Lee | Sketchstory: Telling more engaging stories with data through freeform sketching[END_REF]. Throughout human history people have drawn trees to convey messages about hierarchical data. These hand-crafted trees differ widely: they usually have irregular layouts, make extensive use of metaphors and personalized styles, and contextualize data to convey a message. These historical trees relate strongly to modern infographics (see Lima's book [START_REF] Lima | The Book of Trees: Visualizing Branches of Knowledge[END_REF] for a history of tree visualizations).

These growing movements towards both sketch-based interaction and narrative visualization have many benefits that have not yet been applied to the popular tree layouts.

FLEXIBLE TREES

Flexible Trees are a way of deforming tree layouts via sketching. We describe how to interpret sketched strokes; then we detail how to deform implicit tree layouts using ray casting and line intersections.

Sketch Interpretation

Let a sequence of n sketch points p i = (x i , y i ) be a stroke S = {p 1 , p 2 , . . . , p n }. Sketched strokes have noisy and unevenly distributed sketch points. We filter the sketch points using four-point interpolatory subdivision [START_REF] Dyn | A 4-point interpolatory subdivision scheme for curve design[END_REF] (DLG subdivision), which applies subdivision masks on the coarse points c i to find the fine points f i :

f 2i = c i , f 2i+1 = - 1 16 c i-1 + 9 16 c i + 9 16 c i+1 - 1 16 c i+2 (Eq. 1)
which satisfies C 1 continuity at the limit. To filter the input strokes, we go in the reverse direction i.e. start from fine points to find the coarse points. We first apply the reverse DLG subdivision filter [START_REF] Bartels | Reversing subdivision rules: local linear conditions and observations on inner products[END_REF]:

c i = 1 64 f 2i-4 + 0 f 2i-3 -1 8 f 2i-2 + 1 4 f 2i-1 + 23 32 f 2i + 1 4 f 2i+1 -1 8 f 2i+2 + 0 f 2i+3 + 1 64 f 2i+4 (Eq. 2)
three times on the fine points f i to find coarse points c i . This filter discards high-frequency information and noise. Resulting coarse points are then subdivided three times using DLG subdivision (see Eq. 1) to create the filtered stroke. To filter open curves, we use the insights from J-splines [12] to derive boundary filters:

f 0 = c 0 , f 1 = 7 16 c 0 + 10 16 c 1 - 1 16 c 2 (Eq. 3)
to find the beginning fine points

F = { f 0 , f 1 } from the beginning coarse points C = {c 0 , c 1 , c 2 }.
We then adjust the indexes to find the ending fine points. The rest of the fine points are found from Eq. 1. Next, we derive corresponding reverse subdivision filters:

c 0 = f 0 , c 1 = 7 16 f 0 -f 1 + 26 16 f 2 + 0 f 3 - 1 16 f 4 (Eq. 4)
to find the beginning coarse points C = {c 0 , c 1 } from the beginning fine points

F = { f 0 , f 1 , f 2 , f 3 , f 4 }.
We adjust the indexes to find the ending coarse points and Eq. 2 gives the rest of the coarse points. Finally, the control points are simply the coarse points. With this approach, one can manipulate the stroke by moving control points directly on the stroke -which is not the case with B-Splines.

Layout Deformation: Radial Cladogram

We first illustrate Flexible Trees with the Radial Cladogram (see Figure 2). This explicit tree layout arranges the nodes of a tree in a circle centered around the root node: 1) the leaf nodes are evenly spaced on the circumference of the circle; 2) the internal area of the circle is sliced into d concentric rings; 3) each parent node is positioned on the inner ring corresponding to its depth such that it bisects the angular distance between its children; 4) finally, the edge originating at a child is drawn towards the root; when this edge intersects the concentric ring of its parent it forms a join and completes the edge line of the ring. Figure 2(a) shows a Radial Cladogram and a sketched stroke S that sets the layout boundary.

We first find the closest intersection point between the rays shot from the tree's center to the borders of the window. Rays are shot at uniform angular intervals, with one ray being shot for each leaf node. A ray R(t) = p + td is defined by a point p = (p x , p y ) and a unit direction d = (cos α, sin α); t ≥ 0 is a scalar indicating time. Let w and h be the width and height of the window. For each ray, we compute the intersection times with each border, with t le f t = -p x cos α , t right = w-p x cos α , t bottom = -p y sin α , and t top = h-p y sin α . Given t * = min(t le f t ,t right ,t bottom ,t top ), we find the closest intersection point p * = (p x + t * cos α, p y + t * sin α) for each ray.

Next, we find all intersection points between the line segments running from root to p * s and the line segments of S as follows. Let x 2 -x 1 to find the coordinates of the intersection point (x, y):

x = m * a 1 -b 1 +y 1 -mx 1 m * -m y = mm * a 1 -mb 1 -mm * x 1 +m * y 1 m * -m (Eq. 5)
If S intersects multiple times with the ray, we choose the intersection closest to the root. The last step is to adjust the radius of the Radial Cladogram if a ray intersects the stroke. We calculate the distance between the intersection point and the root node and use it as the leaf node's new radius. Finally, we update the position of all ancestors of this leaf node (except the root) using the average radius of the leaf nodes in the corresponding subtrees, as shown in Figure 2(b).

Other Layout Deformations

We have detailed the Flexible Trees algorithm for the Radial Cladogram (a node-link radial layout). We now describe how to adapt the algorithm to a rectilinear node-link layout (Cladogram), a rectilinear space-filling layout (Icicle Plot) and a radial space filling layout (Sunburst). Figure 3 shows these layouts being deformed.

The Cladogram, or dendogram, is the rectilinear version of the Radial Cladogram. All leaves are drawn uniformly on the bottom of the tree, regardless of their distance from the root, or depth, d. Then the space between root and leaves is equally sliced according to d at that point. Parents are aligned to be at the center of their children, and the edge lines are drawn from parents to their children with a 90 • angle turn at the same horizontal position of the children.

Implementation: As this layout only allows changes to the area below the root node, we need only find the intersections between S and the vertical line segments V from the root to the bottom border. One vertical line v ∈ V is shot for each leaf vertex. We find the closest intersection point p * for v and update the layout to be bound at p * similarly to updating the radius of the Radial Cladogram tree.

The Icicle Plot [START_REF] Kruskal | Icicle plot: Better displays for hierarchical clustering[END_REF] is a rectilinear space-filling layout. It only represents nodes -as filled rectangles; edges are implied by adjacency. All nodes have the same height and are drawn at their exact depth. Parent nodes are sized according to their number of children. Implementation: The approach is similar to the one for the Cladogram. However, because nodes are arranged at their exact depth, the intersection between S and v is not necessarily a leaf node. Thus, we first uniformly divide the vertical distance between the root and the intersection point; then we locate each node based on its depth.

The Sunburst [START_REF] Stasko | Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations[END_REF] is the radial version of the Icicle Plot.

Implementation: The approach uses the ray casting and line segment intersection algorithms of the Radial Cladogram; and arranges the nodes at the level proportional to their depth as in the Icicle Plot. These levels are represented by annuli between co-centered rings around the root. After finding an intersection point p * , we uniformly divide the radial distance between the root and p * to find the inner and outer radius of the annuli using the depth of the node.

DISCUSSION AND FUTURE WORK

We discuss applications of sketch-based layout and show the applicability of Flexible Trees to the authoring of infographics.

Layout Deformation via Sketching

Our sketch interpretation method can interpret freeform shapes. However, our layout deformation algorithm uses the closest stroke intersection after ray casting. We plan to investigate more complex shapes such as concave shapes by adapting the curvature of links and moving nodes, e.g., using physics.

Our algorithm produces fine results for node-link layouts. However, deformed space-filling layouts are more difficult to understand. We plan to explore new strategies for such layouts in future work.

Finally, we implemented the algorithm for four implicit layouts but adapting adjacency and nested layouts is a challenge. For example, drawing an arbitrary shape and fitting a treemap into it raises problems regarding maintaining the relative sizes of the nodes.

Flexible Trees for Focus+Context

Flexible Trees can create focus+context views. Using a sketched curve to expand part of the layout is similar to what can be achieved with a fisheye magnifying glass, but it acts differently. Figure 4 shows a sketched focus+context view of a Radial Cladogram: the leaf nodes of the condensed tree (a) are difficult to distinguish visually; but a simple hand-sketched stroke can open up the subtrees sitting next to that curve (b). It makes the nodes readable locally.

Flexible Trees for Infographics

Flexible Trees are extremely suitable to generating infographics. By drawing different shapes one can add more information about the tree itself (e.g., provide the context of the data) and about its data (e.g., provide information about the tree nodes and links). In this section, we provide examples of infographics that we created by modifying the original outputs of the algorithm in an SVG editor.

Figure 5 shows a tree in the shape of a cup that represents a subset of the SCAA Flavor Wheel dataset. The shape does not add data but it contextualizes the tree by conveying the topic. The coffee cup shape hints at a beverage and the brown colour is typical for coffee.

Figure 7 shows data from WorldLifeExpectancy. This pie chart shows the top causes of death for the United States, Germany and India. The shape adds quantitative data: slice angles represent birth rates for each country. In addition to conveying the information contained in the trees, this infographics puts this information in perspective by also comparing the areas of the trees in the pie chart. Figure 6 shows data from WorldLifeExpectancy. Color distribution and slice angles represent the impact of each cause of death on total percentage of deaths. The shape adds information to the tree: it conveys the country that the data is related to. Filling parts of the countries also conveys quantities like a pie chart does.

Flexible Trees benefit infographics as they make it possible to include context (e.g., coffee cup), qualitative data (e.g., country shapes), and quantitative data (e.g., birth in pie chart layout).

5.

Flexible is a topology-preserving distortion technique adapting the layout of trees according to hand-sketched strokes.

This initial exploration of sketch-based layout paves the way for further research. That includes exploring hand-sketching algorithms for implicit and nested tree layouts; and beyond trees, applying the method to e.g., graph drawing. Generalizing this approach has the potential to reach a large number of people, as it makes it possible to easily and rapidly create layouts of customized shapes. Our discussion of the applicability to infographics generalizes to other visualization types such as graph visualizations, and contributes to filling the gap between Infovis research and infographic design.

Figure 1 :

 1 Figure 1: Tree layouts created using Flexible Trees, with the shapes of a city skyline, an eye, and Twitter's logo.

Figure 2 :

 2 Figure 2: Finding intersection points a) of the rays with the window borders, and b) on the stroke, for a Radial Cladogram.

p * 1 =

 1 (a 1 , b 1 ) and p * 2 = (a 2 , b 2 ) be the two extremities of the line segment running from the root to p * , and p 1 = (x 1 , y 1 ), p 2 = (x 2 , y 2 ) a line segment of S. Then we use the slopes m * = b 2 -b 1 a 2 -a 1 and m = y 2 -y 1

Figure 3 :

 3 Figure 3: Adapting a Cladogram, an Icicle plot, and a Sunburst layout.

Figure 4 :Figure 5 :

 45 Figure 4: Focus+Context effect using free-form strokes on a dense Radial Cladogram layout.

Figure 6 :

 6 Figure 6: Pie chart showing the birth rates of Germany, the United States, and India. Inside the pie slices the trees represent the most common death causes for each country.

Figure 7 :

 7 Figure 7: Infographics using small multiples of deformed trees in the shape of Germany, the United States, and India. The trees and the coloured areas represent the most common causes of death and their percentage.

ACKNOWLEDGMENTS

This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), SMART Technologies, and Alberta Innovates Technology Futures (AITF). We also thank Lindsay MacDonald for proofreading the paper.


Fadi Botros
email: botrosfadi@gmail.com

Charles Perin
email: charles.perin@ucalgary.cabon

Adriel Aseniero

Sheelagh Carpendale
email: sheelagh@ucalgary.ca

Go and Grow: Mapping Personal Data to a Living Plant

Keywords: Human-centered computing → Information visualization; Fitness, Living Visualization, Personal Visualization, Plant

Motivation is a key factor for introducing and maintaining healthy changes in behaviour. However, typical visualization methods (e.g., bar-, pie-, and line charts) hardly motivate individuals. We investigate how a plant-a living visualization-whose health relies on the plant owner's level of activity, can engage people in tracking and self-reflecting on their fitness data. To address this question, we designed, implemented, and studied Go & Grow, a living plant that receives water proportionally to its owner's activity. Our six-week qualitative study with ten participants suggests that living visualizations have qualities that their digital counterparts do not have. This includes people feeling: emotionally connected to their plant; sentiments such as pride and guilt; and responsibility towards their plant. Based on this study, we introduce the Goal Motivation Model, a model considering the diversity of individuals, thus supporting and encouraging a diversity of strategies for accomplishing goals.

INTRODUCTION

Citizens of industrialized nations are subject to sedentary behaviour (prolonged sitting at home, work, or in cars) which can lead to health complications such as obesity [START_REF] Hill | Obesity and the environment: where do we go from here?[END_REF]. Adopting an active lifestyle is difficult [START_REF] Marcus | Physical activity behavior change: issues in adoption and maintenance[END_REF], but the simple act of self-monitoring can lead to healthy behaviour change [START_REF] Speck | Effects of a minimal intervention to increase physical activity in women: daily activity records[END_REF]. More and more people are now self-monitoring with the emergence of tracking devices which measure health data (e.g., steps, calories, and heart rate) [START_REF] Speck | Effects of a minimal intervention to increase physical activity in women: daily activity records[END_REF]. However, the typical way of visualizing such data is through standard bar and line charts, which hardly motivate people [START_REF] Bartram | Design challenges and opportunities for eco-feedback in the home[END_REF]. Finding motivation is key to ushering in behaviour change [START_REF] Li | A stage-based model of personal informatics systems[END_REF]. Thus, visualizing activity data in engaging ways has been studied using e.g., virtual metaphors [START_REF] Consolvo | Activity sensing in the wild: A field trial of ubifit garden[END_REF][START_REF] Lin | Fish'n'steps: Encouraging physical activity with an interactive computer game[END_REF], physical metaphors [START_REF] Khot | Understanding physical activity through 3d printed material artifacts[END_REF][START_REF] Lee | Patina engraver: Visualizing activity logs as patina in fashionable trackers[END_REF][START_REF] Stusak | Activity sculptures: Exploring the impact of physical visualizations on running activity[END_REF] and video games [START_REF] Berkovsky | Physical activity motivating games: Virtual rewards for real activity[END_REF][START_REF] Nenonen | Using heart rate to control an interactive game[END_REF]. We explore living visualizations to extend this trend. While Personal Informatics (PI) tools typically rely on one's self-motivation, we relate the health of a plant to one's activity level. Taking care of a plant can be a strong motivation to maintain healthy activity levels. Constant watering is crucial for a plant's longevity, but a single day of under-watering will not result in its death. Likewise, people need regular exercise to remain healthy.

Based on design criteria gathered from existing literature, we implemented Go & Grow, a computer-equipped cyclamen flowering plant that waters itself different amounts of water everyday according to one's progress towards a daily step goal (see Figure 1). By stimulating the plant with varying amounts of water, the plant becomes a living visualization of the individual's step data.

Results from a six-week long qualitative study indicate that participants gained an increased awareness of their activity level and adapted their daily behaviour. They developed feelings that standard visualizations may not trigger: emotional connection to the plant, pride, guilt, and responsibility. We further introduce the Goal Motivation Model, composed of four levels of goal abstraction surrounding individuals who track their fitness data. We discuss how this model considers the diversity of individuals, and thus supports and encourages an array of strategies for accomplishing goals.

BACKGROUND AND RELATED WORK

The emergence of personal trackers has prompted the research area of PI. PI focuses on helping people make use of personal data (including fitness data) for self-reflection, gaining self-knowledge, and making actionable decisions for healthy lifestyle changes [START_REF] Li | A stage-based model of personal informatics systems[END_REF]. However, previous work in PI has shown that designing tools that help people make lifestyle changes is difficult [START_REF] Li | A stage-based model of personal informatics systems[END_REF], and that people quickly stop using activity trackers. In a recent study, 65% and 75% of participants stopped after two and four weeks respectively [START_REF] Shih | Use and adoption challenges of wearable activity trackers[END_REF]; this happens regardless of fitness change [START_REF] Speck | Effects of a minimal intervention to increase physical activity in women: daily activity records[END_REF]. This may be explained by difficulties from finding motivation and interpreting visualizations hindering people from continued use of PI tools [START_REF] Li | A stage-based model of personal informatics systems[END_REF].

We review research from Information Visualization (Infovis) and HCI that relates to engagement as a strategy for motivation, and describe work that inspired us to use a living plant as a visualization.

Authors' version

Engagement as a Source of Motivation

Engagement is a broad term which, in our context, we define as a state of emotional involvement that leads one to commit or invest in a certain activity. Ideally, engagement leads one to commit to an activity in order to achieve or maintain good fitness levels.

Engaging through Personable Visualizations. One way of engaging people with their data is to provide them with insightful and personable visualizations. Infovis is well suited for this, as its purpose is to help people to gain insights about data [START_REF] North | Toward measuring visualization insight[END_REF]. However, applications for individuals who track their own fitness data have different goals and criteria from traditional Infovis applicationswhich focus on supporting experts working with large amounts of data. Efficiency is not necessarily the measure of success for personal or casual visualizations [START_REF] Huang | Personal visualization and personal visual analytics[END_REF][START_REF] Pousman | Casual information visualization: Depictions of data in everyday life[END_REF] intended for use in everyday life. Instead, other factors such as motivation, aesthetics, playfulness and pleasure are essential [START_REF] Stusak | Activity sculptures: Exploring the impact of physical visualizations on running activity[END_REF], as these can engage people in exploring their data, in having moments of self-reflection [START_REF] Li | Understanding my data, myself: Supporting self-reflection with ubicomp technologies[END_REF][START_REF] Thudt | Visual mementos: Reflecting memories with personal data[END_REF], and in gaining insights in non-professional contexts [START_REF] Rodgers | Exploring ambient and artistic visualization for residential energy use feedback[END_REF].

Companies such as Fitbit and Garmin provide online dashboards for visualizing fitness data using standard visualizations such as bar-, pie-, and line-charts. While these visualizations may be simple enough to be understood by most people, they do not seem to be motivating, as people quickly stop using them [START_REF] Shih | Use and adoption challenges of wearable activity trackers[END_REF]. In contrast, nontraditional or abstract visual representations (i.e. without precise numbers) improve people's awareness of their data [START_REF] Consolvo | Theory-driven design strategies for technologies that support behavior change in everyday life[END_REF]. This is confirmed by evaluations of technologies such as: Ubifit Garden which uses a virtual garden to motivate people to be more active [START_REF] Consolvo | Activity sensing in the wild: A field trial of ubifit garden[END_REF]; and Fish'n'Steps which uses a virtual fish and tank to convey step counts [START_REF] Lin | Fish'n'steps: Encouraging physical activity with an interactive computer game[END_REF]. Abstract visualizations can also provide a sense of privacy while still conveying high-level information [START_REF] Bartram | Design challenges and opportunities for eco-feedback in the home[END_REF].

These observations suggest that personable visualizations, which are both personal "interactive visual data representations for use in a personal context" [START_REF] Huang | Personal visualization and personal visual analytics[END_REF] and affective because they are pleasant and likeable, are a promising avenue for motivating people in collecting and reflecting on their everyday life data.

Applying Cognitive Behavioural Strategies and Social Context. Goal-setting [START_REF] Locke | Building a practically useful theory of goal setting and task motivation: A 35-year odyssey[END_REF], self-assessment, and monitoring of progress [START_REF] Hardeman | Interventions to prevent weight gain: a systematic review of psychological models and behaviour change methods[END_REF] are well-known cognitive strategies to help people in behaviour change. In an HCI context, such strategies can increase people's awareness of their data and motivate them to exercise more [START_REF] Harrison | Tracking physical activity: Problems related to running longitudinal studies with commercial devices[END_REF].

Berkovsky et al. [START_REF] Berkovsky | Physical activity motivating games: Virtual rewards for real activity[END_REF] used virtual game rewards in exchange for physical activity, and Nenonen et al. [START_REF] Nenonen | Using heart rate to control an interactive game[END_REF] used real-time heart rate data to control an interactive biathlon computer game. Both showed that goals and achievements can encourage people to be more active.

Industrial technologies (e.g., Fitbit, Garmin) focus on using cognitive behavioural strategies with social media and data sharing to motivate people to increase their activity levels. Consolvo et al. [START_REF] Consolvo | Design requirements for technologies that encourage physical activity[END_REF] found that groups of friends sharing their step counts over three weeks in order to achieve a group goal increased their awareness and gained more motivation to exercise. This may be due to the positive impression people want to maintain in their community [START_REF] Speck | Effects of a minimal intervention to increase physical activity in women: daily activity records[END_REF].

Inciting Emotions. HCI and Infovis researchers have found that emotions can engage people with data. Affective expression of representations is a factor of engagement [START_REF] Murray | Inventing the Medium: Principles of Interaction Design As a Cultural Practice[END_REF] and emotions can evoke affective experiences [START_REF] Feng | Evaluating affective features of 3d motionscapes[END_REF]. This is echoed by studies related to activity data. For example, Tong et al. [START_REF] Tong | Evaluating the effectiveness of three physical activity visualizations -how people perform vs perceive[END_REF] developed a game that connects the health of a virtual pet with the owner's physical activity. Participants in their study developed a connection with their virtual pet and felt the need to be more active. Similarly, many participants in the study of Fish'n'Steps exhibited emotional attachment to the virtual organisms visualizing their step counts [START_REF] Lin | Fish'n'steps: Encouraging physical activity with an interactive computer game[END_REF].

Norman argues that one's affection for beautiful things is crucial to engagement, interest, and pleasure [START_REF] Norman | Emotion & design: Attractive things work better[END_REF]. Plants are enjoyed for their aesthetics, trigger short-and long-term positive emotions regardless of a person's sex and age [START_REF] Haviland-Jones | An Environmental Approach to Positive Emotion: Flowers, Evolutionary Psychology[END_REF], and can improve health [START_REF] Langer | The effects of choice and enhanced personal responsibility for the aged: a field experiment in an institutional setting[END_REF].

Towards Living Visualizations

Since living plants affect people emotionally and can change people's behaviour [START_REF] Haviland-Jones | An Environmental Approach to Positive Emotion: Flowers, Evolutionary Psychology[END_REF][START_REF] Langer | The effects of choice and enhanced personal responsibility for the aged: a field experiment in an institutional setting[END_REF], researchers have used virtual plant metaphors to represent data. For example, Wang et al. [START_REF] Wang | Design and effects of personal visualizations[END_REF] used a flower metaphor for visualizing Facebook data, and found that virtual representations of living entities could trigger insights. While some studies showed that virtual metaphors can motivate people to change their behaviour [START_REF] Consolvo | Flowers or a robot army?: Encouraging awareness & activity with personal, mobile displays[END_REF][START_REF] Lin | Fish'n'steps: Encouraging physical activity with an interactive computer game[END_REF], others argue that their impact on a person's motivation and behaviour remains unclear [START_REF] Gasser | Persuasiveness of a mobile lifestyle coaching application using social facilitation[END_REF]. Overall, the HCI and Infovis communities have focused on virtual representations [START_REF] Consolvo | Design requirements for technologies that encourage physical activity[END_REF][START_REF] Consolvo | Flowers or a robot army?: Encouraging awareness & activity with personal, mobile displays[END_REF][START_REF] Gasser | Persuasiveness of a mobile lifestyle coaching application using social facilitation[END_REF]; however, there is a growing interest in using physical metaphors to represent data (e.g., [START_REF] Jansen | Opportunities and challenges for data physicalization[END_REF][START_REF] Vande Moere | Beyond the tyranny of the pixel: Exploring the physicality of information visualization[END_REF]). Using physical metaphors to represent personal data have great potential to motivate people to gain awareness and change their behaviour [START_REF] Khot | Understanding physical activity through 3d printed material artifacts[END_REF][START_REF] Lee | Patina engraver: Visualizing activity logs as patina in fashionable trackers[END_REF][START_REF] Stusak | Activity sculptures: Exploring the impact of physical visualizations on running activity[END_REF].

From these two trends, it appears that both virtual and physical plant metaphors can improve awareness and lead to behaviour change. Infotropism [START_REF] Holstius | Infotropism: Living and robotic plants as interactive displays[END_REF] presents both a living and a robotic plant responding to the use of either a recycling or a trash container: the plants receive light from one side according to which container waste is put in. The plants then become visualizations of the proportion of trash and recyclables that are collected. Results from a study suggest a clear difference between the two types of plants: the robotic plant lost the interest of people after two days, while the living plant sustained their interest-with participants still caring for it after the two weeks of the study. They also found that with the living plant, some people changed their behaviour because they started caring for the plant and felt obligated to keep it alive, instead of a desire to recycle more.

We build on Infotropism to explore how people's attachment to living visualizations can be an engaging motivator for behaviour change. In contrast to Infotropism, which explored how the tilt of a plant can reflect group behaviour, where a group is responsible for the plant, we explore how the health of a plant can reflect individual behaviour, where an individual is responsible for the plant.

DESIGN CRITERIA

Looking over the literature, the increasing availability of fitness tracking devices has led to considerable research that assess how technology can help people in increasing their awareness of, and in improving, their activity level. We combine the strategies that have been proven successful for engaging people with their personal data: personable visualization, social context, cognitive behavioural strategies, and inciting emotions. Since people are usually emotionally attached to plants, we use this medium as a way of triggering emotions and engaging individuals with their activity data. From the literature, we identified design criteria that can have a positive effect on individuals' engagement, awareness, and behaviour change: DC1 Abstract representations are more efficient than typical barchartlike visualizations for self-reflecting on personal data [START_REF] Consolvo | Theory-driven design strategies for technologies that support behavior change in everyday life[END_REF][START_REF] Consolvo | Activity sensing in the wild: A field trial of ubifit garden[END_REF][START_REF] Lin | Fish'n'steps: Encouraging physical activity with an interactive computer game[END_REF]. DC2 Aesthetic, playful, and pleasurable representations engage people with everyday data and trigger emotions [START_REF] Li | Understanding my data, myself: Supporting self-reflection with ubicomp technologies[END_REF][START_REF] Rodgers | Exploring ambient and artistic visualization for residential energy use feedback[END_REF][START_REF] Stusak | Activity sculptures: Exploring the impact of physical visualizations on running activity[END_REF]. DC3 Goals that affect the representation leverage successful cognitive behavioural strategies [START_REF] Berkovsky | Physical activity motivating games: Virtual rewards for real activity[END_REF][START_REF] Harrison | Tracking physical activity: Problems related to running longitudinal studies with commercial devices[END_REF][START_REF] Locke | Building a practically useful theory of goal setting and task motivation: A 35-year odyssey[END_REF]. DC4 Representations that can be shared leverage the benefits of the social context on engagement [START_REF] Consolvo | Design requirements for technologies that encourage physical activity[END_REF][START_REF] Speck | Effects of a minimal intervention to increase physical activity in women: daily activity records[END_REF]. DC5 Representations that trigger emotional commitment favor both short-term and long-term engagement [START_REF] Lin | Fish'n'steps: Encouraging physical activity with an interactive computer game[END_REF][START_REF] Murray | Inventing the Medium: Principles of Interaction Design As a Cultural Practice[END_REF][START_REF] Norman | Emotion & design: Attractive things work better[END_REF][START_REF] Tong | Evaluating the effectiveness of three physical activity visualizations -how people perform vs perceive[END_REF]. DC6 Non-virtual living representations leverage both physical metaphors and living plant metaphors, as well as the sustainable interest for living things [START_REF] Holstius | Infotropism: Living and robotic plants as interactive displays[END_REF][START_REF] Khot | Understanding physical activity through 3d printed material artifacts[END_REF][START_REF] Langer | The effects of choice and enhanced personal responsibility for the aged: a field experiment in an institutional setting[END_REF][START_REF] Lee | Patina engraver: Visualizing activity logs as patina in fashionable trackers[END_REF][START_REF] Stusak | Activity sculptures: Exploring the impact of physical visualizations on running activity[END_REF]. Every midnight, the server sends the earned water amount to the Raspberry Pi, which waters the Cyclamen plant accordingly. The owner can log on to an online dashboard to monitor their step and earned water levels, and to manually water the plant.

GO & GROW SYSTEM

The six design criteria informed our design of Go & Grow (see Figure 2), a system composed of a flowering plant, an irrigation system, and a step-count tracker. The amount of water given to a plant reflects the step count of the plant owner. Using a living plant to represent one's activity already satisfies four design criteria:

• DC1: The data is represented loosely (aspect, characteristics, health of the plant), not with precise numerical values. • DC2: A plant is an aesthetically pleasing thing.

• DC5: People gain affection for plants and emotionally engage with them; they are less likely to let plants die than bar charts. • DC6: A plant is a non-virtual living representation. System Overview. Assume Anna is a person using a Fitbit tracker. Anna syncs her tracker with her online Fitbit account using the Fitbit application whenever she wants. Every midnight, the Go & Grow server accesses Anna's daily step count through the Fitbit API, and converts this step count into the amount of water to give to the plant. The server then sends this water amount to the wifi-connected irrigation system, which automatically waters the plant accordingly. Anna can see her daily progress, step count, and watering history in the online dashboard. Anna can also use the dashboard to water her plant in real time, based on her current number of steps.

Go & Grow Server. Once Anna syncs her Fitbit tracker, the Go & Grow server accesses her step data via the Fitbit API and converts her current step count to a percentage of completion towards her daily step goal. As her percentage of completion increases, Anna earns water. The earned water is represented as a percentage of the optimal daily dosage of water that the plant needs.

Everyday at midnight, the server sends the watering amount to the irrigation system and resets the step counter. However, the plant can only be watered in 25% increments (25%, 50%, 75% and 100%) in order to motivate people to achieve goals (DC3). For example, if Anna walked 8,500 steps and her goal is 10,000, she will earn 85% of water, but her plant will only get 75% of it's optimal daily dosage. The difference, 10%, will be banked (available for later use). If Anna achieves higher than 100% of her goal then the plant will get 100% of its water and the extra water will be banked.

Online Dashboard. Anna's online dashboard contains a green progress bar showing her progress towards her daily step goal and a blue bar showing how much water she has in stock. In the dashboard shown in Figure 2, the Anna has achieved 70% of her daily goal, but she can only use 50% of the water. Once Anna has earned at least 25% of water, she can click on the "Water Now" button to water her plant immediately, otherwise it will happen automatically at midnight. We implemented this functionality because: i) it gives the plant owners the opportunity to water their plant before midnight, as the pump makes some noise; and ii) plant owners can water their plant in real-time and observe that the system works as expected.

Irrigation System. The irrigation system consists of a Raspberry Pi (Version 2 Model B) and an aquarium water pump (3.5 gph Tom Aquarium Aqua Lifter Pump). The Wi-Fi enabled Raspberry Pi receives the amount of water from the server, and converts this amount to the time in seconds that the water pump must be turned on. The electronic components are fastened in a plastic box (20 × 10 × 5 cm). All the parts were bought from online stores and cost less than US $90, which makes the system easy to build and reproduce.

Cyclamen Plant. Although this may not look to be the most complicated part of the system, there are many variables to consider for choosing a plant. The main considerations are: P1 Its aesthetics: the plant should be aesthetically pleasing. P2 Its sensitiveness to water: the amount of water the plant receives should quickly impact its health and appearance. P3 Its robustness to dryness: although the plant should be sensitive to water, it should be "forgiving" and be robust enough to not die immediately if not watered for a single day. P4 Its environment requirements: the plant should be able to grow indoors, sometimes with little natural sunlight. We considered many plants before choosing the Cyclamen. We considered tomato plants, which are sensitive to water and provide fruits, but are not particularly aesthetically pleasing, have a strong scent, and give fruits only when outdoor; mint, which grows fast, but triggers allergies; impatiens, which are aesthetically pleasing and sensitive to water, but inexplicably died during our tests. We finally chose Cyclamens, which satisfy P1 to P4; we also did not encounter any issue with them. Cyclamens are valued for their flowers with upswept petals and vibrant colors (P1). Properly watered, they grow flowers at a rapid rate of 1-2 flowers per week, but their flowers will dry up quickly if they lack water (P2). A quick reapplication of water will cause the plants to revitalize (P3). Persistent dry soil, however, will impede healthy growth and will cause the plants to die. Cyclamens thrive in cooler temperatures and do not require direct sunlight, making them ideal for indoor settings (P4).

To determine the exact daily dosage of water Cyclamens need to grow healthily without being under or over watered, we consistently gave three cyclamen plants different amounts of water everyday for four weeks. We recorded observations such as flower color, leaf color, soil moisture, and number of flowers of each plant, and deduced the optimal daily water dosage to be 35 mL.

GO & GROW QUALITATIVE STUDY

We ran a six-week study with ten participants using Go & Grow. We designed this study to explore living visualization for representing personal fitness data; and to examine people's reaction, motivation, and engagement with a personal living visualization.

Study Design

The study consisted of two phases, with each phase being followed by a recorded semi-structured interview.

Phase 1 consisted of a three-week period during which participants wore a Fitbit tracker. During this phase, we determined the average daily step count of each participant as a baseline that we later used to determine their daily goals. The first phase lasted three weeks to limit the novelty effect, because most people stop using their tracking device after three weeks [START_REF] Shih | Use and adoption challenges of wearable activity trackers[END_REF]. After the first phase, we conducted short (15-30 min) entry interviews and collected participants' demographics, levels of activity, previous experiences with PI systems, and thoughts on the study. We asked participants to name their plant, both for privacy purposes and to initiate the emotional connection. The second phase began right after the entry interviews.

Phase 2 consisted of a three-week period using Go & Grow. We linked the Fitbit accounts of participants to their Go & Grow system. We set the daily step count goal of participants to be their average daily step count during Phase 1 plus 10% -so that participants would have to walk a bit more than usual. We introduced them to the online dashboard, and told them to sync their Fitbit at least once a day. At 11pm every day, participants who had not synced their Fitbit yet would receive an email reminding them to sync. At the end of Phase 2, we conducted 20-40 min semi-structured exit interviews containing both questionnaire questions and open questions.

Conditions: The only design criteria which is not enforced by Go & Grow is DC4: sharing the representation. Thus, we considered two types of participants. Individuals have their Go & Grow at home and do not interact with other participants of the study. Group participants are friends placed in a competitive setting, with the plants of all group participants being side-by-side in a shared workplace.

Participants

We recruited ten volunteers by email and word of mouth with no monetary compensation; this was to recruit people who were solely interested in tracking their steps and linking their data to a plant.

We recruited five individuals I1-5 (three male) aged 21-63 (mean 31). There were two students, one engineer, one teacher, and one manager. We recruited five group participants G1-5 (two male) aged 20-30 (mean 24) through a mailing list. All were students working in the same place, knew each other well, and interacted on a daily basis. We lent a Fitbit Zip to participants who did not own a Fitbit.

Go & Grow Deployment

The individuals placed the plant at a location of their choice at home. Three set up their plant in their kitchen, one in their bedroom and one in their living room; all were near a window. For the group participants, we built a Go & Grow with one Raspberry Pi controlling five pumps, each connected to one plant. We set up this system in the semi-public lunch area, next to the main entrance of the workplace. Because plants were given names, participants did not know which plant belonged to whom. Since there were no windows nearby, we added a LED light fixture to illuminate the plants during day hours (see Figure 3).

RESULTS

We transcribed entry and exit interviews and complemented them with the numerical Likert scale answers. We analyzed the transcripts using open coding and affinity diagramming. We extracted 112 codes that we organized into 41 categories. We then grouped related categories into 8 emerging topics. We used Bertifier [START_REF] Perin | Revisiting bertin matrices: New interactions for crafting tabular visualizations[END_REF] to create Figure 4, presenting the summary of these results. The results from the entry interviews are summarized in the "Before Phase 2" section in Figure 4. We report the results from "After Phase 2" section in more detail below, according to the topics listed in Figure 4.

Goals. All seven participants who mentioned goals found it motivating to have a goal. This motivation was not always directly related to the plant, e.g., "I was more obsessed with reaching the 100% than actually watering the plant" [I3]. Five participants enjoyed goal banking, e.g., "it was nice to know that even if I was not getting all of my steps done today, my plant was still getting water" [G5]. However, I4 found it demotivating: "if I had steps carry over then I didn't really care to do that many that day" [I4].

Activity Change. All but G4 increased their average step count during Phase 2 compared to Phase 1. Five participants felt that they had improved their activity level during the past three weeks.

Behaviour Change. All but two participants mentioned taking unusual walks. These walks included: asking friends to go for a walk, e.g., "I would tell my friends that we have to go for a walk because I need steps" [G1]; taking a walk with their dog, e.g., "If knew that I wasn't going to hit it for a day then I would go take [dog] for a walk" [I1]; and extending walks, e.g., "if I was at the end of a walk and I see that I still haven't reached my step goal yet by a little bit then I would extend the walk" [I3]. I2 also changed their behaviour for steps banking instead of for reaching the goal that day: "I was going on vacation and I'm like oh well I should try get an extra 300% [of water] before this just in case I don't have access [to internet]" [I2]. Seven participants mentioned taking unusual walks late in the day to reach their goal, e.g., "when I wasn't at my goal . . . I would pace back and forth down the hallway" [I1].

Six found that their everyday habits had changed during the study. These changes include walking instead of being inactive, e.g., "when I wasn't doing anything then I would go 'well I might as well go for a walk and get some steps in" [I2]; changing meeting habits, e.g., "just for both our sakes, we do walking meetings now" [I5]; being more consistent, e.g., "I feel I've developed a habit now so that's awesome" [I3]; and changing the way of doing everyday things, e.g., "If I was very close to my goal at night, make sure it will get a whole bunch of extra steps, like bouncing around. Or when you brush your teeth, you walk back and forth" [G5].

Increased Awareness. Nine participants mentioned that they either became aware of their activity level, e.g., "it created an awareness of how sedentary I am" [I5], or became more aware than before, e.g., "It's not like I didn't know about those numbers before but now I feel . . . I should be on top of this a bit more." [G4]. Five participants said that they gained their activity awareness by looking at the plant; two by looking at the water; all by looking at the Fitbit multiple times a day and at the dashboard at least once a week.

Plant as a Visualization. Participants told us that the plant affected their behaviour by an average score of 3.3 (1: not at all; 5: a lot). Three group participants liked that the plant showed their data in a semi-private way, e.g., "I could compare in a way but not really. I like how it was hidden" [G4]. Four participants mentioned that the plant represented their data at a high level, e.g., "it was pretty responsive to being watered. . . . I think if you didn't see a big visual difference then it wouldn't have been as motivating" [I1]. Others were more neutral, e.g., "it wasn't a very precise display" [G5].

All but two mentioned the aesthetics and the living aspects of the plant as factors of motivation, e.g., "It was kinda interesting to observe these flowers grow and bloom" [I3]. They all stated that they would not have felt the same with a virtual plant. The fact that the plant can die was the most frequent comment, e.g., "a plant is a living organism. With a virtual plant, if it dies then you don't really care" [G3], and "I feel like with a virtual plant I could always sign up for a new account . . . but with a real flower, I could kill it" [G4].

Emotional Connection. All but one participant looked at their plant at least once a day. Eight felt pride, mentioning their plant to friends, e.g., "I told [friend] about it . . . people thought it was cool" [G2], colleagues, e.g., "I would start proudly talking about my plant" [G3], and family members, e.g., "I showed it to my family. I showed a picture to a couple of people" [I4]. Others had secret pride, e.g., "I have secret pride in my plant, it didn't die, that's really nice" [G5]. Six told us about their emotional connection with their plant, e.g., "I developed a connection with the plant" [I1]. Some mentioned caring about the plant as if it was a pet, e.g., "It was almost like . . . having a pet . . . you're taking care of it" [G3]. Two did not feel connected to the plant, e.g., "I just didn't connect to the plant" [I5].

All but two mentioned that they felt responsible to keep the plant alive and healthy, e.g., "I didn't want it to die! I felt responsible" [I1]. Some talked about the long-term impact of this responsibility, e.g., "I wasn't just thinking about it for the first week then forgetting about it. I was actively conscious . . . the entire time that my flower was dependent on my performance" [G3].

Five mentioned guilt, e.g., "If I didn't water it then I was feeling guilty" [I1], in several ways, including: because of their laziness, e.g., "I'd felt kind of mean if it didn't get watered just because I didn't feel like walking" [G5]; because they were not properly watering the plant, e.g., "I still felt guilty when I didn't have at least 100% water every night" [I3]; and when they received an automatic email, e.g., "Every time I got an email . . . I would feel guilty" [G2].

Social Context. Four group participants mentioned that the plants were a common point for discussion with colleagues, e.g., "It was actually quite entertaining to . . . have a common point to talk about. We stood around the plants and discussed" [G3]. They all talked about their plant with family and friends, e.g., "They [friends/family] still ask me about how my plant is doing every time they see me now, so it's a constant reminder to keep being active" [I3].

Six participants felt social pressure during the study. Other people seeing their plants motivated group participants, e.g., "I would have found it embarrassing if it weren't doing well, because people were paying attention to the plants" [G5]; as well as having their plant being compared to other plants, e.g., "I didn't want my plant to be the worst looking one" [G4]. Individuals mentioned positive social pressure coming from both family members and friends, e.g., "if it started dying then [my family] would say something about it" [I3].

Future Use. After three weeks of Phase 2, we offered the participants the opportunity to keep using the system. Eight decided to continue. The two who did not keep using it had reasons that interfered: I1 moved to a new city and could not take the plant with them and I4 lost the Fitbit tracker on the day of the exit interview.

DISCUSSION

Overall, participants increased their awareness of their activity level. During the three weeks of the study, participants also changed their habits towards a more active lifestyle. Based on our results, we discuss i) the impact of a living visualization, and ii) people's diversities as a factor when designing PI tools. We then introduce iii) the Goal Motivation Model, which is based on the degree of goal abstraction, captures different motivations, and supports diversity.

The Impact of a Living Visualization

Our results indicate that the emotional commitment to the living visualization is a key factor in motivating and engaging people towards improving their activity level. Participants connected emotionally to their plant -all mentioned that they did not want to let their plant die. Participants felt sadness, guilt, and pride towards their plant, and even compared the plant to a pet. The two participants who first told us that they did not feel connected to the plant later told us the opposite. I3 initially said "I personally feel very indifferent about the plant" but later expressed feelings of responsibility for the plant ("I feel some kind of self-obligation every night to make sure that I've gotten enough steps"); felt guilt ("I felt kinda guilty if I didn't have enough water by the end of the day"); and cared for the life of the plant ("I would not have cared at all if the flower was virtual"). I3 further said that the plant affected their behaviour: "I would be about to eat . . . something fattening then I would see the plant and that would somehow make me feel guilty."; concluding "I did care about [the plant] unconsciously".

This emotional commitment is probably due to the living visualizations being different from raw numbers, standard visualizations, and virtual metaphors, because they are alive. Although the plant is not the method by which people gain the most information about their activity level, it is the plant that the participants committed to the most. A living visualization gives meaning to an otherwise less meaningful goal of reaching an arbitrary numerical goal.

Considering the Facets of Diversity

People tracking or willing to track their activity data have diverse activity levels, goals, and motivation; they cannot be reduced to one single kind of stereotyped person. Our results highlight four types of diversity: 1) of activity levels, 2) of schedules, 3) of motivations and goals, and 4) of strategies for accomplishing goals.

1. Diversity of activity levels. People have an activity level ranging from nearly nothing to extremely high. However, standard tools do not consider this diversity. For example, Fitbit.com attributes the exact same goal of 10,000 daily steps to any new user.

Preset goals may be impossible to reach for someone with a low activity level. People with low activity levels in our study committed to the plant and did not care much about the actual numbers. These people still benefit from the social commitment since: i) the raw data is difficult to read because a living visualization is an abstract representation; and ii) both a professional athlete and a non-active person can compare their living visualizations because the goal is set to be relative to the activity of the person.

In contrast, people with high activity levels in our study tended to commit more to the numerical value than to the plant.

2. Diversity of schedules. When people have relatively consistent schedules, a daily goal is reasonable and favours consistency. Some of our participants had erratic schedules, unexpected events, deadlines, illnesses-a few of the many factors that can disturb daily activities. They may be discouraged quickly if their goal has to be reached every day. The living visualization and goal banking helped tackle this issue. First, the living visualization is a forgiving visualization. having less water for a couple of days altered the plant's appearance slightly, but did not kill it. This results in minor punishments for someone who had been less active for a day or two. Second, goal banking can be capitalized. Goal banking consisted of capitalizing extra amounts of water. Participants used the stored water on days when they could not reach their goal.

3. Diversity of motivations and goals. Motivators included having a healthy plant, earning water, and achieving a numerical goal. In fact, participants were motivated by several of these aspects at once and pursued them with varying degrees of commitment. Even those with a low activity level, whose motivation mostly came from the plant and the social context, sometimes gained motivation for achieving the numerical goal. Inversely, those with a high activity level, mostly motivated by the numerical goal, also gained motivation through the social context, the water level, and the plant.

4. Diversity of strategies for accomplishing goals. Our results revealed three strategies that participants employed to ensure that their plant would receive the maximum amount of water every day:

• Changing everyday habits is a long-term behaviour change, that occurred for participants who preferred to change everyday habits rather than having erratic behaviours, typically by taking the stairs instead of the elevator and walking to work; • Planning for inactivity is a mid-term behaviour change that occurred when participants anticipated a period of inactivity. They banked water by taking long walks the days before to ensure that their plant would get enough water everyday; • Reacting under pressure is a short-term behaviour change that occurred when participants realized that they would not reach their daily goal, typically by taking walks late at night. Summary. Standard ways of visualizing personal activity data tend to be generic approaches which do not consider these facets of diversity. Our results show that these facets of diversity are all factors of engagement. Based on this observation, we introduce the Goal Motivation Model, which supports and even encourages considering the facets of diversity when designing PI tools.

Goal Motivation Model

From our results we derive the Goal Motivation Model, illustrated in Figure 5, that supports and encourages considering the facets of diversity. This model, based on the degree of abstractness of visualizations, results in a tradeoff between an person's commitment to a goal and their awareness of their activity level. We describe the four levels of abstraction that surround a person in this model.

1. The precise activity tracker is the standard activity monitoring tool that provides a concrete numerical goal. Few people commit to this numerical goal, as evidenced by the quick drop off from activity trackers [START_REF] Shih | Use and adoption challenges of wearable activity trackers[END_REF]. On the other hand, this level is the most powerful way for people to increase their awareness by providing them with precise numbers. In our study, even participants who already used an activity tracker and an online dashboard before the study became more aware of their activity level. Our interpretation is that people can increase their awareness of their activity level through activity trackers if they have an external motivation to look at their device every day-the plant in our study. This was echoed by our participants, e.g., "Before, sometimes I wouldn't even check it at all for a couple of days, but during the study I would check every day to make sure that my plant would get watered" [G4]. In brief, this level provides the most precise information about the activity, but does not seem to engage people to commit to a goal.

2. The abstracted activity is the intermediate level between the precise activity tracker and the abstract living visualization. Water level does provide a slightly abstract visualization, similar to a bar chart. This level results in a substitute goal to the goal of reaching a number of steps. The key point is that the step count is not an end in itself, and neither is the earned amount of water. In this cascaded approach, the step count goal is a means by which people can collect water; the height of water goal is a means by which they can achieve their concrete goal-maintaining a living entity. Some people may focus on this activity measure instead of their step count, since the abstracted measure contributes more directly to their goal of keeping the plant alive. The abstracted activity shown by the water level also provides people with more awareness than the plant because the activity level is represented in a less abstract way. The substitute goal can also be used to bank abstracted activity.

3. The living visualization results in an emotional goal. People emotionally connect to the living visualization, and commit to the meaningful goal of keeping it healthy. They commit to achieving this goal because the visualization is a living entity whose life depends on their activity. They feel sad when the living entity looks unhealthy; proud when it looks healthy; and guilty when they are inactive. This variety of feelings does not occur when looking at numbers or bar charts. Although the living visualization results in a strong commitment towards the goal of keeping a living entity healthy, it does not provide people with much awareness of their activity level. This is due to the abstractness of the visualization, which in particular does not reflect short-term activity changes.

4. The social context surrounds the person with regards to the living visualization. This context can include other people tracking their fitness data in a competitive/comparative setting with friends, family, and colleagues. Social context creates positive pressure to maintain the living visualization's health, creating a social goal. This social goal relates to Fogg's [START_REF] Fogg | Persuasive Technology: Using Computers to Change What We Think and Do[END_REF] principles of competition, cooperation, and recognition, which bring intrinsic motivation. In this level, the social goal is meaningful: it consists of feeling proud of the health of the living visualization in front of other people. The entourage does not know the numerical value associated to the person's goal, and does not need to know how the living visualization is maintained. They just know that the health of the plant is somehow related to the level of activity of the person.

Goal Motivation Model and the facets of diversity. The model supports all facets of diversity discussed in section 7.2. It supports the diversity of activity levels, ranging from low to high, by using leveled goals with increasing abstractness. It supports the diversity of schedules, ranging from regular activity to erratic schedules, by 1) letting people commit to the forgivable living visualization; and by 2) providing them with a way to compensate inactive days with very active days. It supports the diversity of motivations and goals and the diversity of strategies for accomplishing goals, because it is flexible, which lets people decide the degree of abstractness they commit to and their strategy to reach their goal(s). This model relates to Fogg's [START_REF] Fogg | Persuasive Technology: Using Computers to Change What We Think and Do[END_REF] functional triad. Participants in our study were motivated by all three aspects of the functional triad (social aspect, tool and medium), however, with a commitment-awareness tradeoff illustrated in Figure 5 according to participants' diversity.

Limitations and Future Work

Our qualitative study revealed the existence of factors of engagement with personal activity data. The purpose of this study was to shed light on these factors and to deepen our understanding of what motivates people in tracking and self-reflecting on their personal activity data. Like many similar studies, the short duration of our study makes it difficult to claim that people changed their long-term behaviour. Instead, this work opens the space of using living visualizations for personal data. Our results will inform further research in this area, and we suggest different strategies to expand both the Go & Grow system and the Goal Motivation Model in the future.

Long-term response: While our study unveiled interesting results, a longer study would be able to explore the long-term effects of a living visualization on awareness and behaviour change. However, the fact that eight participants kept using the system after the end of the study is a positive indicator of enduring interest in the system.

Confounding factors: More studies are needed to fully understand the effect of individual factors on motivation and awareness.

Limits of goal banking: Goal banking allowed for forgiveness. However, having an excess of earned water discouraged some participants from being active. A limit could be placed on the amount of water stored, or water accumulation could be prevented by adjusting the daily goal based on recent performance. Gradually increasing their step goal would actively challenge those who have increased their activity level. Similarly, gradually decreasing the step goal of those who have been struggling could provide encouragement.

Beyond steps and water: Other controls could be applied to other factors that influence a plant's growth, e.g., varying amounts of light and adding fertilizer. Similarly, step count does not fully represent one's activity level. Other metrics such as heart rate, calories burnt and floors climbed, could be used to control different parameters that affect plant growth, e.g., lighting according to floors climbed, adding fertilizer according to calories burnt, etc.

Timeliness of change: The plant gave little short term feedback of activity level. Short term feedback could be increased by adding dye to the water, which travels up the plant and tints the petals. A plant that receives more water would be more vibrantly coloured. Making the living visualization less abstract in the Goal Motivation Model would affect the tradeoff between the timeliness of change of the plant and the abstractness of the visualization. Abstractness offers forgiveness and privacy-two important factors of engagement.

Ethical issues: Living visualizations are not constrained to plants. Some participants made a parallel between taking care of a plant and taking care of a pet, which raises some important ethical issues.

CONCLUSIONS

We explored living visualizations as a tool to engage individuals to gain awareness of their activity level and motivate them to be more active. We designed, implemented, and evaluated Go & Grow as a proof of concept of living visualizations. By linking individuals' step count to the amount of water that their plant receives, we revealed unexplored aspects of personal activity tracking. Notably, we found that individuals emotionally commit to the goal of keeping the living visualization alive, regardless of their activity level. Living visualizations differ from existing tools and virtual metaphors, because they trigger feelings such as pride, sadness and guilt.

By analyzing the results from our study, we distilled four goals with different levels of abstractness that individuals can commit to, based on which we built the Goal Motivation Model. This model considers the diversity of individuals, thus supports and encourages diverse strategies for accomplishing goals. Because it encompasses four levels of goal abstractness, it can be applied beyond activity tracking, paving the way for designing technologies that will help individuals commit to personal goals in their everyday life.

Figure 1 :

 1 Figure 1: The Go & Grow system linking individuals' activity data to the amount of water that their personal plant receives.

Figure 2 :

 2 Figure 2: Overview of Go & Grow. The plant owner wears a Fitibit tracker. Once they sync their tracker with Fitbit.com, the Go & Grow server will access their activity data, and compute the amount of water earned corresponding to the number of steps walked.Every midnight, the server sends the earned water amount to the Raspberry Pi, which waters the Cyclamen plant accordingly. The owner can log on to an online dashboard to monitor their step and earned water levels, and to manually water the plant.

Figure 3 :

 3 Figure 3: Go & Grow setup for the group participants.

  MAKES THE DATA HIDDEN/SEMI-PRIVATE REFLECTS WELL THE DATA AT A HIGH LEVEL MOTIVATES BECAUSE ALIVE / AESTHETIC PLANT AS A VISUALIZATION IS MORE ENGAGING THAN A VIRTUAL ONE LOOKED AT THE PLANT N TIMES EACH DAY

Figure 4 :

 4 Figure 4: Topics and categories are in rows. [G] / [I] indicate that the row concerns only group participants / individuals. Participants are in columns; G[1-5] are group participants, I[1-5] individuals, {G, I}average averages. Cross indicate no data.

Figure 5 :

 5 Figure 5: Goal Motivation Model. The four levels of goal abstraction-activity tracker, abstracted activity, living visualization, and social context-play a role in one' awareness and behaviour change and have a commitment-awareness tradeoff.

GO & GROW SERVER FITBIT.COM Fitbit Tracker Sync Sync Water amount

	Amount of Earned	Plant's Name "Water Now"	Basic Statistics
	Water Available	Button

Fitbit App Online Dashboard Cyclamen Flower Water Tank Raspberry Pi Water Pump Number of Steps Fitbit API

	Number of Steps
	Towards Daily Goal

Living Visualization Social Context Abstracted Activity Precise Activity Tracker 9768 Social Goal Emotional Goal Substitute Goal Numerical Goal Commitment Commitment Commitment Commitment Awareness Awareness Awareness Awareness

ACKNOWLEDGMENTS

This research was supported in part by NSERC, SMART Technologies, and AITF. We thank the participants to our study. We also thank Tamara Flemisch, Terrance Mok, and Tiffany Wun for proofreading the paper; as well as David Ledo for drawing our fictional character, Anna.


Sebastian Lay
email: sebastian.lay@ucalgary.ca

Jo Vermeulen
email: jo.vermeulen@ucalgary.ca

Charles Perin
email: charles.perin@ucalgary.ca

Eric Donovan
email: edonovan@ucalgary.ca

Raimund Dachselt
email: raimund.dachselt@tu-dresden.de

Sheelagh Carpendale
email: sheelagh@ucalgary.ca

Slicing the Aurora: An Immersive Proxemics-Aware Visualization

Keywords: Information visualization, proxemic interaction, aurora borealis, interactive surfaces, large displays. ACM Classification H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous

The Aurora Borealis or Northern Lights is a phenomenon that has fascinated people throughout history. The AuroraMAX outreach initiative provides a collection of time-lapse videos of the night sky captured by a camera at Yellowknife in Canada. We present an interactive visualization of this AuroraMAX image data on a large touch display. Our visualization slices each time-lapse video to represent an entire night as a single image or keogram, provides different views on the keograms, and allows people to explore and compare nights to discover interesting patterns. To entice people to interact, we use proxemic interaction and animate the visualization in response to people's movements in front of the display. We deployed the visualization in a public space at an art-science festival. Initial findings suggest that the proxemic interaction aspect helps to draw people in and that the visualization generates interest from passersby, providing opportunities for science outreach.

Introduction

The Aurora Borealis or Northern Lights is a phenomenon that has bewildered people throughout history and continues to fascinate artists, scientists, and photographers. Physicists at the University of Calgary deploy automated cameras and other measuring instruments in northern Canada to study the Aurora Borealis. The cameras used for taking these images are usually equipped with a wide-angle lens to capture the entire night sky. This data and images are made available on the Internet for other scientists to explore [19]. Additionally, the AuroraMAX outreach initiative [START_REF]AuroraMAX project website[END_REF] makes time-lapse videos of aurora activity available to the general public. These videos are created from images of the night sky captured by a high-resolution SLR camera in Yellowknife. Even though these videos are an excellent source for teaching and science outreach, there is no easy way for the general public to explore and engage with this data in more detail. Exploring and comparing different nights requires looking at the time-lapse videos of each of those nights one by one. Sifting through large quantities of video data to find a specific event or interesting occurrence is known to be a tedious and time-consuming task [START_REF] Tang | Exploring video streams using slit-tear visualizations[END_REF].

To provide the general public with an engaging way to explore this high-resolution image data, we designed an interactive visualization for a large touch display. We designed the visualization for large displays because of its benefits for deployment in public spaces such as libraries or museums [START_REF] Hinrichs | EMDialog: Bringing Information Visualization into the Museum[END_REF], and to support multiple people interacting with it at the same time. Large displays are proliferating in (semi-)public spaces [START_REF] Hinrichs | Interactive Public Displays[END_REF][START_REF] Hinrichs | Large Displays in Urban Life -from Exhibition Halls to Media Facades[END_REF][START_REF] Ojala | Multipurpose Interactive Public Displays in the Wild: Three Years Later[END_REF], and have been explored for "educational, entertaining, participative, and evocative experiences" [START_REF] Hinrichs | Interactive Public Displays[END_REF]. This makes these displays an ideal platform for creating engaging information visualization experiences for the general public.

Once one or more people are interacting with a large display, they tend to attract more bystanders, a phenomenon known as the honeypot effect [START_REF] Brignull | Enticing People to Interact with Large Public Displays in Public Spaces[END_REF][START_REF] Müller | Looking Glass: A Field Study on Noticing Interactivity of a Shop Window[END_REF]. However, it can be difficult to get people to start interacting with a display [START_REF] Grace | Conveying Interactivity at an Interactive Public Information Display[END_REF][START_REF] Jurmu | Waving to a Touch Interface: Descriptive Field Study of a Multipurpose Multimodal Public Display[END_REF][START_REF] Müller | Requirements and Design Space for Interactive Public Displays[END_REF], or even to notice that the display is interactive (interaction blindness) [START_REF] Ojala | Multipurpose Interactive Public Displays in the Wild: Three Years Later[END_REF]. To draw attention and entice people to interact, we integrated aspects of proxemic interaction [START_REF] Ballendat | Proxemic Interaction: Designing for a Proximity and Orientation-aware Environment[END_REF] into our visualization. The elements of the visualization move and animate in response to people's movements in front of the display, which can help to make passersby notice that the display is interactive [START_REF] Müller | Looking Glass: A Field Study on Noticing Interactivity of a Shop Window[END_REF].

We deployed the visualization in the University of Calgary's Taylor Family Digital Library, during the local Beakerhead art-science festival. We observed that the proxemic interaction aspect did help people to notice the display while walking by and invited them to interact. The visualization generated lots of interest from passersby, which often interacted in groups.

In what follows, we explain the design of our visualization including the use of proxemic interaction, discuss initial findings from our deployment in a public space, and provide directions for future work.

Keograms: Slicing through the Videos

To allow people to more easily explore the archive of time-lapse videos from the AuroraMAX initiative [START_REF]AuroraMAX project website[END_REF], we designed a visualization that slices through the time-lapse videos. This technique has been used previously for exploring video streams [START_REF] Tang | Exploring video streams using slit-tear visualizations[END_REF], as well as for visualizing temporal data using space-cutting [START_REF] Bach | A Review of Temporal Data Visualizations Based on Space-Time Cube Operations[END_REF].

The AuroraMAX videos tend to be about 3 to 4 minutes in length, depending on the duration of the night. We condense the video of each night into a single image or keogram (Figure 1). The word keogram is derived from 'keoeeit' [START_REF] Mende | Photometric Investigation of Precipitating Particle Dynamics[END_REF], the Inuit word for the Aurora Borealis. keograms can be seen as a horizontal timeline that starts with the evening on the left and ends with the next morning on the right, as shown in Figure 1. Like the time-lapse videos, the keograms differ in length depending on the duration of the night.

Keograms support an at-a-glance overview of the contents of the time-lapse video. They transform a large series of images (i.e., the different frames of the time-lapse video) into a single image. Figure 2 shows several examples of keograms, and how they can be used to identify both occurrences of the aurora and other features such as clouds during the course of the night. Eather et al. [START_REF] Eather | Plasma Injection at Synchronous Orbit and Spatial and Temporal Auroral Morphology[END_REF] first described and used keograms in the seventies for studying the aurora. While they used scanning photometer data to describe the latitude-time morphology of auroras, the images we used in this application were captured using an off-theshelf DSLR camera that captures most visible light.

Visualization Design

The visualization provides an overview of about five months of image data. It also supports smooth transitions between the overview of all nights and a video view to explore the time-lapse video of a particular night. Both the overview and the video view are based on keograms.

Overview

In the overview, keograms for all nights are rotated at 90 degrees to create vertical bars whose height encodes the duration of the night, as shown in Figure 3 (top). By default, the horizontal axis encodes time, such that the rightmost keogram represents the latest night. In addition, small icons below the keograms show the moon phases. A multi-focal detail-in-context view [START_REF] Mark D Apperley | A bifocal display technique for data presentation[END_REF][START_REF] Carpendale | A Framework for Unifying Presentation Space[END_REF][START_REF] Furnas | Generalized Fisheye Views[END_REF] influences the width of these columns. Figure 3 (top) shows one active lens in the middle. The focal detail can be positioned on the display using touch. The focus follows the position of a person's finger while sliding through the columns. Multiple foci can exist when multiple people are sliding through the columns at the same time.

The keograms can be sorted using the buttons in the top left corner of the screen. The columns can be sorted based on various attributes, such as the date or length of the night, and the brightness of the image.

Tapping on a certain keogram selects it and triggers the transition from the overview of all keograms to a view showing the full video of the selected night, as shown in Figure 3 (bottom).

Video View

When a keogram is selected, the space opens up to show a full video of that particular night (see Figure 3 bottom). The video view shows the time-lapse video of the AuroraMAX project for the selected night in the middle of the screen. A larger version of the corresponding keogram is shown below the video and functions both as an indicator and a control for the playback position of the video. This allows people to use the keogram to identify and jump to interesting parts of the video.

Proxemic Interaction

The foci in the overview can be placed using either touch or proxemic interaction [START_REF] Ballendat | Proxemic Interaction: Designing for a Proximity and Orientation-aware Environment[END_REF], supporting multiple people simultaneously. We use proxemic interaction to provide a more immersive experience. People's body movements are translated to motion on the screen to allow them to physically interact with the data. Each person's body in front of the display acts as a cursor that can be used to browse through the data. People walking by the display cause a ripple effect on the keograms when they move across the display. The goal of this effect is to attract the interest of people walking by and to help them notice that the display is interactive as it responds to their presence and movement. The proxemic interaction features are only active in the overview, and they turn off when people are close to the display to avoid bystanders disturbing people who are interacting using touch.

Note that we do not use silhouettes or skeletons to represent people on the display. While these provide stronger cues for people to interact with a display and recognize that it is reacting to their movements [START_REF] Ackad | Skeletons and Silhouettes: Comparing User Representations at a Gesture-based Large Display[END_REF][START_REF] Grace | Conveying Interactivity at an Interactive Public Information Display[END_REF][START_REF] Müller | Looking Glass: A Field Study on Noticing Interactivity of a Shop Window[END_REF], they take up space, disturb the visualization content, and affect the overall experience. Nevertheless, exploring how these representations can be better integrated into visualizations is an interesting direction for future work.

Implementation

We downloaded the AuroraMAX image data from the data page of the Space Physics Groups of the University of Calgary [19]. For every night, there are thousands of images depending on the length of the given night (approximately 1 GB / night). We use a Python script to read a vertical line of pixels in the middle of each image and to combine them into one output image, which are then loaded into the application.

The application is written using web technologies such as HTML, CSS and JavaScript. To support proxemic interaction, we use a Kinect v2 sensor and a small C# program that interacts with the Microsoft Kinect SDK. This program provides tracking data in JSON and communicates with the rest of the application via web sockets. This setup provides flexibility to deploy the application offline on a large display with a Kinect sensor. It also makes it possible to run the application on a website that anybody can use on their devices, regardless of whether they are touch-enabled.

Deployment and Initial Findings

We exhibited the application on a large touch display with Kinect sensor in the entrance hall of the Taylor Family Digital Library at the University of Calgary for a day, as shown in Figure 4. This provides us with initial insights into how people react to the visualization, and how they interacted with it. The overall appeal of the installation was quite high. We had around 40 groups, consisting of one to five people, interacting with the display. We also observed that the proxemic interaction features helped to get people to notice the display while walking by and invite them to interact with it.

Conclusion

We designed a visualization for Aurora Borealis image data on a large touch display to provide an immersive experience for the general public. Our visualization uses proxemic interaction to entice people to interact with and engage with the display. Findings from an initial deployment suggest that this holds promise for drawing people in, and can result in engaging experiences, providing opportunities for science outreach.

Figure 2 .

 2 Figure 2. A set of keograms from our visualization. Reading the images from left to right provides a timeline of aurora activity in the video and other interesting events such as clouds or the rising sun.

Figure 1 .

 1 Figure 1. Keograms slice through each frame of the time-lapse videos to condense the video into a single image.

Figure 3 .

 3 Figure 3. Top: The overview of all keograms, with the night of January 24 th as the focal region. In this view, the keograms are sorted by duration of the night, and icons below the columns indicate the moon phases. Bottom: The video view, where the keogram on the bottom can be used to scrub through the time-lapse video of that night to discover interesting events. AuroraMAX images © Canadian Space Agency.

Figure 4 .

 4 Figure 4. People using the application in the video view during its deployment in the library. Five people are interacting, while a passerby glances at the display.

Acknowledgements

This research was supported in part by NSERC, SMART Technologies and AITF.