# AROM: Rambling Along Data in Augmented Reality to Explore Large Order of Magnitude Values

Aymeric Ferron, Yvonne Jansen, Martin Hachet

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Aymeric Ferron, Yvonne Jansen, Martin Hachet. AROM: Rambling Along Data in Augmented Reality to Explore Large Order of Magnitude Values. VIS 2025 - Workshop on Visualization for Communication at IEEE Visualization Conference, Nov 2025, Vienne, Austria. hal-05224602

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# AROM: Rambling Along Data in Augmented Reality to Explore Large Order of Magnitude Values

Aymeric Ferron ibid, Yvonne Jansen ibid, and Martin Hachet ibid

<figure><img src="image_2.png" /><figcaption>Fig. 1: Three proofs of concept illustrating examples of AROMs: (A) is a 3D vertical bar chart displayed across several floors of an atrium, (B) is a 3D horizontal bar chart displayed across several meters on a parking lot and (C) is a 3D scatter-plot displayed across the width, depth and height of a parking lot.</figcaption></figure>

**Abstract**—Understanding datasets containing values spreading over several orders of magnitude can be difficult for non-experts. In this position paper, we introduce the concept of AROM: augmented-reality visualizations scaled across several meters, inviting people to walk around the physical space to get a sense of differences in order of magnitude of the visualized data. After presenting three different examples of AROMs, we discuss challenges and future research opportunities.

**Index Terms**—Data Visualization, Augmented Reality, Data Visceralization, Immersive Analytics

## 1 INTRODUCTION

A healthy democracy is a democracy where people are well informed and where citizens can make their own opinions based on facts, a concept coined as “enlightened understanding” by the political theorist Robert Dahl [13]. At the time of information fatigue [39] and misinformation [1], it is more important than ever to make objective information available and understandable to the general public.

Over the thousands of topics addressed weekly by media, some of them involve numbers and quantities with large difference in magnitude. According to Höhn et al., a dataset contains orders of magnitude values (OMVs) when at least two of its numerical attributes contains values that span four orders of magnitude or more [20]. In particular, topics such as wealth inequalities, greenhouse gas emissions or war casualties use dataset involving both small and large numbers which might be beyond the general public understanding. It has been shown that people tend to differentiate more precisely small numbers than large ones [24]. It is then important to support citizens understanding of such important data. Scholars in the visualization field developed a variety of techniques to visualize such data. Some are static, including logarithmic scales or alternative representation based on mantissa and exponential [6]. Some are dynamic, including techniques as *panning* or *zooming in and out* [42]. Consequently, there is currently no default method to use when designers want to communicate visualizations

containing OMVs to the general public.

Using augmented reality (AR) application could be a promising solution to help people engage with datasets containing OMVs and get a better understanding of them [16]. First, citizens could engage in a fun and original experience while exploring datasets with AR instead of using more casual displays like desktop or printed media. Second, AR comes with great opportunities by inserting virtual elements representing data in the physical world, making easy to represent huge data spreading over several meters along width, depth and height of a space. Displaying such data next to familiar objects or building could help the general public get a better understanding of the data. Third, AR might bring some cognitive advantages regarding memory and understanding.

In this position paper, we introduce the concept of *AROM*, augmented reality visualizations inviting people to ramble around datasets containing data spreading over several orders of magnitude. We introduce three AROM proofs of concept and we further discuss advantages, limits and research opportunities.

## 2 BACKGROUND & RELATED WORK

### 2.1 Representing Datasets with Order of Magnitude Values

People are not good at understanding large numbers: the higher the numbers, the more imprecisely people differentiate them [24, 33]. For example, people will perceive $100 billion and $2 trillion further apart than $1,200 and $24,000, even though both ratios are 20:1 [8]. It is important for people to get to understand large numbers and datasets containing large value range because that information covers various domains such as government expenditures, greenhouse gas emissions, world population or wealth inequalities. Such datasets are said to contain orders of magnitude values (OMVs) [20].

Representing a data set with several OMVs on a visualization is difficult, especially when the visualization is static. The naive way to represent such datasets is to use a linear bar chart, which tends to flatten the bars representing the lowest values, hindering legibility. A more common way is the use of logarithmic bar charts, but these types of visualization is difficult to understand for non-experts [29, 37]. Recently, scholars in the visualization field investigated representations

* Aymeric Ferron is with Inria, Univ. Bordeaux and LaBRI. E-mail: aymeric.ferron@inria.fr.

* Yvonne Jansen is with CNRS, Inria, Univ. Bordeaux and LaBRI. E-mail: yvonne.jansen@cnrs.fr.

* Martin Hachet is with Inria, CNRS and LaBRI. E-mail: martin.hachet@inria.fr.

Manuscript received xx xxx. 201x; accepted xx xxx. 201x. Date of Publication xx xxx. 201x; date of current version xx xxx. 201x. For information on obtaining reprints of this article, please send e-mail to: reprints@ieee.org. Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx
based on the mantissa and the exponent of the OMVs to improve non-experts numeracy, but those techniques were not tested among the general public [6]. Other solutions include non-static visualizations allowing interaction and exploration, like *panning* or *zooming in and out* techniques [42].

## 2.2 Representing Datasets in Extended Reality

Building on the data visualization field, Immersive Analytics (IA) was introduced as a new initiative mixing data visualization, extended reality (XR) and human-computer interactions (HCI). According to Dwyer et al., IA is “the use of engaging, embodied analysis tools to support data understanding and decision making” [16]. Marriott et al. explain that using immersive visualization could be beneficial for several applications, especially using depth to show an additional abstract dimension [28]. Indeed, IA allows designers to take advantage of immersive technologies to manipulate height, width and depth in order to convey more information than on a 2D display.

Moreover, some examples of IA prototypes try to take advantage of immersion and virtual locomotion to raise engagement in proposing fun experience [28]. An example of such experience is *Ride Your Data*, an immersive visualization where people can ride a roller-coaster following tracks matching the slope of a line graph [10]. This example illustrates the concept of *data visceralization* introduced by Lee et al. defined as “*a data-driven experience which evokes visceral feelings within a user to facilitate intuitive understanding of physical measurements and quantities*” [25]. Data visceralization experiences include virtual reality (VR) experience where users can walk around the Eiffel Tower to get a better sense of its height, for example.

In this work, we are focusing on the potential of AR visualizations, requiring the user to physically move inside and around the visualizations, changing from point of view during data exploration.

## 2.3 Cognitive Advantages of Immersive Visualizations

The effects of XR experience have been investigated on different cognitive dimensions, like memory, understanding and learning.

Some studies in the literature suggest that people perform better on memory-centred task when they interact actively with objects [22] or when they move actively in the virtual space [18]. In fact, it exists several types of memories, but we focus here on long-term memory, in which informative knowledge storage is relatively permanent [4], and, more precisely, in declarative memory, encompassing episodic and semantic memory. *Episodic memory* is the memory “*which receives and stores information about temporally dated episodes or events, and temporal-spatial relations among these events*” [45]. Typical experiments about episodic memory include remembering objects when walking around, or remembering a route. The impact of different features of VR has been tested on episodic memory, especially different levels of immersion (visual fidelity, multi-modal sensory information), presence, interaction and locomotion [41]. *Semantic memory* “*is the mental [...] organized knowledge a person possesses about words [...]*, their meaning and referents [and] about rules, formulas and algorithms for the manipulation of these symbols, concepts and relations” [45]. Typical experiments about semantic memory include learning list of words or knowledge. For example, Sommerauer and Mueller found that students improved their short-term knowledge acquisition and retention when exposed to non-interactive and mobile AR compared to classic museal elements [43]. However, they did not find any difference regarding long-term retention.

Displaying visualizations in the real world through AR visualizations could trigger cognitive mechanisms connected to memorability and understanding. The *context-dependent memory* (CDM) theory suggests that the learning and the recall contexts have an impact on the accuracy of information recall in the semantic memory. The seminal research of Godden and Baddeley in 1975 showed that people are better at recalling information when they are placed in the same context as the one in which they learned [17]. Those results have been replicated in other contexts, especially in VR environment [40]. In these experiments, researchers focus on words learning and recalling. In parallel, some work in information visualization focused on memorability of data

visualization. Studies showed that adding contextual information linked to the topic of charts improves long-term memorability [5, 7].

Having the general public understanding and better remembering datasets containing OMVs could help them when taking decisions in their daily lives. If such immersive experience could strengthen their knowledge about various topics, it might change their attitude toward societal issues, like climate change or wealth inequalities, and increase their likelihood of taking action [2].

# 3 PAPER RATIONALE AND DESIGN

This position paper investigates the opportunities and limits of using data visualizations in AR to enhance understanding and memorability of non-experts about datasets containing OMVs. We propose to scale visualizations such that large values span over several meters and use physical locomotion to explore the data in a visceral way. We present three proofs of concept illustrating the concept and we open the discussion about related challenges.

## 3.1 Definition & Characteristics

*An Augmented Ramble along large Orders of Magnitude (AROM)* is an augmented reality visualization inviting people to ramble horizontally or vertically across several meters in the physical space to fully explore a dataset containing order of magnitude values encoded with virtual elements.

The goal of an AROM is to spread the mapping of the values represented in order to (1) facilitate the comparison between small values which might be flattened on other representations, (2) illustrate the large difference in magnitude between different values in a visceral way and (3) take advantage of the physical world feature to support data exploration and cognitive benefits. AROMs can display different kinds of virtual elements, like charts (bar charts, line charts, scatter-plots...) or representation (3D surfaces, maps, concrete visualizations...).

## 3.2 Proofs of Concept

### 3.2.1 Design Rationale

Since understanding datasets containing values spanning over several orders of magnitude is difficult (see subsection 2.1), we propose to use physical locomotion and visualizations spanning over several meters to represent those data sets in a more legible and visceral way.

Using AR has two main benefits. The first one is to take advantage of the huge space available to represent data with large differences in magnitude. This advantage is not specific to AR: it is common to the entire XR continuum [30]. Indeed, in VR, designers can invest a digital world theoretically infinite in terms of height, width and depth. People can virtually move around the digital creations with the locomotion features created by the designers. In AR, digital representations are displayed in the real world, which is not infinite. However, AROM designers can take advantage of large physical spaces, inviting people to move around in their local physical environment, or to move through different floors or staircases, in order to explore the visualization.

The second advantage is specific to AR. It consists in taking advantage of physical objects situated next to the virtual representations of the datasets. The designers should map the maximum value of the chart to the furthest point in the real environment they want people to explore (in terms of height, width or length). Then, during data exploration, people will notice physical elements around them, allowing them to compare the position of the real object with the data represented. This could create a better understanding of data size, improve memorability and play the role of a cue triggering involuntary memory, remembering the dataset to people even after the end of the AR experience [36].

We developed three prototypes with Unity 3D (version 2022.3.54f1) using the Oculus API for the Meta Quest 3. It illustrates three different AROMs: (1) a vertical bar chart, (2) a horizontal bar chart and (3) a scatter-plot spread across height, width and depth. We are describing briefly the three proofs of concept below.

### 3.2.2 ClimbingChart

ClimbingChart is an AROM encoding values through the height of a linear 3D bar chart. It takes advantage of the ceiling height of a research
laboratory's atrium to expand from the floor to the ceiling. A video of the proof of concept is available online.

The dataset displayed in *ClimbingChart* is about greenhouse gas emissions of several items: 100g of different foods (lamb, beef, salmon, pork, chicken, lentils, plant-based patty), one hour of streaming, a pair of jeans, trips with a car (50km and 5km) and an example of daily carbon budget.

The biggest value is represented by the biggest bar normalized to reach the ceiling of the atrium. The other bars are rescaled proportionally. The smallest values are legible; they are not flattened like it would have been the case on a paper frame or on a static visualization on a desktop computer. Each bar has a label with the name and the numerical value of the data mapped (see Figure 2 (A)).

When displayed, people are able to begin their experience on the ground floor, near the lowest values. Then, they are invited to climb the stairs, exploring the height of the data as they do a physical effort to reach the top of the chart. When located on the ground floor, people can notice the small height of the smallest bar as well as look up to the biggest values which are overhanging them (Figure 2 (B)). When located at the top floor, people can have a sense of the height of the bar charts by noticing the top of the bar and look down to see the small bars far away below them (Figure 2 (C)). People can move freely in the space, getting closer to the bars, going up or down the stairs as their own rhythm.

<figure><img src="image_3.png" /><figcaption>Fig. 2: Different views of *Climbing Chart*, a 3D vertical bar chart: (A) shows the origin of the bar chart, (B) shows a perspective of the biggest bar from the origin of the bar chart and (C) shows a perspective of the biggest bar from the top of the bar chart.</figcaption></figure>

### 3.2.3 WalkingChart

WalkingChart is an AROM using the available width and depth of a parking lot to display the same dataset about greenhouse gas emissions as in *ClimbingChart* (see subsection 3.2.2). A video of the proof of concept is available online. The 3D bar chart displayed in *WalkingChart* is identical as the one in *ClimbingChart*, except it has been oriented at 90°.

During exploration, people can walk around the bars on all the parking lot's surface. When located at the origin of the bars, people can see how far the biggest values extend while notice how little are the bars corresponding to the smallest values of the dataset (Figure 3 (B)). Then, people can walk along the bar chart, experiencing the length of the data while moving horizontally to the extreme values.

<figure><img src="image_4.png" /><figcaption>Fig. 3: Different views of *Walking Chart*, a 3D horizontal bar chart: (A) shows the origin of the bar chart, (B) shows a perspective of the biggest bar from the origin of the bar chart and (C) shows a perspective of the biggest bar from its top.</figcaption></figure>

### 3.2.4 WalkingScatterPlot

WalkingScatterPlot is an AROM using the available width, height and depth of an outside parking lot to display a 3D scatter-plot. A

video of the proof of concept is available online. This visualization displays several data points about the environmental impact of food: water consumption is encoded with height, greenhouse gas emissions with depth and land use with width. Each point is represented as a sphere with several labels (name of the food and the three numerical values for the three datasets). The three axes are represented as 3D lines and the origin of the scatter-plot is located at their junction. In this visualization, the lower the value, the closer the point from the origin of the three axes. During exploration, people can walk around the scatter-plot to explore and locate the different points of the dataset. As a user, the first author had a sense of how the different types of food can form clusters according to their different environmental impacts (e.g. cluster of vegetables, cluster of pork-based food...).

<figure><img src="image_5.png" /><figcaption>Fig. 4: Different views of *Walking Scatter-plot*: (A) shows the origin of the scatter-plot and (B) shows a perspective of the scatter-plot from a position encoding higher data points.</figcaption></figure>

## 4 DISCUSSION & FUTURE WORK

This position paper introduces the concept of *AROM* and describe three proofs of concept. Now, we will discuss opportunities and limits of using data visualizations in AR to enhance understanding and memorability of non-experts about datasets containing OMVs.

### 4.1 Visualization Challenges

#### 4.1.1 Rambling Around Data

Since AROMs use AR, the location of the experience is important because it could showcase the data in different ways. In *ClimbingChart*, we decided to explore the use of the height dimension of the space. In that case, the chosen location must contain a means to explore it.

In a building, a staircase is well suited: people can walk up or down the stairs at their own pace, exploring the bar chart like one could do when visiting an exposition in a museum. An elevator could be an alternate solution for people to get from the lower point to the higher point of the bar chart. However, using an elevator decrease the agency of people experiencing it (usually, an elevator can't be interrupted in the middle of its path) and the physical effort of climbing stairs is also removed since taking an elevator is effortless.

Outside, different topologies could fit for a good experience. Stairs in an urban environment could allow people to climb and see the top of the visualization. In a natural environment, we could imagine displaying the bar chart near a cliff, a tree or a hill, offering the possibility to climb in order to reach the top of the visualization. For more extreme data, we could even imagine reaching extreme height, by using an air-balloon or taking an elevator to the top of a skyscraper.

When using the width and the depth of the space available to display horizontal charts, spanning over several meters, like in *WalkingChart*, we could allow people to bike along the visualization or even be transported in a car to explore all the dataset.

#### 4.1.2 Landmarks

It is also possible to take advantage of the specific features of a location. Each location contains objects and architectural or natural features. When displaying a bar chart, the different bars will reach different heights which could match the height of several physical elements around the visualization. We call these elements *physical landmarks*. For example, if a bar chart is displayed in a staircase with several floors,
a bar could reach the floor of the first floor when another could reach the ceiling of the second floor. People are then able to get a sense of the values at stake by projecting the values represented in the location through the physical landmarks.

It is possible to take advantage of the physical landmarks present in a location, but designers can also add temporary physical landmarks for the sake of the experience. For example, designers can add some little elements, like erasers or note books near the lowest values, and tanks or ladders near the biggest one. These physical landmarks could help get a sense of the size of the data.

Since we are using AR technologies, it is also possible to include *virtual landmarks*, purely digital objects that could play the same role as the physical ones. It might be relevant especially in locations where there are few physical landmarks, or when the designers would like to use landmarks difficult to move, like tanks, for example.

## 4.2 Hypotheses on Cognitive Effects

We have several hypotheses regarding the potential cognitive benefits from people exploring datasets with AROMs. First, having people moving physically to explore the visualization might get them a better understanding of the dataset. For example, in a dataset about greenhouse gas emissions of food consumption, people might know which item pollutes more than others (e.g. *"I know beef pollutes more than pork."*) but they might ignore *how much more*. If people have to walk a distance of three times longer for the beef than for the pork, they would then spend different amounts of time and effort to reach each data point. This could lead to a better understanding of the difference in order of magnitude between values, increasing their overall understanding about a dataset. As discussed in *subsubsection 4.1.1*, designers could use several types of locations and use different types of locomotion mode for people. Let's focus briefly on the cognitive perspectives of locomotion types. There are two cognitive dimensions of locomotion in XR: the *volitional dimension*, "which allow a subject to choose how to interact with the environment" and the *motoric one*, "in which subjects physically carry out the locomotion interaction via the XR system's input device(s)" [41]. Within a high volitional context, people decide where they want to go, while in a low volitional context, this decision is taken by an external agent. Within a high motoric context, people perform a physical action to move in the XR environment, while in a low motoric context, people remain passive. These dimensions were tested in a *between-subject study* where people were in a car in a VR environment, with objects appearing by the roadside [35]. Results suggest that the higher the volitional or the motoric dimension is, the higher the spatial memory. Thus, it would be interesting to investigate the impact on memorability, engagement and understanding of actively moving (by foot or by bike) compared to being moved in the visualization (by car or by elevator).

Second, designing AROMs based on the layout of a physical space could help people better understand datasets. Based on landmarks (see *subsubsection 4.1.2*), people could make comparisons of the data with familiar objects. Imagining a vertical bar chart such as the one in *subsubsection 3.2.2*: some bars could reach the height of a table, of a plant, or could even reach the first or the second floor of a building. These physical elements could help people compare the order of magnitude of the data and could help them to better understand them.

Finally, displaying data in augmented reality using physical elements as means of comparison could help people better remember the data. Based on the CDM theory, experiments studied how people could benefit from situated visualizations in a learning context [38]. In the CDM theory, when environmental features enhance the recall of information, these features are called **contextual memory cues**. In situated visualization, contextual memory cues could be various, including the physical referent of the visualization, or even the entire environment where the experience takes place [38]. Here, the benefits of the CDM are illustrated in the case of voluntary memories, i.e. *"memories based on strategic retrieval processes, reflecting a goal-directed and controllable search"* [36]. However, involuntary autobiographical memories, i.e. *"memories of personally experienced events that come to mind with no immediately preceding attempts at retrieval"*, are at least frequent as

their voluntary counterparts [36]. It means that having people experiencing visceral AR visualization in an environment they know and use often may trigger involuntary memories a long time after the experience took place, enhancing memorability and learning. Petiot et al. showed that AR objects elicit twice more involuntary memories compared to object displayed on a blank page during an immediate re-exposure phase to contextual cues, but no measurable effect was found on the long term [34]. Further studies should investigate if AROMs could trigger involuntary memories on the short and long term.

In this work, we do not discuss situated visualization strictly speaking because we do not choose the physical environment for its link to the data displayed. However, we focus on two close concepts: (1) physical landmarks in the environment could serve as comparative tools for the different features of the AR visualization and (2) the same physical landmarks could act as contextual memory cues to trigger involuntary memories or help retrieve information after having experienced the AR visualization. Thus, we want to take advantage of both semantic and episodic memory by having people discovering and learning features about datasets displayed in a familiar environment in which they live daily.

## 4.3 Technical Limitations

Despite important improvement in XR headsets over the last years, the current AR technologies suffer from several limitations. Our proofs of concept are implemented for the MetaQuest 3, taking advantage of the video pass-through technology. However, the occlusion management does not perfectly work. Occlusion is important because it allows to render virtual elements behind physical elements when necessary, increasing the likeness with reality. No occlusion leads to render all the virtual elements ahead of the physical elements, producing unlikely renderings (see Figure 5 (1)). However, the MetaQuest 3 has a hard time computing space's depth and rendering the virtual objects correctly. Currently, the Meta SDK supports two types of occlusion: the soft option and the hard option (cf Figure 5 (2) and (3)). Using a soft occlusion leads to a foggy rendering around physical objects, with virtual objects tending to recover physical ones. Using *hard* occlusion leads to a rendering with jagged edges, with physical objects tending to recover virtual ones. The hard occlusion is cheaper to compute. In both cases, boundary between virtual objects and physical objects is clearly visible. This might reduce the augmented reality experience and the immersion feeling and presence [12].

<figure><img src="image_6.png" /><figcaption>Fig. 5: Different occlusion settings with the MetaQuest3: (1) no occlusion, (2) hard occlusion and (3) soft occlusion.</figcaption></figure>

Moreover, our proofs of concept suffer of several limits regarding the range of locomotion. By default, the MetaQuest 3 restricts movement in the physical world with a built-in safety feature called the **Guardian**. This tool allow developers to select a zone in a room where people can move freely in AR or VR without hitting any obstacles. This safety area can be up to 15 meters by 15 meters. This area can be considered as small in the context of AROMs. In our proofs of concept, we were able to disable the **Guardian**. Considering AROMs are AR applications, disabling the **Guardian** presents a reasonable risk because people are able to see the physical environment at any time. However, when people are getting too far from the starting point of the visualization, an operating system's warning message triggers, interrupting the experience. People can simply accept the warning to proceed, but this unsolicited message could hinder presence and immersion.
Considering this technology is really recent and currently under
development, updates for the operating system and the Meta SDK are
often released. Between the first prototype of AROM in February 2025
and another test in July 2025, it seems that security pop-ups regarding
the position in the physical space do not behave the same. We can
assume that some of the limitations listed here could be solved in
a close future. We only tested AROMs on MetaQuest 3, but others
hardware could be investigated, such as the Apple Vision Pro, the
HoloLens 2 or with iOS or Android tablets.

## 4.4 Future work

### 4.4.1 Situated Visualization

Beyond the topology of the different location available, designers could explore different aspects proper of situated visualization. Among the data visualization field, some visualizations about physical objects can be displayed in AR or with data visualizations: it is called *situated visualizations*. Willett et al. define a *situated data representation*, as “*a data representation whose physical presentation is located close to the data’s physical referent(s)*” [46]. An example could be to represent the line chart picturing the electric consumption of a laptop next to it: here, the physical referent is the laptop and the data representation is the line chart. It allows displaying data in its context. Thus, people could understand better information and context [44], and some works suggest that situated visualizations could improve decision making [27].

Bressa et al. explain how *places* are important to display data, explaining how a location is shaped by its history, local identity and cultural heritage [9]. For example, it could be interesting to display data about the Second World War's casualties<fnref n="1" /> in a former battlefield.

Furthermore, it is interesting to display data in context. For example, it would make more sense to display a dataset about greenhouse gas emissions of food near a restaurant than next to a cliff, where displaying data about coastal erosion would make more sense.

### 4.4.2 Scale Choice

The proofs of concept presented in subsection 3.2 use abstract representations. It means the scale of the visualization can be changed according to designer's and user's needs.

A designer would probably choose a scale allowing the data to be legible, without occlusion between virtual objects. In *WalkingScatterPlot*, the scale has been chosen to prevent any occlusion between two data points near the origin of the visualization, where the smallest values are packed (Figure 4 (A)). However, this scale impacts the biggest values, which are displayed at a huge distance from the origin. Actually, in *WalkingScatterPlot*, the beef greenhouse gas emissions value is so big compared to the smallest value that it's displayed far beyond the limit of the parking lot. It is interesting to notice the data points with a high water consumption value are rendered rather high, making it difficult to read or to locate the points in the air without a way to move up and down. Thus, a designer would make sure that all the data points remain accessible and legible during the augmented reality experience, allowing people to properly ramble around data (see subsection 4.1.1).

Considering AROMs are designed for a specific location, questions remain regarding the reuse of an AROM in another physical environment. Considering abstract visualizations, such as bar charts and scatter-plots, designers would probably ensure that the new environment is big enough to contain the biggest value of the dataset. We could imagine a dynamic system to easily rescale an AROM to a new physical environment, allowing reusing the visualizations easily. This could allow non-experts to display AROMs in their own private space by rescaling them and by discovering datasets in a personal and familiar environment. Thus, users could spontaneously take advantage of physical landmarks to test AROMs in comparing visualization's size with objects in their immediate surroundings, creating "opportunistic" visualizations [19]. Adding a functionality for non-expert to rescale the visualization in a private environment could allow them to explore AROMs in familiar spaces. However, changing the scale without any

guidance could lead to lose from sight some data points if the scale is
increased. In this case, rambling in the visualization could become com-
plicated. A system including a world-in-miniature [14] could probably
help people during their exploration.

### 4.4.3 Extending to Other Types of Visualizations

In this position paper, we present three proofs of concept, mainly
focusing on abstract and aggregated visualizations as bar charts and
scatter-plots (see subsection 3.2). However, AR is well suited for other
designs, such as concrete visualizations. This type of visualization
has been conceptualized by Chevalier et al. and is “*the process of
visually relating complex measures with familiar objects from the real
world*” [11]. Typical examples of concrete visualizations are stacking
sugar cubes to represent the sugar contained in soda bottles [11], or
representing waste mass through a pile of trash bags [3]. We could
imagine using those types of visualizations in an AROM to represent
data in a more concrete and visceral way.

Since AROMs are using the physical space to render digital visual-
izations, we could imagine mixing AROMs with data visualization.
According to Jansen et al., a data physicalization is “*a physical artifact
whose geometry or material properties encode data.*” [23]. For exam-
ple, Perovich et al. designed human-sized physical bar charts, allowing
people to touch, push and squeeze the bars, increasing engagement
of participants [32]. We could measure the difference in cognitive
advantages between such kind of data physicalization with intangible
digital ones, getting inspiration of studies comparing VR human-sized
visualizations with data physicalization [15].

AROMs could also be well suited for data storytelling to support
understanding and exploration. According to Lee et al., data story-
telling consists in delivering a visual data story including several story
pieces backed up by data supported by visualizations presented in a
meaningful order [26]. Data storytelling has been broadly used with
immersive applications [31]. AROMs could benefit from such narrative
structure by guiding people during the exploration, building up knowl-
edge about the dataset through a progressive discovery. Such research
perspective echoes the concept of strollytelling where people explore a
data storytelling by walking [21].

## 5 CONCLUSION

In this position paper, we introduce *AROMs*, augmented reality visual-
izations spreading over several meters, inviting people to ramble in the
physical world around data covering several orders of magnitude. After
describing three proofs of concepts, we discuss research implications
regarding visualization challenges, cognitive aspects and technical lim-
itations. Thanks to their anchor in the physical world, AROMs might
be well suited for communicating about dataset covering datasets hard
to grasp due to their large difference in orders of magnitude. Further
work should be done to understand the potential effects of AROMs on
memorability and understanding, as well as on engagement.

## ACKNOWLEDGMENTS

The authors wish to thank Leana Petiot for her help on the cognitive dimension of this paper. This research was funded by the ANR grant ANR-22-CE33-0003.

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