MapStack: Exploring Multilayered Geospatial Data in Virtual Reality

Maxim Spur

1 a

, Vincent Tourre

1 b

, Erwan David

2 c

, Guillaume Moreau

1 d

and Patrick Le Callet

Architectural and Urban Ambiances Laboratory, Centrale Nantes, Nantes, France

Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany

Polytech’Nantes, Universit

e de Nantes, Nantes, France

Keywords:

Coordinated and Multiple Views, Virtual Reality, Geospatial Data Visualization, Immersive Analytics.

Abstract:

Virtual reality (VR) headsets offer a large and immersive workspace for displaying visualizations with stereo-

scopic vision, compared to traditional environments with monitors or printouts. The controllers for these

devices further allow direct three-dimensional interaction with the virtual environment. In this paper, we make

use of these advantages to implement a novel multiple and coordinated view (MCV) in the form of a vertical

stack, showing tilted layers of geospatial data to facilitate an understanding of multi-layered maps. A formal

study based on a use-case from urbanism that requires cross-referencing four layers of geospatial urban data

augments our arguments for it by comparing it to more conventional systems similarly implemented in VR:

a simpler grid of layers, and switching (blitting) layers on one map. Performance and oculometric analyses

showed an advantage of the two spatial-multiplexing methods (the grid or the stack) over the temporal mul-

tiplexing in blitting. Overall, users tended to prefer the stack, be ambivalent to the grid, and show dislike for

the blitting map. Perhaps more interestingly, we were also able to associate preferences in systems with user

characteristics and behavior.

1 INTRODUCTION

Analysis and decision-making in geospatial domains

often rely on visualizing and understanding multiple

layers of spatial data. Cartographers have for cen-

turies created methods of combining multi-layered in-

formation into single maps to provide multidimen-

sional information about locations. More recently, the

Semiology of graphics (Bertin, 1973), and research

on visual perception (Ware, 2012) led to advances in

understanding how e.g., visual channels can be best

employed to clearly represent as much data as pos-

sible in an effective and (often space-) efﬁcient way

(Munzner, 2014).

While the above led to established practices for

displaying many types of geospatial information, cre-

ating effective maps showing multilayered informa-

tion remains a nontrivial task even for domain experts,

and research is ongoing (Andrienko et al., 2007).

With an ever-increasing amount of spatial data being

https://orcid.org/0000-0001-7815-1915

https://orcid.org/0000-0003-4401-9267

https://orcid.org/0000-0002-5307-1795

https://orcid.org/0000-0003-2215-1865

collected and generated at faster rates, and a rising

demand to get ahead of this data, there may not al-

ways be the time and resources to craft bespoke map

visualizations for each new analysis task that requires

understanding a multitude of layers. It may also not

be practical to display too much information on one

map, no matter how well designed, when the maps

are too dense or feature-rich (Lobo et al., 2015).

An alternative solution is displaying multiple

maps of the same area at the same time. With comput-

erization, this approach of multiple coordinated views

(MCVs) (Roberts, 2007) was adapted to this use-case

(Goodwin et al., 2016), which can juxtapose different

maps, or layers of a map, on one or multiple screens,

and synchronize interactions between them, such as

panning, zooming, the placement of markers, etc.

A downside to spatial juxtaposition is a reduction

in the visible size of each map, as limited by screen

space, and the head/eye movement required to look

at different maps. On the other hand, MCVs should

be employed when the different views “bring out cor-

relations and or disparities,” and can help to “divide

and conquer,” or “create manageable chunks and to

provide insight into the interaction among different

Spur, M., Tourre, V., David, E., Moreau, G. and Callet, P.

MapStack: Exploring Multilayered Geospatial Data in Virtual Reality.

DOI: 10.5220/0008978600880099

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP, pages 88-99

ISBN: 978-989-758-402-2; ISSN: 2184-4321

Figure 1: Left: Temporal multiplexing (blitting); center and right: spatial mutliplexing — in a grid (center), and in our

proposed stack (right). Shown as implemented for the user study, with controller interaction.

dimensions,” as recommended in the guidelines set

forth by (Wang Baldonado et al., 2000).

Commodity-grade virtual reality headsets (VR-

HMDs) are steadily increasing their capabilities in

terms of resolution and ﬁeld of view, offering

an omnidirectional and much more ﬂexible virtual

workspace than what is practical or economical with

positioning monitors, prints or projections in a real

environment. Another beneﬁt VR-HMDs provide is

stereoscopic vision, which allows for a more natu-

ral perception of three-dimensional objects. Further-

more, VR devices such as the HTC Vive or the Oculus

Quest usually come with controllers that are tracked

in all axes of translation and rotation, presenting users

with direct means of three-dimensional interaction

with the virtual environment.

A crucial advantage of VR over AR for our appli-

cation is the complete control over the environment

even in small ofﬁces, whereas the translucent nature

of AR-HMDs requires a controlled real environment

— a large enough and clutter-free real background to

place the virtual objects. Another advantage with cur-

rently available headsets is the typically much larger

ﬁeld-of-view of VR-HMDs, providing less need for

head movements, and, crucially, showing more data

at the same time, which is essential for preattentive

processing (Healey and Enns, 2011).

These potential advantages appear applicable even

to the display of ﬂat topographic maps without 3D

features , and allow for different kinds of spatial ar-

rangements than otherwise feasible (no restrictions

on monitor numbers or placement). A case has been

made for separating and vertically stacking different

data layers of a map (Spur et al., 2017), an MCV ap-

plication which seems most suited for such an immer-

sive system.

For this work, we developed an implementation

of this stacking system (the titular MapStack, further

referenced as the stack) speciﬁcally with the VR case

in mind, as we believe this is where its advantages

are most pronounced and could be best utilized. The

main beneﬁt under analysis here is its hypothesized

ability to balance a trade-off of MCVs: stacking lay-

ers in this way allows them to be larger and still closer

together than by other means of juxtaposition. To

evaluate this stack’s performance in visualizing multi-

layered maps in a decision-making task, we set up a

controlled user study. In it, we compared the stack

to two more traditional methods of MCVs (Figure 1):

temporal multiplexing or blitting, where all layers oc-

cupy the same space and a user toggles their visibility,

and spatial multiplexing in a grid, showing all layers

side by side.

Even though these methods work well in the

traditional desktop computer environment, we im-

plemented them using the same VR environment

and means of interaction as our proposed stacking

method. This allows for a fairer comparison and con-

trols for the “wow-effect” of using VR, particularly

for test participants with little experience in it.

The design space for comparative or composite

(map) visualization encompasses more than these op-

tions (Javed and Elmqvist, 2012), but other methods,

such as overloading and nesting (e.g., by using a lens

or by swiping), appear more practical for just two lay-

ers, and have indeed been investigated for that pur-

pose (Lobo et al., 2015). To our knowledge, no stud-

ies exist to date on evaluating these map comparison

techniques with more than two layers or in VR.

Our paper contributes to research on multilayered

map visualization:

• with a novel spatial multiplexing approach based

on a stack of maps, derived from a study of the

available design space for comparison tasks and

its application in VR; and

• an evaluation of this stack wholly done in VR,

in comparison to two more traditional systems

within a controlled user study.

MapStack: Exploring Multilayered Geospatial Data in Virtual Reality

2 RELATED WORK

2.1 Urban Data Visualization

As a particular domain in geospatial visualization to

focus on, we chose the rapidly expanding ﬁeld of ur-

ban data visualization. In (Zheng et al., 2016), many

current examples of urban data visualization are given

from the point of view of visual (Keim et al., 2008)

and immersive analytics (Dwyer et al., 2018). Typical

systems present one type of information, or closely

related data like transportation on an interactive map

(Ferreira et al., 2013), or superimpose few and sparse

layers (Liu et al., 2011). While most systems work

with a ﬂat map view, some others started utilizing

and showing in perspective projection the 3D shape of

cities and buildings (Ferreira et al., 2015). While this

provides a better sense of the urban shape, occlusion

of data can occur — this is addressed in (Chen et al.,

2017a) by “exploding” the building models. Vertical

separation of data layers has also been done for legi-

bility purposes when there was no occlusion to miti-

gate in (Edler and Dickmann, 2015).

2.2 Immersive Analytics

The emerging ﬁeld of immersive analytics (Fonnet

and Pri

e, 2019) aims to combine the advances of

immersive technologies with visual analytics (Chan-

dler et al., 2015) and has already resulted in applica-

tions for large-scale geospatial visualizations (Yang

et al., 2018). Urban environments however have so

far mostly been immersively explored only in 3D city

models (Chen et al., 2017b), or by adding data to one

spatial layer (Filho et al., 2019).

2.3 Multiple and Coordinated Views

In (Knudsen and Carpendale, 2017), arguments for

immersive analytics are reiterated, with a call for

more research into its application to coordinated and

multiple views (interchangeably abbreviated to CMVs

or MVCs) (Roberts, 2007) — a powerful form of

composite visualization by juxtaposition (Javed and

Elmqvist, 2012). Many of the systems mentioned

above contain MCVs in the shape of a map view aug-

mented by connected tables or charts, others (Lobo

et al., 2017; Mahmood et al., 2018) also link related

maps in innovative ways. While studies have been

made to explore the efﬁcacy of different compositions

of such map views (Lobo et al., 2015), to our knowl-

edge they have so far only evaluated the case of two

map layers, and also not within immersive environ-

ments.

3 SYSTEM DESIGN

3.1 Visual Composition Design Patterns

As explained in (Lobo et al., 2015), combining mul-

tiple layers of geospatial data into one view can be

a straightforward superposition, as long as the added

information is sparse and the occlusion of the base

map or blending of color or texture coding is not an

issue. This is not the case when the map layers are

dense and feature-rich, and this is where other design

patterns of composite visualization views as deﬁned

in (Javed and Elmqvist, 2012) should be explored:

Juxtaposition: Placing visualizations side-by-side in

one view;

Superimposition: Overlaying visualizations;

Overloading: Utilizing the space of one visualiza-

tions for another;

Nesting: Nesting the contents of one visualization in-

side another; and

Integration: Placing visualizations in the same view

with visual links.

Superimposition methods, as opposed to the plain su-

perposition described above could be made useful if

applied locally, e.g., like a lens (Lobo et al., 2015).

With more than two layers though, a lens-based com-

parison interface quickly becomes less trivial to de-

sign, e.g., a “base” layer becomes necessary, as well

as either controls or presets for the size, shape and

placement of a potentially unwieldy number of lenses

(Trapp and D

ollner, 2019). When mitigating this by

using less lenses than layers, it becomes necessary to

fall back to temporal multiplexing as discussed below.

Overloading and nesting can be dismissed for map

visualizations. Though they are related to superimpo-

sition, they are deﬁned to lack a “one-to-one spatial

link” between two visualizations, which is central to

most map comparison tasks.

This leaves juxtaposition and its augmented form,

integration, which adds explicit linking of data items

across views. Those are familiar and relatively easy

to implement design patterns that have been shown to

increase user performance in abstract data visualiza-

tion (North and Shneiderman, 1997). The challenges

in designing effective juxtaposed views, as (Javed and

Elmqvist, 2012) describe, lie in creating “efﬁcient re-

lational linking and spatial layout”. The ﬁrst chal-

lenge could be addressed by relying on the integration

design pattern, and the second one is where we pro-

pose the vertical stack method as an alternative to be

evaluated against a more classical, ﬂat grid of maps.

In Gleicher’s earlier paper (Gleicher et al., 2011),

juxtaposition is also talked about in the temporal

IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications

sense: “alternat[ing] the display of two aligned ob-

jects, such that the differences ‘blink’”. Lobo et al.

(Lobo et al., 2015) refer to this as temporal multi-

plexing or blitting and see it also as a version of su-

perimposition. As one of the most common compo-

sition techniques (e.g., ﬂipping pages in an atlas, or

switching map views on mobile device), and one we

observed being used by the urbanists in our lab work-

ing with geographic information systems, we also in-

cluded it in our comparative study.

3.2 The Stack

Since the strategy a user will employ for comparing

the layers will be a sequential scan (Gleicher, 2018),

we believe a design where the distances between the

layers are minimal would fare better. Figure 2 shows

how the stack helps this sequential scan by presenting

each layer in the same visual way — all layers share

the same inclination relative to the viewer and are thus

equally distorted by perspective. Increasing this incli-

nation allows the layers to be stacked closer together

without overlap, while still preserving legibility up to

reasonable angles (Vishwanath et al., 2005).

Stack

Grid

Stack

Grid

Figure 2: Spatial arrangement of the layers in the stack and

grid systems, highlighting the larger visual distance (β) be-

tween layers in the grid, assuming same height (h) of the

maps, same minimum distance to viewer (d

), and same

gap between layers (γ). Viewing angles (α) and distances

(d) are constant in the stack and different in the grid.

Scanning through a stack also requires eye movement

in one direction only — all representations of an area

are aligned vertically. Additionally, the way the indi-

vidual maps are arranged in the stack mirrors the way

maps are traditionally, and still today, often viewed in

professional settings: as a ﬂat print or display on a

table, inclined to the viewer — even in VR applica-

tions (Wagner Filho et al., 2019). All maps share the

same relative inclination and distance to the viewer,

and thus the same perspective distortion, making them

easier to compare (Amini et al., 2014).

The stereoscopic display of VR gives an immedi-

ate clue that the maps are inclined and not just dis-

torted, helping the visual perceptive system decode

the effect of perspective and removes the need for ki-

netic depth cues (Ware and Mitchell, 2005). In ad-

dition, this inclination is also made clearly visible to

a user by framing the map layers in rectangles, which

helps to indicate the perspective surface slant. Studies

exist that show how picture viewing is nearly invari-

ant with respect to this kind of inclination or “viewing

obliqueness,” bordering on imperceptibility in many

cases (Vishwanath et al., 2005).

4 STUDY

4.1 Task Design

In (Schulz et al., 2013), the concepts of data, tasks,

and visualization are combined in two ways to ask

different questions:

Data + Task = Visualization? and

Data + Visualization = Task?

The ﬁrst combination asks which visualization needs

to be created for a given task and data, whereas the

second can follow as an evaluation process, once a

visualization has been deﬁned: how well can tasks be

performed on this data using this visualization?

To perform this evaluation, the task and data had

to be well deﬁned. Usually, the effectiveness of geovi-

sualization systems is evaluated with simpliﬁed tasks,

such as detecting differences between maps or ﬁnding

certain features on a map (Lobo et al., 2015). While

these methods can often be generalized to map leg-

ibility, we aimed at deﬁning a task that could more

directly test how well a system can facilitate an under-

standing of multiple layers of spatial data. Such task

design was focal to this project, and conducting the

experiment with it, the applicability of that method-

ology to evaluate geovisualization systems could also

be investigated.

4.2 Comparison Design Considerations

Gleicher argues that “much, if not most of analysis

can be viewed as comparisons” (Gleicher, 2018). He

describes comparison as consisting in the broadest

sense of items or targets, and an action, or what a

user does with the relationship between items.

Following Gleicher’s considerations on what

makes a “difﬁcult comparison”, our task needs to be

reﬁned:

the number of items being compared;

the size or complexity of the individual items; and

the size or complexity of the relationships.

The ﬁrst two issues we directly addressed by sim-

plifying the choice a user had to make. We divided the

area of the city for which we had coverage of all four

data layers into twelve similarly-sized regions — one

MapStack: Exploring Multilayered Geospatial Data in Virtual Reality

for each scenario. In each, we outlined three items

— the candidate areas, out of which a user would

then only select the one most “problematic” candi-

date. This considerably reduces the number of items,

their size and complexity. Having a ﬁxed number of

candidates in all scenarios allows us to compare com-

pletion time and other metrics, such as the number of

times participants switched their attention from one

candidate to another, in a more consistent way. Differ-

ent numbers of candidates could help generalize ﬁnd-

ings, but would also require accordingly longer or a

larger number of experiment sessions , an endeavor

which we relegate to future work.

To address the relationships’ complexity, we pro-

vided candidates that were as similar to each other as

possible, while differing in ways that are interesting

from the urbanist point of view. For example: One

scenario’s candidates are all segments with a round-

about, have similar energy consumption, but one of

them is close to a tram stop, and another has stronger

light pollution. We aimed for the users to balance

fewer aspects, while providing insights to urbanists

regarding the remaining differences that mattered the

most in a decision.

4.3 Use Case: Urban Illumination

With the help of a group of experts consisting of ur-

banists, architects, and sociologists, we developed a

use case around ongoing research into public city il-

lumination that requires understanding multilayered

spatial information to make informed choices. It re-

lies on four data layers (Figure 3):

Light Pollution: an orthoimage taken at night over

the city

Energy Consumption: a heatmap visualization of

the electrical energy each street lamp consumes

Night Transportation: a map of public transit lines

that operate at night and their stops, including bike-

sharing stations, and

Night POIs: a map of points of interest that are rele-

vant to nighttime activities.

Figure 3: The four data layers: light pollution, street light

energy consumption, nighttime transportation, and points of

interest at night.

Given these four layers, a user would be tasked with

identifying areas they consider most “problematic”,

based on an explanation on the signiﬁcance of each

layer and then comparing them. The user in this sce-

nario is a citizen, participating in shaping updates to

urban illumination — new regulations are being put

in place to limit light pollution and energy consump-

tion. Excesses should be reduced, while critical ar-

eas such as transportation hubs or highly frequented

places should stay well-illuminated, or even receive

additional lighting where not sufﬁciently present.

4.4 Implementation and Interaction

Using data provided by the local metropolitan area ad-

ministration (light pollution and street lamp informa-

tion) and OpenStreetMaps (transportation and points

of interest), we created the four layers as map styles

on the Mapbox platform (Mapbox, 2019). Using its

API, we could load these as textures directly into

Unity 3D (Theuns, 2017) to build a system for nav-

igating the layers and candidates.

The layers were presented as ﬂoating rectangular

surfaces with a 3:2 aspect ratio in front of the user,

at an apparent distance around 1.5 meters from the

viewer and perpendicular to the ground plane for blit-

ting and the grid, and tilted at about 45 degrees rela-

tive to the user’s viewing axis in the stack (Figure 2).

Tracking the position of the HMD in virtual space,

the individual layers were accordingly rotated to keep

vertical viewing angles constant, ensuring all layers

looked equally distorted by perspective from any po-

sition of the viewer.

The aforementioned position, size and orientation

of the layers were determined with direct feedback

from the previously mentioned group of experts dur-

ing the development and pre-testing phases to achieve

a comfortable display — similar to a printed map ly-

ing ﬂat on a table in front of a viewer. Since users are

free to move around in a sitting position in a rolling

ofﬁce chair and adjust their view, or even stand up as

they see ﬁt during viewing, a more precise method of

devising those parameters was deemed not necessary

for this study. Our goal here was to make viewing the

three methods equally comfortable for a fair compar-

ison.

We used the HTC Vive kit, which provides con-

trollers tracked in 3D space. The two controllers were

divided into two main functions: one for controlling

the map — panning, zooming, and switching layers

in the blitting system, and the other for controlling

the candidates — highlighting, fading, selecting and

conﬁrming the selection of chosen candidate areas.

Panning and zooming was accomplished by holding

the side button and moving the controller in 3D space

— motion parallel to the map plane translated the im-

IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications

age, while perpendicular motion (pushing or pulling

it) translated to zooming. This controller was given to

users in their dominant hand, as the highest dexterity

required for the other controller was to just swipe a

thumb across the touchpad to select candidates on an

annular menu.

As an additional visual aid, pressing the map con-

troller’s trigger activates a “laser beam” emanating

from the tip of the controller, painting a marker at the

point where it hits a map layer. In the case of the

grid and stacking views, that marker (a green sphere)

is mirrored on each layer, and in the latter case also

linked by a thin green line. This creates additional

visual linking, ranging from implicit to explicit and

elevating the stacking view to an integrated view de-

sign pattern (Javed and Elmqvist, 2012).

4.5 Participants

26 participants took part in the experiment (9 female,

17 male), with ages ranging from 18 to 45 years

(M=21, SD=6.33).

21 reported as currently being students, with 17

holding at least a bachelor’s degree. Most participants

had either never tried VR before (10), or for only less

than ﬁve hours total (13). Two have had between ﬁve

and twenty hours of VR experience, and one more

than twenty hours. We also asked about experience

with 3D video games: Nine participants reportedly

never play those, nine others only a few times per

year. Four play a few times per month, one a few

times per week and three play every day.

The participants’ responses to questions about

their familiarity with the city we visualized, its map

and their comfort of reading city maps were normally

distributed on the visual analog scales we employed.

All participants were tested on site for visual acuity

and colorblindness and all have passed.

4.6 Stimuli

As described in subsection 4.2, the map of the city

for which we had data coverage was divided into sce-

narios that each contained three candidate areas for

consideration. One stimulus thus consists of a pairing

between a viewing system and a scenario — a portion

of the map beyond which the user could not pan and a

limited zoom range, and the three candidate areas pre-

selected for this portion. Twelve such scenarios were

created — one was chosen to always be shown ﬁrst,

in the ﬁrst tutorial that introduced the layers and the

interactions with the blitting system. The remaining

eleven scenarios were presented in random order —

the next two tutorials, which introduced the remain-

ing two systems also used a random scenario from

this pool.

4.7 Design And Procedure

The experiment was a within-subject design — each

participant was exposed to each system equally, and

we could directly inquire about preferences among

the systems. It was further split into a tutorial and

evaluation phase, consisting of multiple scenarios.

Each scenario consisted of the task, preceded by in-

structions (full introductions in the tutorial phase, and

short reminders in the evaluation phase), and a ques-

tionnaire part. Completion of a task stopped the data

recording and prompted the participant to remove the

headset to proceed to the questionnaire on a separate

PC, which then guided to the next task and its intro-

duction.

4.7.1 Tutorial Phase

After a brief introduction on how to handle the VR

headset and controllers by the experimenter, the tuto-

rial phase — consisting of three scenarios — began.

The ﬁrst scenario showed the same map region and

used the blit layering system for each user, to ensure

maximum consistency in their training. Blitting was

chosen here as this is the closest to what participants

were likely to already be familiar with from using dig-

ital maps, and because it appears as just a single map

at a time, which allowed to explain the signiﬁcance of

each map layer in sequence and without interference.

At ﬁrst, all controller interaction is disabled. The

tutorial system gradually introduced and enabled pan-

ning, zooming, and blitting the maps, and selecting

and conﬁrming candidates by asking the participant

to perform simple tasks and waiting for their success-

ful completion. While introducing the blitting mech-

anism, each layer was explained in detail — partic-

ipants could not switch to the next one before con-

ﬁrming their understanding of the summary. The ﬁrst

scenario concluded with a reiteration of the common

task of all scenarios: selecting and conﬁrming one of

the three candidates, following by the instruction to

remove the VR headset and proceed with the ques-

tionnaire, which introduced the types of questions that

will be answered throughout the session.

The second and third scenarios introduced in a

similar fashion the grid and stack systems, omitting

the blitting and individual layer explanations. They

similarly ended with the actual task and the question-

naire. Here, the order was balanced between the par-

ticipants: half were ﬁrst exposed to the grid, the other

half to the stack. The map regions were randomized

from this point on.

MapStack: Exploring Multilayered Geospatial Data in Virtual Reality

4.7.2 Evaluation Phase

The remaining nine scenarios were divided into three

blocks of three tasks: a repeating permutation of the

three layering systems (G(rid), S(stack), B(blit)), bal-

anced across the participant pool, e.g., GSB-GSB-

GSB, or BGS-BGS-BGS. The tutorial system was

pared down to only instruct participants in the blitting

system to switch between all layers at least once, and

to show reminders of the controller functions as well

as the layer descriptions if requested. Instructions ap-

peared to evaluate the scenario, pick a candidate and

proceed to the questionnaire.

4.7.3 Balancing

The balancing may seem to be impacted by this

choice of procedure, but we believe the beneﬁts of

presenting blitting — the simplest of the three sys-

tems in terms of visual complexity — outweighs the

downsides, especially since we were particularly in-

terested in whether the differences between the two

spatial multiplexing systems were signiﬁcant. The

simplicity of blitting allowed us to craft an in-system

tutorial that is consistent for each participant and

gradually eases them into interpreting the visuals and

interacting with the environment. This consistency

was ultimately deemed to be more “balancing” in our

view than a random choice of system for the ﬁrst pre-

sentation — the rest of each session (the nine trials

after the tutorial part) was completely balanced.

4.8 Apparatus and Measurements

The test procedure was split between two devices: the

HTC Vive VR setup, and a separate PC running a

questionnaire (Guse et al., 2019). All instructions af-

ter the start of the experiment (including the tutorial)

and interactions with the participants were handled

automatically by the questionnaire and the prototype:

instructing the user to put on the headset, instructing

to select one candidate and taking the headset off af-

ter having done that, asking questions about the per-

ceived workload, asking to put the headset back on

for the next task, and so on. This procedure had the

additional beneﬁt of providing the participants with

regular breaks from wearing the headset and the asso-

ciated physical and mental fatigue.

For interacting with the software prototype, the

HTC Vive was used, connected to a Windows PC ca-

pable of running it at the maximum frame rate of 90

Hz and the maximum resolution of 1080 × 1200 pix-

els per eye (approx. 100×100 degrees of ﬁeld of view

excited binocularly). Standard HTC Vive controllers

were used, and the headset was ﬁtted out with an SMI

binocular eye tracking device, capable of sampling

gaze positions at 250 Hz with a reported accuracy of

0.2 degrees.

For subjective assessments, we asked the partic-

ipants via questionnaire for explanations as to why

they chose each candidate after each trial, and ﬁnally,

presented post-hoc questions about pairwise prefer-

ences for the systems in terms of legibility, ease of

use and visual design, as well as solicited free-form

feedback for each system in form of a voluntary text

ﬁeld.

In addition to this declarative user feedback, we

also recorded performance aspects (completion times,

interaction measurements) and physiological data via

oculometry. Eye movements bring a wealth of infor-

mation — they are overt clues about an observer’s at-

tention deployment and cognitive processes in gen-

eral, and are increasingly being tracked for evaluat-

ing visual analytics (Kurzhals et al., 2016). In the

context of map reading, measuring gaze allows us to

know precisely which map layers participants chose

to observe in particular, and at which times. Further-

more, gaze features and their properties, such as sac-

cades and ﬁxations can be derived, in this case by pro-

cessing with the toolkit developed for the Salient360!

dataset (David et al., 2018), using a velocity-based

parsing algorithm (Salvucci and Goldberg, 2000).

5 RESULTS

5.1 User Preferences

The post-hoc, pairwise questions about user prefer-

ences for the systems asked which one of two (ran-

domly ordered and balanced, creating full pairwise

comparison matrices (PCMs)) they thought was bet-

ter in terms of map legibility, ease of use, and visual

design. The questions were clariﬁed, respectively:

1. Which system showed the map layers in the clear-

est way and made them easier to understand for

you?

2. Which system made interaction with the maps

and candidate areas easier for you?

3. Which system looked more appealing to you?

From the PCMs, we derived rankings for each system

under each aspect, as shown in Figure 4(a). This was

done by counting the number of times a system “won”

against another in the pairwise comparisons — two

times means it is “preferred” by the user, one time

puts it in second place, and zero times in the third and

last place — it then is the “disliked” system under that

aspect.

IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications

Readability

25%

50%

75%

100%

Blit

Grid

Stack

36%

40%

23%

32%

60%

12%

32%

65%

3rd

2nd

1st

Ease of Use

Blit

Grid

Stack

50%

42%

15%

54%

31%

35%

62%

Visual Design

Blit

Grid

Stack

58%

15%

27%

12%

69%

19%

31%

15%

54%

(a) System

Rankings

Readability

25%

50%

75%

100%

Blit

Grid

Stack

36%

40%

23%

32%

60%

12%

32%

65%

Stack

Grid

Blit

Completion times

Seconds

120

Blit

Grid

Stack

Total

Blit Grid

-0.5 0.5 1.5

Blit Grid

-0.5 0.5 1.5

Blit Grid

-0.5 0.5 1.5

Blit Grid

-0.5 0.5 1.5

Blit Grid

-0.5 0.5

Blit Grid

-0.5 0.5

(b) Bradley-

Terry Scores

Table 1-1-1

Blit

Grid

Stack

0 ﬁrsts

58%

38%

31%

1 ﬁrsts

27%

23%

2 ﬁrsts

15%

35%

19%

3 ﬁrsts

27%

Number of “Wins”

25%

50%

75%

100%

Blit

Grid

Stack

27%

19%

35%

15%

23%

27%

31%

38%

58%

0 wins

1 wins

2 wins

3 wins

Table 2

Blit

16%

Grid

36%

Stack

48%

Overall Preferred

25%

50%

75%

100%

48%

36%

16%

Blit

Grid

Stack

Table 1-2-1

Blit

Grid

Stack

3 lasts

38%

19%

2 lasts

27%

12%

1 lasts

12%

19%

15%

0 lasts

23%

81%

54%

Table 1-1-1-1

Blit

Grid

Stack

0 ﬁrsts

58%

38%

31%

1 ﬁrsts

27%

23%

2 ﬁrsts

15%

35%

19%

3 ﬁrsts

27%

Number of “Lasts”

25%

50%

75%

100%

Blit

Grid

Stack

54%

81%

23%

15%

19%

12%

27%

19%

38%

3 lasts

2 lasts

1 lasts

0 lasts

Table 2-1

Wins

Lasts

Blit

16%

42%

Grid

36%

27%

Stack

48%

31%

Overall Disliked

25%

50%

75%

100%

31%

27%

42%

Blit

Grid

Stack

Figure 4: (a) How often (in percent) each system was

ranked ﬁrst, second, or last in terms of legibility, ease of

use, and visual design by the users; (b) Pairwise compar-

isons scored with the Bradley-Terry model.

The blitting system came in last in each regard

with more than half of our participants. The grid was

close to evenly rated ﬁrst or second in terms of legi-

bility and ease of use (only one participant rated it last

in ease of use), while being behind the stack in terms

of best-ranked visual design and also slightly in ease

of use.

The same PCMs were ﬁt with the Bradley-Terry

model (Bradley, 1984) to assign each system a rel-

ative score (c.f. Figure 4(b)); the results mirror the

preference rankings in Figure 4(a), aside from giving

an advantage to the grid over the stack in all but visual

design. The grid evoked quite consensual responses,

being most often rated as second place in all aspects

and almost never as the worst. The stack received

the most ﬁrst place rankings, but also a considerable

number of last place ones, showing that it provoked

stronger “love it or hate it” responses. The blit system

is similar in that regard, only that the best and worst

ratings are reversed for it.

Condensing those pairwise comparisons further,

we counted how many times each system was given

a “ﬁrst” and a “last” ranking by each user, combined

across all aspects, shown in Figure 5 (a). This shows

how the majority of users gave the blit system zero

ﬁrsts — it is not the “best” under any aspect for them,

and how the stack was the only system to receive all

three possible ﬁrsts by any users (a quarter of our sam-

ple). The number of “lasts” received mirrors those

observations, and further highlights how the grid was

a middle ground — receiving only very few single

“lasts,” if at all. Most participants found either the

stack, or more so, the blit system worse than the grid

in at least one aspect.

The last distillation of the PCMs results in which

system a user “preferred” or “disliked” overall, by

choosing the one that has received the most “ﬁrsts”

or the most “lasts,” (as described above) respectively,

shown in Figure 5 (b). By this measure, almost half

our participants preferred the stack, and only a small

Number of “Firsts”

25%

50%

75%

100%

Blit

Grid

Stack

27%

19%

35%

15%

23%

27%

31%

38%

58%

0 ﬁrsts

1 ﬁrsts

2 ﬁrsts

3 ﬁrsts

Overall Preferred

25%

50%

75%

100%

48%

36%

16%

Blit

Grid

Stack

Number of “Lasts”

25%

50%

75%

100%

Blit

Grid

Stack

54%

81%

23%

15%

19%

12%

27%

19%

38%

3 lasts

2 lasts

1 lasts

0 lasts

Table 2-1

Wins

Lasts

Blit

16%

68%

Grid

36%

Stack

48%

32%

Overall Disliked

25%

50%

75%

100%

32%

68%

Blit

Grid

Stack

(a) First/Last

Counts

(b) Extreme

Ratings

Figure 5: (a) Which proportion of participants gave the sys-

tems a number of zero to four ﬁrst or last rankings across

all aspects (legibility, ease of use, and visual design); (b)

which proportion gave the most “ﬁrsts” (and therefore “pre-

ferred”) or “lasts” (and therefore “disliked”) to each system

in the pairwise comparisons.

fraction the blit system, while the blit was disliked by

a majority, and not a single user rated the grid system

as the worst in most aspects.

5.2 Subgroupings

Since our main goal was to evaluate the legibility of

the systems, we used the rankings from Figure 4 to

split the participants’ data (Figure 6 and Figure 7)

into three subgroups (plus the total): pB, pG, and

pS. Those refer to data from users who preferred the

B(lit), G(rid), or S(tack) system, i.e., ranked it ﬁrst

in the pairwise comparisons under that aspect. This

could be done since the number of participants who

did so were roughly comparable: out of the 26 total

participants, 6 fell into pB, 10 into pG, and 9 into pS.

5.3 Interactions

Task completion times (Figure 6(a)) show how pG

users were faster than pB, and pS faster still, while

the total differences between the systems are balanced

out by the preference groups.

The stacking system saw the most gaze switches

(Figure 6(b)) from layer to layer, and blitting the least,

with a lower variability from subgrouping — this can

clearly be attributed to the requirement to manually

switch between layers for the blit system, as opposed

to just switching one’s gaze over. There is a tendency

for pS users to make the fewest gaze changes across

all systems than other users, most strongly though in

the blitting system.

5.4 Oculometry

Saccade amplitudes (Figure 7(a)) were lowest in the

grid and highest in the stack, with pS users having

MapStack: Exploring Multilayered Geospatial Data in Virtual Reality

(a) Saccade amplitudes

Degrees

6.5

7.5

Blit

Grid

Stack

Total

7.6

7.0

6.7

6.8

7.1

6.3

6.9

7.2

6.7

6.9

Milliseconds

210

220

230

240

250

Blit

Grid

Stack

Total

227

232

235

230

233

239

237

241

240

232

235

238

(a) Completion times

Seconds

120

Blit

Grid

Stack

Total

(b) Gaze changes between map layers

Number

100

Blit

Grid

Stack

Total

(b) Saccade peak velocities

Degrees per µs

Blit

Grid

Stack

Total

19.2

17.3

18.1

21.7

20.0

18.3

17.7

16.3

13.7

19.3

18.2

16.3

Table 2-1-1-1

Total

16.33

18.21

19.30

13.71

16.27

17.67

18.29

20.02

21.66

18.08

17.31

19.16

(a) Saccade amplitudes

Degrees

6.5

7.5

Blit

Grid

Stack

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

7.6

7.0

6.7

6.8

7.1

6.3

6.9

7.2

6.7

6.9

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

(a) Saccade amplitudes

Degrees

6.5

7.5

Blit

Grid

Stack

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

7.6

7.0

6.7

6.8

7.1

6.3

6.9

7.2

6.7

6.9

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Figure 6: Each system’s recorded performance mea-

surements (means and bootstrapped conﬁdence intervals);

grouped by users who preferred the blit, grid or stack sys-

tem (pB, pG, pS) and in total.

a tendency to make the largest, especially in their

preferred system. In contrast, saccade peak veloci-

ties (Figure 7(b)) were highest with pG and lowest

with pB users, and the differences across the systems

are corresponding to those with gaze changes (Fig-

ure 7(a)).

Mirroring the number of gaze changes and sac-

cade peak velocities, users in general ﬁxated the

shortest (Figure 7(c)) in the stack, and the longest in

the blitting system. That trend is followed by pG and

pS (who had the shortest of all), but not by pB users,

who had consistently higher ﬁxation durations, less

affected by the systems.

5.5 User Characteristics

Of the personal characteristics we gathered about the

participants, their habits with 3D-based video games

yielded the most interesting results when paired with

their preferred and disliked systems, as shown in Fig-

ure 8. Most of those who never play 3D video games

prefer the grid, the rest the stack, and none the blit

system. Most of those who identify as playing at least

a few times per month prefer the stack.

Concerning the “disliked” system chart, the blit

system overwhelmingly earned the least favorite sta-

tus, from all kinds of participants almost proportion-

ally to their distribution in our sample. As seen before

in Figure 5 (b), none placed the grid system last.

(a) Saccade amplitudes

Degrees

6.5

7.5

Blit

Grid

Stack

Total

7.6

7.0

6.7

6.8

7.1

6.3

6.9

7.2

6.7

6.9

Milliseconds

210

220

230

240

250

Blit

Grid

Stack

Total

227

232

235

230

233

239

237

241

240

232

235

238

(a) Completion times

Seconds

120

Blit

Grid

Stack

Total

(b) Gaze changes between map layers

Number

100

Blit

Grid

Stack

Total

(b) Saccade peak velocities

Degrees per µs

Blit

Grid

Stack

Total

19.2

17.3

18.1

21.7

20.0

18.3

17.7

16.3

13.7

19.3

18.2

16.3

Table 2-1-1-1

Total

16.33

18.21

19.30

13.71

16.27

17.67

18.29

20.02

21.66

18.08

17.31

19.16

(a) Saccade amplitudes

Degrees

6.5

7.5

Blit

Grid

Stack

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

7.6

7.0

6.7

6.8

7.1

6.3

6.9

7.2

6.7

6.9

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

(a) Saccade amplitudes

Degrees

6.5

7.5

Blit

Grid

Stack

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

7.6

7.0

6.7

6.8

7.1

6.3

6.9

7.2

6.7

6.9

Total for system

by users who preferred Blit (pB)

by users who preferred Grid (pG)

by users who preferred Stack (pS)

Figure 7: Each system’s recorded physiological mea-

surements (means and bootstrapped conﬁdence intervals);

grouped by users who preferred the blit, grid or stack sys-

tem (pB, pG, pS) and in total.

3D Video Game Playing Frequency

EveryDay

SomePerWeek

SomePerMonth

SomePerYear

Never

Preferred System

Blit

Grid

Stack

Disliked System

Blit

Grid

Stack

Figure 8: The numbers of participants who play 3D-based

video games at different frequencies, and their distribution

among who “preferred” and “disliked” each system.

5.6 User Feedback

The free-form feedback yielded positive and negative

commentary for all systems. Complaints about the

blit system included the need to constantly change

layers, making comparisons more difﬁcult or taking

more time. The bigger surface of the single map in it

was remarked as a positive, in fact as being better or

simpler than the other systems for analyzing a single

IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications

layer.

The grid system received mostly positive remarks

in terms of giving a good and simple overview over

the layers, however there were complaints about the

reduced size of each layer and also about the need for

large head movements to view them in sequence.

The comments about the stack were mostly sug-

gestions about a speciﬁc part that needs improve-

ments, rather than comparisons to the other systems:

position, orientation, and size of the layers were all

suggested to be changed in speciﬁc ways. Some ex-

pressed the demand for a way to change the orien-

tation and order of the layers. The direct positives

named were a practical “ensemble” view of all layers,

and it being the fastest system to compare them.

Interestingly, the participants’ overall system pref-

erences, or even particular comparisons had little

bearing on the kind of feedback they offered — users

expressing that one system as their favorite did not

necessarily choose that one during the pairwise com-

parisons over the other systems. What did correlate

however to the content of their feedback was their

categorization into 3D gaming frequency — those

who play more were generally more likely to offer

more detailed and constructive feedback, and were

also more inclined to comment positively on the stack

and its potential for improvement.

5.7 Discussion

Our exploration-based task design with no “correct”

answers allowed participants to freely interact with

the map layers, coming up with their own strategies.

Measurements and questionnaires allowed us to link

preferences with behavior, showing how there is no

one system that is universally better, only better suited

for certain behaviors. Examples linking behavior and

preferences, as taken from ﬁgures 7 and 6 are:

• Users with the lowest peak saccade velocities, low

saccade amplitudes and longest ﬁxation durations

preferred the blit system, which allowed switch-

ing layers in place instead of moving their eyes.

• Users who preferred the grid appeared to be those

most comfortable with quickly bridging the large

distances between layers in that system — they

were the ones with the fastest peak saccade veloc-

ities.

• Users with the fastest completion times and the

fewest gaze changes between layers preferred the

stack — that system, with its short distance be-

tween layers may have allowed them to view mul-

tiple ones from one ﬁxation point, without need-

ing to shift their gaze to neighboring layers. Mul-

tiple users, particularly those in favor of the stack

expressed a wish to be able to rearrange the layers,

probably to aid this behavior further.

Completion times did not vary substantially from sys-

tem to system, but the number of gaze changes did.

The “distance” between layers (physical, or temporal

by way of switching) is anti-proportional to that num-

ber, and proportional to ﬁxation durations — a shorter

distance reduced the barrier to switch attention to dif-

ferent layers.

When looking beyond performance and physio-

logical measurements and deeper into the preferences

and user characteristics, more patterns emerge that

could explain users’ perception and acceptance of a

system. Participants who were already familiar with

navigating 3D environments in the form of video

games were more likely to take advantage of the con-

trols and views offered in both the blitting and the

stack system. One requires more interaction, the other

perhaps an ability to understand “unusual” spatial ar-

rangements. The “middle ground,” i.e., the grid is by

far preferred among those who play video games the

least: it requires no interaction to switch layers, and

the layout is much simpler than the stack.

In this study, we deliberately evaluated the three

concepts in isolation, i. e. each system on its own, and

not a combination of them, making effects easier to

separate. We especially did not compare the layering

systems to a single map that contains all information

in one layer. We assume a situation where that is not

a practical solution — if it were, there would be no

need for a separation into any of the layering systems

in the ﬁrst place.

We also limited the scenarios to four data layers

to keep an “even playing ﬁeld”: increasing that num-

ber would have resulted in worse layer navigation in

the blitting system (no longer mapped to cardinal di-

rections on the touchpad, requiring ﬁner interaction),

and smaller tiles with often irregular arrangements

(such as for prime numbers of layers) for the grid sys-

tem. We therefore believe stacking could accommo-

date higher numbers of layers more easily — needing

only slightly more vertical space and/or ﬂatter angle

for more layers — and would have a clearer advantage

over the other systems in those cases.

Similarly, we also limited customizability by hav-

ing the sizes, shapes and orientations of all systems

ﬁxed for consistency between users and trials, though

there were multiple wishes for exactly that being ex-

pressed by the participants.

MapStack: Exploring Multilayered Geospatial Data in Virtual Reality

6 CONCLUSION

We investigated an extension of MCVs into VR and

cartography with comparisons of map layers greater

in number than two, while contributing to research

into visual and immersive analytics of multilayered

geospatial data.

Arguing from previous work on composite visual-

ization, we introduced a novel MCV system speciﬁ-

cally tailored to VR and evaluated its merits using a

task design that is close to actual tasks in urbanism

and similar geospatial domains. Our analysis shed

light on differences in users’ map reading behavior

and how that affects their judgement of different sys-

tems, or which kinds of comparison views are better

suited to which users.

Furthermore, that analysis through different as-

pects (user preferences, characteristics and perfor-

mances) showed there is no one measurement sufﬁ-

cient to compare or judge systems. Slower comple-

tion times could mean a deeper focus on the task, and

a high or low number of gaze changes between maps

could indicate both more or less comparisons being

done, just as well as a feeling of concentration or of

being lost.

Different users can have opposing priorities and

preferences when it comes to these systems, so op-

timizing for one type would probably make it worse

for another. This came to light by limiting the ﬂexi-

bility of our participants in their choice of system or

arrangement, and brought us to the conclusion that

precisely that ﬂexibility is what may be necessary in

a truly useful system.

Future work could see an implementation of re-

quested features, such as being able to rearrange the

order of layers and their shape and position. A hy-

brid system, combining the advantages of blit, grid

and stack should also be investigated. With the stack

by itself being shown to be usable, a number of them

side-by-side — like a tilted grid, or even cyclically

arranged — could accommodate a larger number of

layers, especially if those layers can be grouped by

columns, like quarterly data in different years.

A different kind of user study could also be set

up that presents participants with all available options

(choice of system, possibilities of rearrangement) and

lets them freely choose and customize as they see ﬁt

for their task at hand. Switching up the number of

layers or other interactive elements could then shed

light on which conﬁgurations work best for which

scenario.

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