Enhancing Usability in Large Map Interactions: A Novel Magnifying

Lenses Technique for Tabletops

Fernando Pedrazzi Pozzer

1 a

, Gustavo Machado de Freitas

1 b

, Gabriel Fronza Schuster

2 c

Felipe Marques de Carvalho de Oliveira

3 d

, Cesar Tadeu Pozzer

3 e

and Lisandra Manzoni Fontoura

4 f

Curso de Ci

encia da Computac¸

ao, Federal University of Santa Maria, Santa Maria, Brazil

Curso de Engenharia da Computac¸

ao, Federal University of Santa Maria, Santa Maria, Brazil

Departamento de Computac¸

ao Aplicada, Federal University of Santa Maria, Santa Maria, Brazil

Programa de P

os-Graduac¸

ao em Ci

encia da Computac¸

ao, Federal University of Santa Maria, Santa Maria, Brazil

Keywords:

Usability, Tabletops, Focus+Context, Overview+Detail, Large Maps, NASA-TLX.

Abstract:

Tabletops are large interactive displays that enable users to interact for collaborative analysis and planning on

datasets. These devices allow communication using pointing and gestures and interactive zooming, searching,

and modifying data. However, when interacting with large maps, the user must analyze details that can only

be viewed by enlarging the map region. Furthermore, it is interesting to visualize the context (zoom out)

and the details of a speciﬁc area (zoom in). Magnifying lenses are often used for this purpose, but these

lenses have the disadvantage of losing context. In this paper, we present a new technique for interaction using

magnifying lenses for tabletops to improve interaction usability in large maps. We analyzed several techniques

discussed in the literature for magnifying lenses. We performed experimental implementations in the context

of the SIS-ASTROS GMF Project. We validated the work through objective and subjective analyses, and the

results demonstrated signiﬁcant improvements in objective metrics, such as time and accuracy, as well as in

subjective metrics, including frustration, effort, and mental demand. The NASA-TLX Questionnaire was used

to evaluate the subjective metrics.

1 INTRODUCTION

The use of interactive surfaces, such as tabletops,

has been shown to enhance performance across vari-

ous tasks (Fitzmaurice and Buxton, 1997) and to be

a promising tool in collaboration (Mateescu et al.,

2021) and planning scenarios involving large data

sets, especially in contexts that require analysis and

manipulation of large-scale maps (D

oweling et al.,

2016). Interactive tabletops facilitate this collabora-

tion by allowing multiple users to interact simultane-

ously with digital maps, promoting a common under-

https://orcid.org/0009-0009-2860-1135

https://orcid.org/0009-0001-1326-4238

https://orcid.org/0009-0006-9771-4860

https://orcid.org/0009-0004-5734-5904

https://orcid.org/0000-0002-3321-4828

https://orcid.org/0000-0002-4669-1383

standing of the situation and the improvement of im-

plemented measures (Westendorf et al., 2017).

Tabletops have been used in contexts such as

education (Bortolaso et al., 2014), health (Yang

et al., 2014; Madni et al., 2016), crisis management

oweling et al., 2016), trafﬁc simulation (Kubicki

et al., 2013), among others. Their use presents sat-

isfactory results with map-related systems due to the

panoramic view. In health, the number of elements

that can be displayed on the screen allows more de-

tails about the medical image to be seen. It sup-

ports better analysis (Yang et al., 2014; Madni et al.,

2016). D

oweling et al. (2016) described the use of

tabletops in contexts such as crisis management, in

which collaboration between different organizations

is essential for situational analysis and action plan-

ning. Furthermore, these devices offer an intuitive

interface that improves the efﬁciency and quality of

teamwork, overcoming the limitations of traditional

448

Pozzer, F. P., Machado de Freitas, G., Schuster, G. F., Marques de Oliveira, F. C., Pozzer, C. T. and Fontoura, L. M.

Enhancing Usability in Large Map Interactions: A Novel Magnifying Lenses Technique for Tabletops.

DOI: 10.5220/0013300100003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 448-459

ISBN: 978-989-758-749-8; ISSN: 2184-4992

workstation-based solutions (D

oweling et al., 2016;

Salvador-Herranz et al., 2016).

Despite the advantages, implementing interactive

tabletops presents several challenges. One of the main

ones is the need for visualization techniques that al-

low users to switch between high-level and planned

views of the map without compromising collabora-

tion (Bortolaso et al., 2014; Liu et al., 2023; For-

lines and Shen, 2005). In large datasets, highlight-

ing a part of the information is a widespread oper-

ation. However, contextual data (the area surround-

ing the interest area) is usually lost when this opera-

tion involves zooming. This loss can compromise the

user’s analysis and, according to Forlines and Shen

(2005), making zoom-in and zoom-out operations is

time-consuming. There are a few attempts to solve

this problem using magniﬁcation lenses, as seen in

the survey by Tominski et al. (2017).

FingerGlass (K

aser et al., 2011), DTLens (For-

lines and Shen, 2005), and OrMiS (Bortolaso et al.,

2013) are multi-touch interfaces that explore human-

computer interaction, focusing on zooming tech-

niques and visual content manipulation. FingerGlass

aser et al., 2011) offers a virtual lens for magnify-

ing speciﬁc regions on smaller screens, DTLens (For-

lines and Shen, 2005) allows multiple users to explore

different parts of a map or diagram simultaneously,

and OrMiS (Bortolaso et al., 2013) uses a virtual lens

to focus on speciﬁc objects within a simulation for

detailed exploration. However, no proposed approach

was considered suitable for an environment that con-

sists of analyzing and interacting with large maps with

minimized context loss. Hence, the necessity of cre-

ating a new technique emerges.

The approach proposed in this paper, although ap-

plicable to any application with large-scale maps, is

intended to be used in an integrated simulation envi-

ronment for the Artillery of a national Army. This

environment presents a tactical virtual simulator for

training mid-rank ofﬁcers in rocket launching activi-

ties using a saturation rocket system. It is necessary

to note that the exercises are intended for one trainee

at a time. This fact impacts the design of the solution,

which, unlike other augmenting lens techniques such

as those presented by Bortolaso et al. (2013) and For-

lines and Shen (2005), does not have the intention to

promote better simultaneous collaboration.

Our approach consists of a magnifying lens di-

vided into two circles. The smaller one called the

indicator is positioned by the user over the area of in-

terest and has a small inner circle that indicates the

amount of zoom applied. The larger circle called

the viewport is adjacent to the indicator - just like

an overview+detail display (K

aser et al., 2011) - and

covers only a small part of the context regardless of

the amount of zoom, taking into account the concept

of focus+context (Bortolaso et al., 2013).

We aim to introduce a magnifying glass that en-

hances the usability of interaction on large maps, par-

ticularly when highlighting and interacting in speciﬁc

areas of interest. These features are designed to re-

duce the loss of context and enhance interaction efﬁ-

ciency, ultimately beneﬁting the user experience.

The remainder of this paper is structured as fol-

lows: Section 2 presents some fundamental con-

cepts regarding magnifying glasses in tabletop envi-

ronments. Section 3 describes related work and some

speciﬁcations of the environment in which this tech-

nique must be implemented. Section 4 presents the

method used in this paper as well as the proposed ap-

proach. Section 5 describes the validation methods

used to validate the work. Finally, in Section 6, the

results are presented and discussed. In Section 7, ﬁ-

nal considerations and suggestions for future work are

described.

2 BACKGROUND

Tabletops have been widely adopted in various do-

mains due to their capacity to facilitate collaboration

and improve data analysis through interactive inter-

faces. They have been particularly effective in con-

texts such as education (Bortolaso et al., 2014), where

students engage in collaborative learning activities

and beneﬁt from direct manipulation of educational

content in a shared environment. In the healthcare

sector, tabletops have been utilized to enhance med-

ical diagnostics and treatment planning. The abil-

ity to display large volumes of data and interact with

medical images in real time allows healthcare profes-

sionals to analyze complex datasets more comprehen-

sively. For instance, the large surface area of tabletops

makes it possible to display high-resolution medical

images, which improves the precision of diagnostics

and supports more detailed decision-making (Yang

et al., 2014; Madni et al., 2016).

Additionally, in crisis management, tabletops

have proven invaluable for situational awareness and

decision-making during emergencies. D

oweling et al.

(2016) described their use in collaborative environ-

ments where multiple organizations, such as ﬁre de-

partments, police, and rescue teams, must coordinate

their efforts. The tabletop’s interactive interface al-

lows participants to manipulate maps, annotate essen-

tial areas, and visualize real-time data, fostering an

integrated approach to crisis resolution. This capabil-

ity signiﬁcantly enhances the ability to share critical

Enhancing Usability in Large Map Interactions: A Novel Magnifying Lenses Technique for Tabletops

449

information, develop action plans, and track resources

in dynamic, high-stress environments.

Moreover, tabletops offer an intuitive and natu-

ral interface that leverages multi-touch capabilities,

allowing multiple users to interact simultaneously.

It fosters greater participation and engagement in

group settings, especially when compared to tradi-

tional workstation-based solutions, which often limit

the number of active participants. The panoramic

view provided by tabletops not only enhances map-

related tasks but also supports the visualization of

large datasets across various domains. In health, for

example, the number of elements that can be dis-

played on the screen allows more details about med-

ical images to be seen, thus supporting better analy-

sis and collaborative interpretation (Yang et al., 2014;

Madni et al., 2016). In addition, as noted by Salvador-

Herranz et al. (2016), tabletops also contribute to im-

proving the efﬁciency and quality of teamwork, over-

coming limitations related to screen size and interac-

tion constraints found in more traditional computing

setups.

It is essential to clarify some key terms used

throughout this work to ensure a consistent under-

standing of the concepts central to our discussion:

• Interest Area: This refers to the speciﬁc area of

the display that is being magniﬁed. In the context

of the proposed technique, the interest area is rep-

resented by the smaller circle within the magniﬁer

(as illustrated in Figure 6). This area is of particu-

lar importance as it contains the detailed informa-

tion that the user wants to analyze more closely,

and the magniﬁcation is applied exclusively to this

portion of the map or dataset.

• Magniﬁed Area: Also known as the ”augmented

area” or ”magniﬁed view,” this term describes the

enlarged version of the interest area. In the pro-

posed model, the magniﬁed area is rendered adja-

cent to, and tangent with, the interest area. This

design ensures that users can easily correlate the

zoomed-in details with the corresponding location

in the broader context without losing sight of the

surrounding elements. By maintaining this prox-

imity between the magniﬁed and interest areas,

users can navigate between different levels of de-

tail more ﬂuidly, improving overall usability.

• Context: This term refers to the region surround-

ing the interest area, which remains visible but un-

altered by the magniﬁcation. In systems where

magniﬁcation techniques are employed, preserv-

ing the context is critical to maintaining spatial

orientation and understanding how the zoomed-

in details relate to the larger dataset. Without the

context, users may struggle to interpret the mag-

niﬁed information in relation to the whole, poten-

tially leading to confusion or errors in data inter-

pretation.

Currently, as outlined by Cockburn et al. (2009),

there are four primary magniﬁcation techniques com-

monly employed to facilitate information visualiza-

tion on display devices:

• Zooming: This is the technique currently imple-

mented in the simulator, where users adjust the

magniﬁcation by zooming in and out of the dis-

play. However, a limitation of this approach is

that it introduces a temporal separation between

viewing the overall context and the magniﬁed de-

tails. Users must repeatedly zoom in to examine

ﬁner details and then zoom out to regain a sense

of the larger context, creating a disjointed and less

efﬁcient workﬂow.

• Overview+Detail: In this technique, the context

and the magniﬁed region are spatially separated

on the screen. Typically, the user sees a smaller,

zoomed-in window (detail) alongside a broader

view (overview) of the entire dataset or map.

While this method provides simultaneous access

to both the magniﬁed details and the general con-

text, it requires users to mentally correlate the two

different views, which can sometimes introduce

cognitive load, particularly when switching focus

between them.

• Focus+Context: This technique aims to provide a

more integrated view by magnifying the interest

area while distorting, but not entirely obscuring,

the surrounding context. For example, a ﬁsheye

lens can enlarge the central area of interest while

maintaining a recognizable but compressed ver-

sion of the nearby context. This approach mini-

mizes the need to switch between zoomed-in and

zoomed-out views, allowing users to maintain sit-

uational awareness of the entire dataset.

• Cue: In this approach, not only the layout of el-

ements within the display but also the way they

are rendered is altered. For instance, particular

objects may be highlighted, dimmed, or ﬁltered

out entirely based on the user’s speciﬁc focus or

query. This technique allows for more dynamic

interaction with the dataset by emphasizing rele-

vant details while de-emphasizing or hiding less

critical information, thus guiding the user’s atten-

tion efﬁciently.

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450

3 RELATED WORKS

In the survey presented by Tominski et al. (2017), sev-

eral types of interactive lenses were addressed. It is

essential to notice that, according to the authors, an

interactive lens is a tool to visualize part of a dataset

and not only to magnify it. To categorize the tech-

niques, the authors adapted the taxonomy presented

by Shneiderman (2003). One type of interactive lens

the authors approach is the one designed for multi-

variate data, i.e., for data organized in dimensions.

This type of lens can be seen in Decal-Lenses by

Rocha et al. (2018), which adapts itself to follow arbi-

trary surfaces. Another addressed lens type is the one

to analyze geospatial data. Ma et al. (2020) introduce

GTMapLens, a technique to visualize texts associated

with geographical locations.

There are works that, despite not presenting inter-

active lens techniques, help to outline problems re-

lated to the traditional zoom technique and present

their solutions, like Bettio et al. (2021) that pre-

sented a novel approach to assist the user in exploring

datasets by guiding the navigation, and Dumont et al.

(2023) that uses cartographic techniques to add visual

cues to maps while the user is zooming, to reduce user

disorientation. There is also research regarding the

applicability of visualization techniques in different

conditions, e.g., multi-device environments, as pre-

sented by Liu et al. (2023).

In this paper, we are interested in magnifying

lenses and how to minimize context loss. There are

various approaches to this problem. K

aser et al.

(2011) use a bi-manual approach, where one hand

positions the magnifying lens and the other interacts

with the augmented area. Thus, context is only oc-

casionally lost when the magnifying lens is needed.

There are lenses intended for data analysis, such as

Forlines and Shen (2005), lenses that avoid the loss of

context using distortion techniques (Carpendale et al.,

2004), and even through transparency (Pietriga and

Appert, 2008). Finally, the speciﬁc problem of mag-

niﬁcation for military use on maps has been addressed

in the work of Bortolaso et al. (2013), one of the main

bases for our work.

We detail three works that were fundamental to

the development of the proposed approach, aiming to

facilitate the explanation of the design rationale.

3.1 DtLens

Aiming to allow groups to explore simultaneously

high-resolution spatial data, DtLens was created to

enable multiple augmenting lenses, which, besides

detailing data, provide tools for the user to interact

with it on the tabletop (Forlines and Shen, 2005). The

tool uses distortion to avoid context loss and to bestow

a pliable rubber surface aspect to the dataset. The tool

provides a slider to adjust the magniﬁcation (Figure

1). This feature allows the user to easily change the

magniﬁcation, even if it requires only one hand.

Figure 1: DTLens, from Forlines and Shen (2005).

3.2 FingerGlass

FingerGlass is a bimanual technique that allows the

user to deﬁne an interest area with one hand and to

interact with it using the other in a magniﬁed view

aser et al., 2011). The authors state that the user

should use the non-dominant hand (referred to by

them as ”coarse hand”) to determine this viewport and

to make the interaction using the other one referred to

by them as ”ﬁne hand”). Figure 2 exempliﬁes a right-

handed user.

This technique aims to decrease the loss of contex-

tual data by setting the position of the magniﬁed view

tangent to the interest area instead of overlapping it.

This way, the immediately adjacent area is preserved.

This tool vanishes when the user releases the coarse

hand to provide a faster interaction.

To adjust the magniﬁcation, the user can change

the distance between the coarse hand ﬁngers, which

determine the viewport or the radius of the magniﬁed

view, by dragging the borders of it with the ﬁne hand.

3.3 OrMiS

OrMiS (Orchestrating Military Simulations) is a

tabletop application designed to train military ofﬁcers

on combat and strategic maneuvers with map-based

tasks (Bortolaso et al., 2013). This tool provides

Enhancing Usability in Large Map Interactions: A Novel Magnifying Lenses Technique for Tabletops

451

Figure 2: FingerGlass, from K

aser et al. (2011).

lenses that enable users to work at different zoom lev-

els, allowing interaction with speciﬁc parts of the map

without disrupting the rest. This enhances simultane-

ous collaboration among trainees. (Figure 3).

Figure 3: Lenses of OrMiS, from Bortolaso et al. (2013).

It is essential to point out that, unlike FingerGlass,

the lens does not require the user’s interaction to be

shown. This feature enables the user to work on dif-

ferent parts of the map without closing it, which is

vital in slow-paced interactions.

The user can adjust the zoom by making a pinch

gesture and move the lens by swiping both move-

ments inside the magniﬁed area. These move-

ments are intuitive and well-established in the human-

computer interface community. However, they may

be slow to use when applied in large datasets, requir-

ing several repetitions of the pinch gesture to achieve

the maximum or the minimum zoom available and the

swipe action to move the lens expressive distances.

It is interesting to show that the authors found a

way to deal with the context loss. Using an auxil-

iary screen, they combined the focus+context tech-

nique used in the lens with the overview+detail tech-

nique. The screen shows the entire map and high-

lights the part displayed with the current global zoom

and the areas the lenses occupy. These areas are di-

vided into two parts: the interest area and the loss

context (highlighted with red) (Figure 4). Therefore,

the users can see all the information hidden by their

lenses and glimpse the magniﬁcation ratio they use.

Figure 4: Map overview screen, from Bortolaso et al.

(2013).

3.4 Conclusion

FingerGlass, DTLens, and OrMiS explore human-

computer interaction in multi-touch interfaces, with

a particular focus on zooming techniques and visual

content manipulation. While they share the goal of fa-

cilitating interaction with data on touchscreens, each

has speciﬁc features and applications. FingerGlass

proposes a virtual lens to allow the user to magnify a

speciﬁc region of the screen on a smaller screen tan-

gent to the interest area, which is ideal for mobile de-

vices such as smartphones and tablets. The multiple

virtual lenses in DTLens allow multiple users to ex-

plore different parts of a map or diagram at the same

time, facilitating collaboration on design and engi-

neering projects. The virtual lens in OrMiS is used to

focus on speciﬁc objects within a simulation, allow-

ing users to explore the details of a system or process

in a training environment.

Considering that each of the above techniques was

designed for a speciﬁc environment (all different from

the SIS-ASTROS Simulator), neither of them is suit-

able for the required type of interaction. Thus, the

adopted solution combines parts of their approaches.

4 RESEARCH METHOD

The research method consists of four phases, as

shown in Figure 5):

• Identify Tactical Simulator Requirements. In

this step, we identiﬁed the critical usability re-

quirements for a virtual tactical simulator that op-

erates on tabletops and uses large-scale maps. The

goal was to determine the essential features and

interactions needed to enhance user experience

using magnifying lenses in this speciﬁc context

(see Section 4.1).

• Propose New Approach. Based on an in-depth

analysis of related works and existing literature,

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

452

Figure 5: Research method.

we proposed new functionalities for the magnify-

ing lens technique that were tailored to address

the usability challenges identiﬁed in tactical sim-

ulation scenarios (see Section 4.2).

• Experimental Validation. We conducted a series

of experiments to validate our approach, gather-

ing both objective and subjective data as described

in Section 5. The objective analysis measured

performance improvements, while the subjective

analysis captured user perceptions through well-

established evaluation methods.

• Discussion. The results obtained were analyzed

and discussed in Section 6, aiming to consolidate

the ﬁndings and provide insights for further re-

search and practical applications in tactical sim-

ulation systems.

4.1 Identify Tactical Simulator

Requirements

The SIS-ASTROS Simulator for which we are devel-

oping this magniﬁer was designed to present two user-

interface perspectives: two-dimensional (2D) and

three-dimensional (3D). From a 2D perspective, the

users interact with a map via a tabletop display. This

touch-screen simulation tabletop, where we intend to

implement the proposed magniﬁer, allows users to in-

teract with the simulation, performing all the tasks

described in the military doctrine for launching rock-

ets. In technological terms, the digital tabletop is a

capacitive touch-screen interface with 84” 4k resolu-

tion that allows multiple touches simultaneously. The

3D perspective presents the visualization of the simu-

lated terrain, including bridge conditions and vegeta-

tion density, on a Wall-TV panel. A third component,

the instructor’s control station, allows the instructor

to conﬁgure simulation exercises through a 2D visu-

alization interface, creating an entire operational situ-

ation that the trainees must examine. All these three

elements are interconnected and synchronized.

The tasks performed in the simulator require pre-

cise control over zooming and panning while simul-

taneously maintaining awareness of the broader oper-

ational landscape. At this stage, several requirements

were deﬁned:

• Quick switching between overview and detail:

Users must be able to transition seamlessly be-

tween a complete overview of the map and de-

tailed views of speciﬁc regions. Traditional zoom-

ing methods often require repetitive zooming in

and out, which disrupts the ﬂow of analysis and

can lead to cognitive overload.

• Focus on areas of interest while preserving con-

text: It is essential to allow users to concentrate

on speciﬁc areas of interest without losing sight

of the surrounding context. Maintaining the spa-

tial relationship between the magniﬁed area and

the broader map ensures that users do not become

disoriented.

• Intuitive gesture-based controls: Users should be

able to quickly adjust the size and position of the

magnifying lens through intuitive gestures, ensur-

ing that the lens does not obscure critical informa-

tion outside the magniﬁed area. It ensures that in-

teractions are smooth and non-intrusive, enhanc-

ing usability.

This approach is designed to minimize user

fatigue, streamline interactions, and ultimately

support more efﬁcient decision-making in high-

stakes tactical scenarios.

4.2 The New Proposed Approach

The new proposed approach was designed follow-

ing Nielsen’s heuristics (Nielsen and Molich, 1990).

Other heuristic works that were also relevant to the

development - which are speciﬁc for tabletops - are

de Franceschi et al. (2020) and de Franceschi et al.

(2021), which discuss the importance of adapting us-

ability heuristics and propose new heuristics for table-

tops, respectively.

To meet the deﬁned requirements for the virtual

tactical simulator, we employed the following tech-

niques: non-vanishing technique, minimizing the loss

of context, and magniﬁcation adjustment, each de-

scribed below. The implemented magnifying lenses

can be seen in Figure 6.

Enhancing Usability in Large Map Interactions: A Novel Magnifying Lenses Technique for Tabletops

453

4.2.1 A Non-Vanishing Technique

The technique presented in this paper is meant to be

implemented in a simulator where the interactions are

slow-paced and may lead to menu opening, which re-

quires the user to use both hands. Hence, the feature

of vanishing the lens when the user interrupts his con-

tact with it presented in FingerGlass may not be ad-

equate. It was decided that the best approach in this

scenario was the one present in OrMiS and DtLens,

which allows the user to interact with other parts of

the environment without vanishing.

4.2.2 Minimizing the Loss of Context

In addressing the issue of avoiding context loss in the

area of interest, we rejected the distortion approach

due to its potential interference with usability, aligned

with Bortolaso et al. (2013), as it would impair the

visualization of symbols in the area. To minimize the

loss of context, we opted for a strategy aligned with

FingerGlass, where the enlarged area does not overlap

the area of interest but ﬁnds itself tangent to it. This

way, some contextual data will be lost, but only a tiny

part of it will be lost once the augmented area covers

only a part of the area adjacent to the interest area.

4.2.3 Magniﬁcation Adjustment

Regarding the magniﬁcation adjustment, we decided

to use the slider presented in DtLens because it en-

ables the user to change it with one hand (Figure 6).

Also, due to the use of large maps in the simula-

tor, the user needs to be able to adjust high levels of

magniﬁcation without too much effort. The features

presented by OrMiS and FingerGlass would not be

suitable because they would require, respectively, too

many pinch gestures and a considerable offset of the

hands to drag the borders of the lens.

Besides the circular slider, in line with the

overview+detail technique presented in OrMiS, a sub-

indicator that surrounds the interest area is shown in-

side the indicator (Figure 6). The sub-indicator’s ra-

dius varies according to the magniﬁcation rate, con-

tributing to the minimized loss of contextual data be-

cause there will not be any element covering the area

between the indicator - which has a constant radius -

and the sub-indicator. Moreover, it provides the user

with additional feedback on the current magniﬁcation

rate, which contributes to a more intuitive interaction.

4.2.4 Lens Movement

The lens is moved by dragging the indicator instead of

making a swipe movement inside the magniﬁed view.

This approach was chosen to avoid excessive work-

load in moving the lens throughout the tabletop once

it consists of an 84-inch screen. Independently of the

magniﬁcation and, thus, the radius of the interest area,

the indicator keeps a constant size. If it had the same

size as the interest area, with high magniﬁcation lev-

els, it would be hard for the user to move the lens

once the human ﬁnger is not as precise as a mouse

input (Albinsson and Zhai, 2003).

Figure 6: Two instances of our proposed magniﬁer. Each one has an indicator (The smaller circle) showing the amount

and where the zoom is applied, and a viewport showing the augmented area. The slider allows the user to change the zoom

amount.

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454

5 VALIDATION

We opted for an ad-hoc implementation using C# and

the Unity engine to evaluate the concepts applied in

our magniﬁer. This approach provided a more ag-

ile and efﬁcient way to test, focusing on validating

the usability of the magniﬁers without the immediate

need to integrate such features into large-scale soft-

ware.

The application consists of a section of a map rep-

resenting a region. At the beginning of the test, the

user is presented with a panel containing three ran-

dom coordinates and a coordinate picker (Figure 7).

The goal for the user is to select the requested coor-

dinates on the map. Each participant performed the

task twice, once using the traditional pan+zoom tech-

nique and once using our magniﬁers. The order of

the techniques was alternated for each participant to

avoid training bias in the results.

Figure 7: Coordinates panel.

The map has visual coordinate guides along its

edges to help users position the marker (Figure 6).

This information is lost when the participant zooms

in to achieve greater precision. Conversely, if the user

relies solely on the guides, they fail to select the coor-

dinate accurately. The magniﬁer addresses this chal-

lenge by offering both precision and context preser-

vation.

The experiment involved 20 students of Engineer-

ing and Computer Science (14 men and 6 women) be-

tween 20 and 25 years old (Figure 8), all of whom

did not have experience with tabletop environments.

They received a brief demonstration on how to use

the touch table before starting the tests. The valida-

tion of our technique was divided into two parts. The

ﬁrst is an objective analysis, evaluating user accuracy

and task completion time. The second is a subjective

analysis using the NASA-TLX questionnaire after the

test session.

Figure 8: User testing the magnifying lens on a large-scale

map.

5.1 Objective Analysis

To assess the impact of magniﬁers on task execution,

we recorded the time (T) it takes for the user to com-

plete the task with and without the use of the tool, as

well as the precision of the coordinates picked, simi-

lar to what is done in Albinsson and Zhai (2003).

5.2 Subjective Analysis

For the subjective analysis, this paper uses the NASA-

TLX questionnaire. This questionnaire evaluates

the workload of a determined task by calculating a

weighted average between the ratings (varying from

5 to 100) given by the subject to assess the impact of

six factors (Hart and Staveland, 1988). The six factors

are Mental Demand (MD), Physical Demand (PD),

Temporal Demand (TD), Performance (P), Effort (E),

and Frustration (F). The performance is assessed in-

versely: lower ratings indicate a greater signiﬁcance.

Table 1 shows the data collected during the experi-

ment. The ratings will be multiplied by 5, once the

range for the subject to pick is between 1 and 20.

In this study, the weights typically assigned by

each participant to balance the relative importance of

these factors were not collected, as the primary focus

Enhancing Usability in Large Map Interactions: A Novel Magnifying Lenses Technique for Tabletops

455

Table 1: Data collected during the experiment.

User Mode Time MD PD TD P E F Score

0 Pan+Zoom 221.75 17 7 16 5 10 13 0.9745

0 Magniﬁer 192.39 10 5 10 3 7 6 0.9639

1 Magniﬁer 104.96 10 10 15 8 10 5 0.9569

1 Pan+Zoom 97.90 5 10 5 5 5 5 0.9808

2 Pan+Zoom 246.41 15 10 10 12 15 10 0.9768

2 Magniﬁer 192.64 10 8 8 6 8 6 0.9942

3 Pan+Zoom 284.44 12 5 10 8 9 16 0.9946

3 Magniﬁer 186.22 10 8 10 12 8 13 0.9898

4 Magniﬁer 256.53 6 1 6 10 1 5 0.9946

4 Pan+Zoom 202.65 10 4 3 9 2 1 0.9811

5 Magniﬁer 185.60 8 4 5 6 5 3 0.9866

5 Pan+Zoom 103.98 15 8 8 4 10 12 0.9579

6 Pan+Zoom 207.93 5 7 11 11 2 6 0.9732

6 Magniﬁer 164.77 1 4 7 3 1 3 0.9981

7 Pan+Zoom 93.85 1 1 5 5 3 5 0.9896

7 Magniﬁer 98.53 5 2 5 5 3 2 0.9914

8 Pan+Zoom 204.15 6 7 11 6 7 6 0.7948

8 Magniﬁer 257.88 5 7 10 5 7 5 0.9007

9 Magniﬁer 253.08 15 5 18 10 10 5 0.9779

9 Pan+Zoom 259.06 16 5 12 10 10 4 0.9504

10 Pan+Zoom 276.13 14 17 13 12 11 11 0.9632

10 Magniﬁer 142.61 10 7 8 17 7 3 0.9743

11 Pan+Zoom 313.06 10 5 13 5 15 11 0.9686

11 Magniﬁer 272.33 12 7 15 18 10 8 0.9309

12 Magniﬁer 312.79 11 8 12 3 10 12 0.9741

12 Pan+Zoom 229.28 6 7 6 3 5 3 0.9577

13 Pan+Zoom 259.88 16 6 10 10 4 16 0.9310

13 Magniﬁer 219.62 12 6 12 14 7 6 0.9610

14 Pan+Zoom 204.47 5 10 5 10 15 10 0.9842

14 Magniﬁer 202.25 10 5 5 5 10 7 0.9723

15 Pan+Zoom 138.31 10 10 10 5 1 1 0.9791

15 Magniﬁer 150.41 5 10 5 5 1 1 0.9807

16 Magniﬁer 182.15 16 10 16 15 11 8 0.9849

16 Pan+Zoom 113.33 13 12 15 17 10 6 0.9901

17 Pan+Zoom 198.02 10 10 15 10 10 10 0.9739

17 Magniﬁer 139.33 5 5 5 5 5 5 0.9782

18 Pan+Zoom 152.19 7 14 15 5 13 3 0.9392

18 Magniﬁer 199.08 5 4 9 3 8 2 0.9809

19 Magniﬁer 96.66 5 8 2 18 5 5 0.9873

19 Pan+Zoom 147.17 10 5 8 15 8 2 0.9245

was on analyzing the individual scores for each cate-

gory rather than the overall weighted score.

6 RESULTS AND DISCUSSION

As previously mentioned, to avoid training bias, some

of the participants (13) began the test round using

the pan-zoom technique, while the others (7) started

with the proposed approach. Hence, the data is di-

vided into two groups for the analysis. The ﬁrst one

presents data of both the zoom and the lens of the ﬁrst

round only (Figure 11), while the latter, the second

round (Figure 12). After these individual analyses,

we present the overall results (Figure 13).

For the ﬁrst round (Figure 11), it can be seen

that the Mental Demand (MD) of the lens is slightly

higher, which is expected once it presents unfamiliar

features for the users, who are in touch with the tech-

niques around zooming, such as pinching and sliding

on a daily basis. The lens presented a signiﬁcantly

lower Physical Demand (PD) and Effort (E) because

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

456

Figure 9: Average Time to Complete the Task.

Figure 10: Average Accuracy of the Task.

it involved fewer movements to perform the task. Re-

garding the Frustration (F), it can be said that the lens

outperformed the zooming technique as well, which

indicates that, overall, the users found the lens to be

more pleasant to use. Interestingly, the subjects felt

considerable time pressure (TD) and felt that their

performance using the lens wasn’t as good as their

performance using the zooming. However, as Figures

9 and 10 show, the average time to complete the tasks

using the lens was lower and the accuracy higher, in-

dicating that the users had high expectations of their

performance using the lens.

For the second round (Figure 12), in general, the

workload contributors’ ratings decreased, which indi-

cates that there is a learning curve regarding the use

of the test environment. Another indication of this is

the fact that the Mental Demand for the lens dropped

while the zoom didn’t change much, meaning that

even a more complex tool can be easily used once the

user has little contact with the environment. The sub-

jects felt a higher frustration but still considered the

effort to be lower. They felt a more signiﬁcant time

pressure using the lens. It can be seen in Figure 9 as

Figure 11: First Round Workload Ratings.

well, which shows that the user took a more signif-

icant time to complete the tasks. Nevertheless, they

considered their performance with the lens to be bet-

ter and had higher accuracy using it.

Figure 12: Second Round Workload Ratings.

Overall (Figure 13), the lens outperformed the

zoom in every aspect but the performance, which, as

mentioned before, is probably due to high expecta-

tions of the subjects, once the average accuracy and

time to complete the tasks were better using it.

7 CONCLUSION

In the present work, we develop a new approach to

improve users’ interaction with large datasets in table-

top environments. We describe the design of a digital

magnifying lens as a tool to highlight speciﬁc areas of

interest on a map, aiming to minimize loss of context

and improve interaction efﬁciency.

The results of the usability validation analysis re-

vealed that the proposed magnifying lens helped the

Enhancing Usability in Large Map Interactions: A Novel Magnifying Lenses Technique for Tabletops

457

Figure 13: Overall Workload Ratings.

users to perform tasks involving high-precision coor-

dinate picking on a large map. Both the time needed

to ﬁnish the activity and the accuracy were better

than the traditional pan-zooming. Moreover, the users

felt more comfortable using the lens according to the

NASA-TLX questionnaire results, in which the lens

outperforms the zooming in all aspects except perfor-

mance. As mentioned, this fact is probably due to the

high expectations the subjects had with the lens once

their performance using it was better in terms of the

average accuracy and time to complete the tasks.

Given the positive result, implementing our tech-

nique over the traditional pan-zooming technique is

worth it. Although the tests were conducted on a large

tabletop for a military simulation project, it is worth

noting that this technique can be used on other plat-

forms, improving interactions in various types of ap-

plications, simulation environments, or serious games

involving data analysis, interaction with maps, secu-

rity, and surveillance, among others.

For future work, it is recommended that the mag-

nifying lens be implemented in practical environ-

ments to evaluate its effectiveness in real contexts of

use.

ACKNOWLEDGEMENTS

We thank the Brazilian Army and its Army Strategic

Program ASTROS for the ﬁnancial support through

the SIS-ASTROS GMF project (898347/2020).

REFERENCES

Albinsson, P.-A. and Zhai, S. (2003). High precision touch

screen interaction. In Proceedings of the SIGCHI

Conference on Human Factors in Computing Systems,

CHI ’03, page 105–112, New York, NY, USA. Asso-

ciation for Computing Machinery.

Bettio, F., Ahsan, M., Marton, F., and Gobbetti, E. (2021).

A novel approach for exploring annotated data with

interactive lenses. In Computer Graphics Forum, vol-

ume 40, pages 387–398. Wiley Online Library.

Bortolaso, C., Oskamp, M., Graham, T. N., and Brown, D.

(2013). Ormis: a tabletop interface for simulation-

based training. In Proceedings of the 2013 ACM Inter-

national Conference on Interactive Tabletops and Sur-

faces, ITS ’13, page 145–154, New York, NY, USA.

Association for Computing Machinery.

Bortolaso, C., Oskamp, M., Phillips, G., Gutwin, C., and

Graham, T. N. (2014). The effect of view techniques

on collaboration and awareness in tabletop map-based

tasks. In Proceedings of the Ninth ACM Interna-

tional Conference on Interactive Tabletops and Sur-

faces, pages 79–88.

Carpendale, S., Light, J., and Pattison, E. (2004). Achiev-

ing higher magniﬁcation in context. In Proceedings

of the 17th Annual ACM Symposium on User Inter-

face Software and Technology, UIST ’04, page 71–80,

New York, NY, USA. Association for Computing Ma-

chinery.

Cockburn, A., Karlson, A., and Bederson, B. B. (2009).

A review of overview+ detail, zooming, and focus+

context interfaces. ACM Computing Surveys (CSUR),

41(1):1–31.

de Franceschi, V. D., Fontoura, L. M., and Silva, M. A. R.

(2020). Usability heuristics for tabletop systems de-

sign. In ICEIS (2), pages 555–562.

de Franceschi, V. D., Fontoura, L. M., and Silva, M. A. R.

(2021). Tabletop interface design: Developing and

using usability heuristics. In Filipe, J.,

Smiałek, M.,

Brodsky, A., and Hammoudi, S., editors, Enterprise

Information Systems, pages 708–729, Cham. Springer

International Publishing.

oweling, S., Tahiri, T., Riemann, J., and M

uhlh

auser,

M. (2016). Collaborative interaction with geospatial

data—a comparison of paper maps, desktop gis and

interactive tabletops. Collaboration Meets Interactive

Spaces, pages 319–348.

Dumont, M., Touya, G., and Duch

ene, C. (2023). More is

less–adding zoom levels in multi-scale maps to reduce

the need for zooming interactions. Journal of Spatial

Information Science, (27):93–124.

Fitzmaurice, G. W. and Buxton, W. (1997). An empirical

evaluation of graspable user interfaces: towards spe-

cialized, space-multiplexed input. In Proceedings of

the ACM SIGCHI Conference on Human factors in

computing systems, pages 43–50.

Forlines, C. and Shen, C. (2005). Dtlens: multi-user table-

top spatial data exploration. In Proceedings of the 18th

Annual ACM Symposium on User Interface Software

and Technology, UIST ’05, page 119–122, New York,

NY, USA. Association for Computing Machinery.

Hart, S. G. and Staveland, L. E. (1988). Development of

nasa-tlx (task load index): Results of empirical and

theoretical research. In Hancock, P. A. and Meshkati,

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

458

N., editors, Human Mental Workload, volume 52

of Advances in Psychology, pages 139–183. North-

Holland.

aser, D. P., Agrawala, M., and Pauly, M. (2011). Finger-

glass: efﬁcient multiscale interaction on multitouch

screens. In Proceedings of the SIGCHI Conference

on Human Factors in Computing Systems, CHI ’11,

page 1601–1610, New York, NY, USA. Association

for Computing Machinery.

Kubicki, S., Lebrun, Y., Lepreux, S., Adam, E., Kolski, C.,

and Mandiau, R. (2013). Simulation in contexts in-

volving an interactive table and tangible objects. Sim-

ulation Modelling Practice and Theory, 31:116–131.

Liu, Y., Zhang, Z., Pan, Y., Li, Y., Liang, H.-N., Craig, P.,

and Yu, L. (2023). A study of zooming, interactive

lenses and overview+ detail techniques in collabora-

tive map-based tasks. In 2023 IEEE 16th Paciﬁc Visu-

alization Symposium (PaciﬁcVis), pages 11–20. IEEE.

Ma, C., Zhao, Y., AL-Dohuki, S., Yang, J., Ye, X., Kamw,

F., and Amiruzzaman, M. (2020). Gtmaplens: In-

teractive lens for geo-text data browsing on map. In

Computer Graphics Forum, volume 39, pages 469–

481. Wiley Online Library.

Madni, T. M., Nayan, Y. B., Sulaiman, S., Abro, A., and

Tahir, M. (2016). Usability evaluation of orientation

techniques for medical image analysis using table-

top system. In 2016 3rd International Conference

on Computer and Information Sciences (ICCOINS),

pages 477–482.

Mateescu, M., Pimmer, C., Zahn, C., Klinkhammer, D., and

Reiterer, H. (2021). Collaboration on large interactive

displays: a systematic review. Human–Computer In-

teraction, 36(3):243–277.

Nielsen, J. and Molich, R. (1990). Heuristic evaluation of

user interfaces. In Proceedings of the SIGCHI Confer-

ence on Human Factors in Computing Systems, CHI

’90, page 249–256, New York, NY, USA. Association

for Computing Machinery.

Pietriga, E. and Appert, C. (2008). Sigma lenses: focus-

context transitions combining space, time and translu-

cence. In Proceedings of the SIGCHI Conference

on Human Factors in Computing Systems, CHI ’08,

page 1343–1352, New York, NY, USA. Association

for Computing Machinery.

Rocha, A., Silva, J. D., Alim, U. R., Carpendale, S.,

and Sousa, M. C. (2018). Decal-lenses: Interactive

lenses on surfaces for multivariate visualization. IEEE

transactions on visualization and computer graphics,

25(8):2568–2582.

Salvador-Herranz, G., Camba, J. D., Naya, F., and Con-

tero, M. (2016). On the integration of tangible ele-

ments with multi-touch surfaces for the collaborative

creation of concept maps. In Learning and Collabo-

ration Technologies: Third International Conference,

LCT 2016, Held as Part of HCI International 2016,

Toronto, ON, Canada, July 17-22, 2016, Proceedings

3, pages 177–186. Springer.

Shneiderman, B. (2003). The eyes have it: A task by data

type taxonomy for information visualizations. In The

craft of information visualization, pages 364–371. El-

sevier.

Tominski, C., Gladisch, S., Kister, U., Dachselt, R., and

Schumann, H. (2017). Interactive lenses for visual-

ization: An extended survey. In Computer Graphics

Forum, volume 36, pages 173–200. Wiley Online Li-

brary.

Westendorf, L., Shaer, O., Varsanyi, P., Meulen, H. V. D.,

and Kun, A. (2017). Understanding collaborative de-

cision making around a large-scale interactive table-

top. Proceedings of the ACM on Human-Computer

Interaction, 1:1 – 21.

Yang, Q., Liu, J., Qin, Y., Yu, C., Yuan, Q., and Shi, Y.

(2014). Studying accessible states of user interfaces

on tabletops. In 2014 IEEE 11th Intl Conf on Ubiqui-

tous Intelligence and Computing and 2014 IEEE 11th

Intl Conf on Autonomic and Trusted Computing and

2014 IEEE 14th Intl Conf on Scalable Computing and

Communications and Its Associated Workshops, pages

402–405.

Enhancing Usability in Large Map Interactions: A Novel Magnifying Lenses Technique for Tabletops

459