Visualizing Indoor Activity: Prototyping a Digital Interface for Location-based Indoor Positioning

Visualizing Indoor Activity Prototyping a Digital Interface for Location-based Indoor Positioning

Master’s thesis by João Ramos hello@joaoramos.org

Submitted in partial fulfillment of the requirements for the degree Master in Science in Multimedia Communication from Universidade de Aveiro in an Erasmus agreement with the IT University of Copenhagen.

Copenhagen, May 2011

Thesis supervision Prof. Dr. John Paulin

“Knowledge makes everything simpler.”

John Maeda

Abstract This work addresses a digital solution to support decision-making and situation awareness in indoor environments, through locationbased positioning of relative human density magnitudes in confined spaces. The solution, VIA (Visualizing Indoor Activity), is a visual prototype based on the converging theoretical grounds of Locationbased services, Information Visualization and Simplicity and is meant to assist facility and services management tasks.

Acknowledgements I am grateful to my supervisor, John Paulin Hansen, for his guideance and attention. Many thanks to my professor, Rui Raposo, for his understanding and support.

A special thanks to Fabienne, who kindly stood by my side and encouraged me during countless days of work. I’m thankful to my family — particularly to Marco, my brother — and friends for their support and to my students and clients, for enriching my Erasmus experience in Copenhagen.

I am also grateful for Maarten, Sam, Jonatas and Jack for proofreading this document. A special acknowledgment to Catherine DaveyStovin (Damarel) for her expert feedback on my work and for Michael, Dina and Stanley for their participation on the inquiries.

Content

Introduction

1

Target audience

4

Motivation

4

Research method in brief

5

Structure of the report

5

Related literature and theoretical focus 2.1

7

Location-based services

7

2.1.1

Indoor positioning

7

2.1.2

Privacy

9

2.1.3

Challenges in LBS

10

2.2 Information Visualization

11

2.2.1

Historical overview

11

2.2.2

Definition

12

2.2.3

2.2.2.1

From Data to Wisdom

12

2.2.2.2

Visualization subsets

14

2.2.2.3

Visualization methods

15

2.2.2.4

The Understanding Process

16

Cognition

17

2.2.3.1 2.2.4

2.2.5

Visual Taxonomies

Design Patterns

18 20

2.2.4.1

Display Patterns

20

2.2.4.2

Behavior Patterns

23

2.2.4.3

Interaction Patterns

25

Key attributes

26

2.3 Simplicity

28

2.3.1

The ten laws

29

2.3.2

Why it matters

35

2.4

Discussion

35

Methodology

37

3.1

Explaining the methods

38

3.2

User inquiries

40

3.2.1

Participants

40

3.2.2

Material

41

3.2.3

Listing the tasks

41

3.2.4

Interviewing

43

3.2.5

Procedure

44

Results and discussion 4.1 Visual prototype: Pre-alpha 4.1.1

Basics and decisions on design

46 46 50

4.1.2

User interaction

57

4.2 Results from the inquiries

59

4.2.1

Usability tests 4.2.1.1

4.2.2

Considerations

Qualitative interviews 4.2.2.1

Considerations

4.3 Visual prototype: Alpha 4.3.1

Reflections

59 60 64 65 68 70

Conclusion

73

References

76

Appendix

80

1 Introduction

Chapter 1

Introduction

Area of research The three intersecting areas of research upon which this study focuses on are Location-based services, Information Visualization and Simplicity. They address the location awareness enhanced by Visualization methods and design patterns and the challenges of design through simplicity. This research also addresses privacy-sensitive issues and cognitive processes related to perception and attention.

State of the art There has been a growing number of projects and applications addressing location-based visualizations and immersive data in computer-supported interfaces. For instance, Nino Cometti and PICTURE 1

Christoph Brandin’s Light Control in Complex Spaces 1 (PICTURE 1) is a digital interface that allows a user to navigate through a location-aware visualization of a specific facility by turning its lights on and off or even ad1

For more information, see http://vimeo.com/3399961.

Visualizing Indoors Activity

Introduction 2

justing their intensity. This Swiss digital interface was groundbreaking in its clutter-free aesthetics and clever pursuit for locationaware representations and controls. Another inspirational example is Refugee Finder 2 , from Elena Gianni, Jesper Svenning and Pedro Andrade. It is another remarkable digital interface using locationaware visualization techniques used to successfully represent density through animation. A strong example of a location-based positioning system is Cisco’s Wireless Control System 3 , a platform to manage indoor Wi-fi networks that is notable for its device-centric tracking and monitoring. More targeted competitor solutions are described in Chapter 2 — Related literature and theoretical focus.

All these examples share preoccupations related to location-based data and visualization in computerized interfaces, but they don’t seem to address problem solving based on indiscriminate positioning technology. Instead, they are built for specific positioning technologies, such as Bluetooth, wireless telephony infrastructures (i.e.

GSM), FM transmission, pressure sensors, etc.

Research problem Technologically enhanced mobile devices have flourished in the past few years, allowing a variety of check-in and geo tagging crossplatform applications to settle, like Foursquare 4 , Gowalla 5

and

many others. Similar systems were applied to highways 6 and shopping centers 7 to electronically track consumer patterns and behaviors, in order to accomplish relevant tasks. Hence, in large and high-

For more information, see http://vimeo.com/13428106.

For more information, see http://bit.ly/ciscowcs 4 For more information, see http://foursquare.com/ 5 For more information, see http://gowalla.com/ 6 For more information, see http://viaverde.pt 7 For more information, see http://bit.ly/rfidtechnology 2 3

Visualizing Indoors Activity

3 Introduction

density environments it became clear that for whatever security, productivity or marketing reasons, it would be remarkably relevant to track people’s movements and actions in indoor environments.

This challenge can be pursued through Visualization, a tool to support sense-making and situation awareness — it has often been used to enhance the understanding of complex datasets and environments. Its utilitarian application is a crucial concern when dealing with massive quantities of data being collected by elaborated environments with high-density human dynamics as in airports, hospitals or even universities. Those indoor facilities can take advantage of a way to track and visualize their customers and spaces towards a more adequate, optimized and unified service concerning security, satisfaction and effectiveness. That could be an effective way to foresee dwell time in public services, to identify consumer hotspots in shopping centers, to spot high-density areas in auditoriums, or even predict potential disputes between enthusiasts in sports stadiums, etc.

With this in mind, this project will strive to answer the following question: “How to best support facility management in their decision-making tasks through a digital interface that can inform them on the movements of people and objects in indoor environments?" and inspire awareness amongst indoor tracking and visualization.

The objective of the thesis First and foremost, this thesis fulfills its primary purpose by reporting the prototyping of a solution to the before mentioned research problem.

Visualizing Indoors Activity

Introduction 4

This thesis is expected to inspire further approaches to provide facility and service management a location awareness monitoring tool.

In the end, both the documentation and the prototype serve as a framework for future indoor location-based services, tracking solutions and related R&D projects.

Target audience This thesis is intended for academics and visual designers within the fields of Location-based services and Visualization. It is also directed for facility and services managers and operators.

Motivation My background education in Communication Design and Online Media inspired my fascination for visual strategies to enhance, support and even complement location awareness. Pursuing the chimera of visual thinking took me to the endeavoring world of Information Visualization through Simplicity and allowed me to embrace this exciting project at the IT University of Copenhagen. I often recall John Maeda’s response to one of my emails, on which I questioned him about the future of Visualization as a decision-making support tool: “Absolutely vital to our future because we are overrun with so much data that nobody knows how to make actionable. We are in a golden age for artists and designers who choose to help make sense or context of the chaos in which we are now immersed.�

Visualizing Indoors Activity

5 Introduction

This is both my motivation and my motto.

Research method in brief The first stage consisted on brainstorming, planning and sketching the interface and information architecture, according to what was expected to fulfill facility management tasks and support overall situation awareness. It was followed by a pre-inquiries prototype design stage using relevant design patterns and pre-attentive variables which was targeted for multiple devices and different viewports.

This design phase was supported by John Maeda’s vision of Simplicity.

The second stage consisted of conducting a series of inquiries to reveal usability issues, user emotional responsiveness and targeted feedback. The results and critical analysis of these findings served as guidelines for the alpha (post-inquiries) stage of the visual prototype — proposed as solution for the research question.

A more descriptive methodology is presented in Chapter 3 — Methodology.

Structure of the report Chapter 1: Introduction — A summarized walk-through on the areas of interest, inspiration, research problem and objectives of this project, followed by the target group specification, personal motivation and a brief preview of the research method.

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Introduction 6

Chapter 2: Related literature and theoretical focus — An introduction to Location-based services and indoor positioning systems, followed by a structured and detailed walk-through on the main areas of research. Is finishes with a discussion relating the areas of research and their relevance and applicability for the research project in hand.

Chapter 3: Methodology — An extensive explanation of how this research project was conducted, describing not only the methods but also the procedures employed during the inquiries.

Chapter 4: Results and discussion — Presentation of the pre-alpha (pre-inquiries) and alpha (post-inquiries) visual prototypes and critical discussion of the findings and results obtained from the usability tests and qualitative interviews.

Chapter 5: Conclusion — An overview of this research project, raising further opportunities in both research and development.

References — An extensive and ordered list of all the authors and their publications cited across this document.

Appendix — Supplements and attachments containing the documents that supported the inquiries and both versions of the visual prototype. The listed contents are included in the attached CD-ROM.

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7 Related literature and theoretical focus

Chapter 2

Related literature and theoretical focus

2.1 Location-based services Location-based services (LBS) are information services accessible through mobile devices that take advantage of the geographical positioning of the device. One of the various contexts of LBS is indoor tracking, where mobile devices with network access and positioning technologies (Raper, Gartner, Karimi, & Rizos, 2007) such as smart phones, laptops or Radio Frequency Identification (RFID) tags are used to map the relative positioning of assets within a certain facility. There is a variety of technologies used for location-based tracking, such as Bluetooth, Wi-fi, GPS (Global Positioning System), RFID, etc.

2.1.1 Indoor positioning Unlike those with global coverage (e.g. GPS positioning), local positioning systems (LPS) are alternative positioning technologies that process location-sensitive data within a variety of LBS applications, like the indoor positioning systems (IPS). IPS are networks of de-

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Related literature and theoretical focus 8

vices used to monitor the relative positioning of people or assets inside a building, providing environmental context and raising location awareness. While global navigation satellite systems take advantage of GPS technology (supported by most of the recent smart phones) for accurate outdoor positioning, IPS have to surpass significant signal deterioration caused by construction materials. One of the ways to do it is by reusing any available wireless indoor infrastructures, like WLAN (Wireless Local Area Network), RFID, UWB (Ultra Wideband), Bluetooth, etc. Studies have been conducted to find new techniques that can enhance accuracy in indoor positioning systems without compromising the company’s budget. A solid example is the robust WLAN-based positioning technique proposed by Chang, Rashidzadeh, & Ahmadi (2010) that uses differential Wi-fi access points to reduce noise generated by environmental factors and dynamics. There are several indoor positioning tools on the market, each one with its own characteristics depending on the positioning technology and environment-

PICTURE 2

specific factors. One of the many available commercial solutions is Ekahau’s Real Time Locating System Controller (PICTURE 2), ERC 8 , a monitoring system that captures any Wi-fi emission devices and maps them on Ekahau’s Site Survey, the analysis and reporting tool that is shipped with the product. Another example is Cisco’s Wireless Control System (PICTURE 3), WCS 9 , a managing platform that enables IT administrators to plan, deploy, monitor, troubleshoot and report on indoor wireless

PICTURE 3

networks by setting up the points of

8 9

For more information, see http://bit.ly/ekahau For more information, see http://bit.ly/ciscowcs

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9 Related literature and theoretical focus

access on the WCS’s interface. Both solutions are Wi-fi locationbased positioning systems for indoor environments. An example of a Bluetooth-based IPS is Zonith’s Bluetooth Indoor Positioning Module (PICTURE 4) 10 , a monitoring system that tracks Bluetooth-enabled devices through strategically positioned Bluetooth beacons. The accuracy of the assets’ positioning is set by the level of granularity (i.e.

PICTURE 4

density of Bluetooth beacons).

2.1.2 Privacy Plotting sensitive information about individuals in indoor positioning might trigger privacy issues and induce anxiety on those users who do not want to share data about their current location. Many researchers have studied ways to avoid violating the users’ privacy.

While a group of researchers proposed the use of “dummy data” to blur accurate positioning (Kido, Yanagisawa, & Satoh, 2005), others suggested a model of information “obfuscation” through global or local refinement (Duckham, Kulik, & Birtley, 2006), and others even proposed a strong access control enforcement on the operation of sensitive data exchange (Youssef, Atluri, & Adam, 2005). Indeed, these models are paving the way for privacy-safe indoor positioning, as further research revealed that users start to concern less about privacy if they found the LBS attractive and protective enough (Barkhuus, 2004). When evaluating the Gatecaller service, a LBS that sends targeted information to airport customers about boarding and departure, Hansen, Alapetite, Andersen, Malmborg, & Thommesen (2009) observed that only a small percentage of subjects felt them10

For more information, see http://bit.ly/zonith

Visualizing Indoors Activity

Related literature and theoretical focus 10

selves monitored. This is another indicator that reveals the increasing acceptance of LBS among users in indoor spaces.

2.1.3 Challenges in LBS As location-based services become increasingly popular, new market niches begin to settle. One of which is location-based marketing 11 , a new kind of service targeted for mobile devices that handles proximity-driven strategies, location-based SMS campaigns, and business intelligence, etc. Another challenge in LBS are local searches 12 , through which users can search and browser local points of interest like the closest coffee shop, local irish pubs, the biggest shopping center on the block, etc. Other challenges in-

PICTURE 5

clude location-based social network campaigns and promotions 13 made on top of social LBS like Gowalla, Foursquare, etc. Indoor navigation systems14 also seem to be a blossoming challenge for LBS, inspired by a growing number of mobile applications for indoor navigation like Nokia’s Indoor Navigation (PICTURE 5), the FastMall app, Insiteo15, etc.

The solution addressed by this work meets the purposes of these last examples of indoor navigation-driven LBS. These solutions share the same goal: providing location awareness to their users. To achieve such aspiration, they provide users a tool to assist the understanding of context and spatial awareness (e.g PICTURE 5 illustrates a sugFor more information, see http://georillas.com For more information, see http://www.decarta.com 13 For more information, see http://geotoko.com/ 14 For more information, see http://bit.ly/indoorns 15 For more information, see http://www.insiteo.com 11

12

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11 Related literature and theoretical focus

gested path within an airport terminal). That tool is Information Visualization, as described in the following topic.

2.2 Information Visualization 2.2.1 Historical overview Since the very beginning of time humans have been trying to graphically represent their actions and vicissitudes through symbology, wall paintings, and drawings on papyrus and stone. These phenomena often told stories and narratives, becoming the very foundation of latter visual representations and leading to the first meaningful and novel visualizations. Some of its most inevitable and historical landmarks include John Snow’s 1854 illustration of cholera outbreaks 16 , relating an inspiring and analytical mapping of water pumps (crosses) and cholera deaths (circles); Harry Beck’s extraordinary map of London Underground (PICTURE 6) 17 , influenced by electrical circuits conventions and based on logiPICTURE 6

cal relationships (between subway stations); and Minard’s 1869 March of Napoleon on Moscow 18 , an information graphic that illustrates Napoleon’s army losses during their cataclysmic russian campaign in 1812, featuring related multidimensional variables such as space, time, direction, size, and temperature — later proclaimed as the best statistical graphic ever drawn (Tufte, 1983).

For more information, see http://en.wikipedia.org/wiki/John_Snow_(physician) For more information, see http://en.wikipedia.org/wiki/Tube_map 18 For more information, see http://en.wikipedia.org/wiki/Charles_Minard 16 17

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Related literature and theoretical focus 12

2.2.2 Definition Information Visualization is a powerful solution to support understanding. It was largely boosted by the adoption of PCs by designers, APIs, and high level programming languages (Manovich, 2010), giving visualization designers the opportunity to easily manipulate large datasets into new, interactive, digitally-supported, and often dynamic formats. Also known as Infovis, it is described as the use of digital tools to amplify cognition (Shneiderman, Card, & Mackinlay, 1999) and assists the understanding of shapeless, unsightly, and unformatted data as meaningful pictures. David McCandless envisions Information Visualization as “a form of knowledge compression, a way of squeezing an enormous amount of information and understanding into a small space” (McCandless, 2010). Unsurprisingly, Infovis is now emerging as a medium, with it’s very own expressive potential (Rodenbeck, 2008), and is ready to be a mass medium in its own right (Viégas, 2010), with an established role in big mass media players like the New York Times or The Guardian, and also through famous research groups like the Stanford Visualization Group. In a nutshell, a brief glimpse over previous definitions and expert insights on Infovis simply boils down to the phenomena of interactive, graphical presentation of data to support understanding.

2.2.2.1

From Data to Wisdom

With so much data being generated to feed the so far established Information Era (e.g. Google handles more than 20 petabytes of data per day 19 ), Shedroff (1999) formulated an understanding process, the Continuum of Understanding, a line connecting the dots between data and wisdom. It is also known as the DIKW (acronym for Data,

19

Fore more information, see http://bit.ly/google20PB

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13 Related literature and theoretical focus

Information, Knowledge, and Wisdom) continuum.

Data “Data is simple facts, lacking any context” (Dürsteler, 2007) and doesn’t have the ability to inform or provide any kind of meaning at all. If we look at a data string like 31031987, it may doubtfully represent random numbers, an anniversary, a product code, a password, etc. That’s because it’s a group of unordered, out of context, observational mensurations. In Information Visualization, data is often referred as abstract data, since it doesn’t attach context or proper structure.

Information Information, as the next understanding level, brings context to data.

It is the meaningful product in which data transforms into. As Lev Manovich envisioned in The Shape of Information (Manovich, 2005), the term information contains the word form within it, which may somehow suggest that information builds a context out from the data, generating meaning, idea, information. It’s a structured construction of data into form, also metaphorized by Dürsteler as “distillation of data” (Dürsteler, 2007), by providing relationships and patterns between data objects.

Knowledge When information is integrated with experience, it generates knowledge (Mazza, 2009). Knowledge is what people learn and acquire in interpersonal relationships and experiences, through communication and interaction. That makes it a crucial participatory level of communication (Shedroff, 1999), unlike previous stages of the DIKW continuum.

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Related literature and theoretical focus 14

Wisdom Wisdom is the ability to apply knowledge. It’s the ultimate level of understanding and comprehension for its abstract, intimate, and vague nature. Unlike data and information, it cannot be created — nor shared like knowledge — and must be acquired by oneself.

It is important to note two important contrasting gradients happening through the DIKW span: as the continuum evolves from Data to Wisdom, the level of context decreases at the same rhythm the level of abstraction increases, in opposite directions. With this in mind, it becomes apparent that observational and statistical data lacks of any kind of abstraction, since it’s pure functional and concrete data that is involved in a properly defined context. On the other hand, the experience and knowledge convergence, as the ultimate level of understanding, is defined by a high stage of abstraction that lacks of any context, as Wisdom can be applied to pretty much any context.

2.2.2.2

Visualization subsets

According to a recent systematization of the non-artistic Visualization subsets (Masud, Valsecchi, Ciuccarelli, Ricci, & Caviglia, 2010), there are three main intersecting categories. They are not mutually exclusive, as described below.

Data Visualization, generalized as graphical representation of data.

It may be static or interactive and covers Information Visualization.

Information Visualization, as a computer supported form of interactive Data Visualization, as previously described in point 2.2.2 — Definition.

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15 Related literature and theoretical focus

Scientific Visualization, as a form os spatially-mapped Data Visualization, as seen on maps and body tomographies.

Knowledge Visualization, by transcending typical Infovis data-centric purposes with the goal of inspiring and motivating action.

Information Aesthetics, by exploring typical visualization techniques for atypical purposes (Lau & Moere, 2007) other than augmenting the understanding of data.

Information Graphics, as the subset of non-interactive visualizations. This subset includes newspaper infographics (short term for information graphics).

2.2.2.3

Visualization methods

Each dataset has its own anatomy, its specific shape and its very own narrative potential, making its representation dependent on certain existing visualization methods — or inspiring visualization designers to create their own representations. These methods are frameworks to reach the unique expressiveness in which these datasets translate and transform into.

Useful examples of visualization methods • Metro map: a simplified mapping of links and relations, hiding the complexity of a geographically-accurate map. It supplies the users

Visualizing Indoors Activity

Related literature and theoretical focus 16

with a sequential and relational representation of metro stations.

• Treemap (PICTURE 7) 20 : supplies the user with a nested overview on the data, mapping objects according to certain attribPICTURE 7

utes (e.g. relevance, color, size, etc).

• Datamap: just like in a Treemap, a Datamap provides the user with a simplified insight on the data. Though, unlike the former, it geographically maps the dataset over a map.

2.2.2.4

The Understanding Process

Behind every data visualization there’s an understanding process that starts by getting the data and ends by adding interactivity to it.

Ben Fry once stated that every data understanding process starts with a set of numbers and a question (Fry, 2007), consequently leading the user to an answer. That’s the very basic setup of any visualization and is constituted by the seven steps listed below: • Acquire: obtaining the data; • Parse: ordering the data; • Filter: removing data noise; • Mine: mapping the data as a way to discern patterns; • Represent: through a basic visual model, using any visualization methods;

20

For more information, see http://newsmap.jp/

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17 Related literature and theoretical focus

• Refine: improve the basic visual model; • Interact: add interactivity and data manipulation; In a similar revision, Colin Ware dissects the data visualization process with the following four stages (Ware, 2004, p. 5): “The collection and storage of data itself”.

“The preprocessing designed to transform the data into something we can understand”.

“The display hardware and the graphics algorithms that produce an image on the screen”.

“The human perceptual and cognitive system (the perceiver)”.

A close analysis over both processes reveals an apparent and consistent overlapping. It is clear that both Ben Fry’s seven steps and Colin Ware’s visualization stages constitute a joint perspective on how visualizations are designed and captured by end-users.

2.2.3 Cognition Understanding the mechanism of visual stimulation in Information Visualization is a very important aspect to be taken in consideration when designing the solution. According to Colin Ware, there are two main symbol categories — elemental representations of reality — as shown below: Arbitrary: symbols with no perceptual basis that require previous learning, as they are social constructions of a certain subject. As an example, the word book has no per-

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Related literature and theoretical focus 18

ceptual relationship with the actual object it represents.

Therefore, it is an arbitrary, culture-driven symbol.

Sensory: symbols with the ability to use the perceptual processing mechanism of the human brain, requiring no previous learning at all. Unlike arbitrary symbols, they tend to be stable across individuals and require no previous learning.

However, there are no purely based arbitrary or sensory symbols, as they inherit at least a small percentage of a certain antagonist category. Any ideal communication product would potentially be designed only by using sensory symbols, as they require no learning or social construction whatsoever (e.g. users would spend no time learning how to handle it or how to react to unexpected situations).

Nevertheless, arbitrary symbols are required to represent cultural concepts, like navigational patterns, menus, help monitors, and so on.

2.2.3.1

Visual Taxonomies

In pre-conscious perception, it’s possible to highlight elements in a certain visual setup. By shifting certain visual properties or attributes of a given symbol, it is indeed possible to immediately depict change or modification in contrast to its context. This happens because pre-conscious perception occurs before any conscious insight is triggered and so immediate attention is given to certain symbol. In Information Visualization, this visual and perceptive strategy is remarkably useful, as Visualization designers often tend to delegate top-priority information to this perception stage. According to Jenifer Tidwell, there are just about eight visual variables, known as Gestalt pre-attentive variables (PICTURE 8), that enable pre-conscious

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19 Related literature and theoretical focus

perception in end-users.

PICTURE 8

Tidwell pointed out some of the Gestalt grouping and alignment techniques (PICTURE 9) as layout strategies to enhance and improve UI (User Interface) perception, as required measures to form a clear visual hierarchy and maintain visual flow (Tidwell, 2005).

PICTURE 9

Proximity: by grouping elements closer, users will associate them with one another.

Similarity: if two elements have similar visual properties (like shape, color, size, orientation), users will associate them with each other.

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Related literature and theoretical focus 20

Continuity: by aligning smaller elements, users will perceive continuous lines and curves and will try to “fill in the blank” .

Closure: users will tend to visualize closed forms and groups of elements that aren’t explicitly closed.

2.2.4 Design Patterns Design patterns are reusable formulas to usual and iterative problems — they are frameworks to solve certain recurrent issues that happen in many situations. Just like in architecture or software design, these patterns are applications of “reusable components to fulfill recurring tasks” (Behrens, 2008, p. 21). However, they’re not manuals nor style guides. For the purpose of this research, Visualization-oriented design patterns are helpful for Infovis designers and enthusiasts design and develop their visualizations and to address certain aspects of the before-mentioned research problem. With that in mind, Christian Behrens recently systematized these design patterns into three main categories (TABLES A, B, AND C) with relevant instructions, data specifications, and graph details. For the purposes of this research, only a descriptive overview will be made, assisted by examples taken from javascript-based visualization toolkits such as Protovis 21 and G.Raphaël 22 , and from Gapminder World 23, an online interactive visualization tool.

2.2.4.1

Display Patterns

These design patterns are visual display solutions, often related to the way how data and information are shown in order to provide the For more information, see http://vis.stanford.edu/protovis/ For more information, see http://g.raphaeljs.com/ 23 For more information, see http://www.gapminder.org/world 21

22

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21 Related literature and theoretical focus

user with new understanding. Its sub-genres are described below (TABLE A), and must not be taken as mutually exclusive or being part of an exhaustive list.

Correlations: relation between two or more quantitative datasets in multidimensional axes. Scatterplots and bubble charts are two correlation solutions, as representations drawn in a Cartesian coordinate system. Although scatterplots represent uniform unidentified elements of data, bubble charts feature properly tagged and identifiable elements. The shown example is a Gapminder World bubble chart.

TABLE A

Continuous Quantities: the display of the development of quantitative values over a certain interval (Behrens, 2008). Simple and multi-set line charts, stacked area charts, stream graphs, and sparklines are examples of continuous quantity solutions. The shown example is a featured Protovis stream graph.

Discrete Quantities: visualization of absolute magnitudes of nominal data items and comparison of their quantitative values (Behrens, 2008).

As examples of this solution, there are simple, multi-set, stacked and isometric bar charts, dot matrixes, and also span charts. A G.RaphaĂŤl bar graph serves as example.

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Related literature and theoretical focus 22

Proportions: the display of the relative magnitude of several quantitative values that allow comparisons between magnitude segments (Behrens, 2008). Simple pie charts and ring charts are examples of proportion solutions. The shown example is a featured G.Raphaël interactive pie chart.

Flows: expression of isolated systems by means of their input and output flows and identification of the proportional magnitudes of the single flows (Behrens, 2008).

Sankey diagrams and thread arcs (like Behrens’ solution shown on the left) are examples of flow solutions.

Hierarchies: representation of hierarchical structures and relations within the dataset. As examples or hierarchical solutions, there are tree diagrams and tree maps, like David McCandless’ Billion Dollar-o-Gram, shown as example.

Networks: expression of abstract representations of networks. As network example solutions, there are diagram maps (like the before mentioned Metro Map), the relation circle and the pearl necklet. The shown example is a featured Protovis force directed layout.

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23 Related literature and theoretical focus

Spatial Configurations: mined space-related representations on a map. As spatial configuration example solutions, there are both topographic and thematic maps. The shown example is a featured Stamen Design’s Crime-spotting Project.

2.2.4.2

Behavior Patterns

Design patterns related to visualization manipulation by the user. Its sub-genres are described below (TABLE B).

Navigation: basic control behaviors as well as strategies to present data as navigable structure (Behrens, 2008). They include simple zooming (e.g. zooming on Google Earth); local zooming (e.g. accessibility magnifying lenses); panning (e.g. panning on Google Maps); timeline (e.g.

Gapminder World navigation); linked multiples (e.g. when the data is distributed over multiple viewports; and overview plus detail (e.g. when navigating through Ben Fry’s The Preservation of Favoured Traces — shown as example).

TABLE B

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Related literature and theoretical focus 24

Filtering: control behaviors that handle the display of data depending on selective order criteria (Behrens, 2008). They include layering (e.g. selecting or deselecting filters on a search query); active objects (e.g. the visual display of a selected file or folder on an OS); boundary filter (e.g. selecting in and out markers on a certain dataset range); facet browsing (e.g. user limits range of results gradually as he determines search criteria step by step); dynamic query (e.g. the new Google instant search — shown as example).

Arrangement: control behaviors used to sort or manipulate the arrangement of data or certain data elements and groups. They include selective arrangement (e.g. solutions where different perspectives at the same data lead to different impressions (Behrens, 2008)), sortable columns (e.g. like sorting iTunes songs by genre or artist — shown as example), custom dimensions (e.g. discriminative size of different elements on bubble charts), and isolated comparison (e.g. patterns that allow a temporary comparison between two data elements).

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25 Related literature and theoretical focus

Exploration: control behaviors whose function is to provide the user with tools to navigate and accomplish their tasks. They include detail on demand (e.g. by separating the information of each data item into several cascading levels (Behrens, 2008)), and data-tips (e.g. like informative tooltips and interactive captions). The shown example features a Protovis interactive visualization.

Transition: a very important set of design patterns responsible for animations within a visualization.

As an example, there are animated transitions (e.g. when a certain data item pops up or suffers a visual state change, like the very foundations of the Gapminder World visualizations — consisting in a moving bubble chart).

2.2.4.3

Interaction Patterns

Design patterns (TABLE C) are meant to provide the user means to interact with a visualization (Behrens, 2008).

Boolean selection: standard singlechoice options like radio buttons, checkboxes (shown as example), and drop-down menus.

TABLE C

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Linear adjustment: methods that let the user explore large, intervalbased datasets, such as single and double sliders. The example on the left is iTunes’ volume single slider.

Spatial navigation: patterns that deal with the navigation within the entire spatial display as well as with the manipulation of single items, such as drag and drop and selection masks.

2.2.5 Key attributes Information Visualization is powered by key attributes that enhance the perception of the environment and are very important to stimulate context awareness through an aesthetically embodied experience. The following topics describe key attributes and inherited foundations of Information Visualization.

Aesthetics Recently classified as a Visualization field that converges aesthetics, data, and interaction (Lau & Moere, 2007), Information Aesthetics is known to be responsible for captivating and engaging the users. In fact, Norman stated that “attractive things make people feel good” and makes it “easier for people to find solutions to the problems they encounter” (Norman, 2004, p. 19). Its emotional value allows beautiful Information Visualizations (also classified by Lau and Moere as Information Aesthetic Visualizations) to accomplish their goals in a more pleasant way.

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27 Related literature and theoretical focus

Visual analytics Information Visualization is a tool for sense-making and is envisioned as a framework to support analytical reasoning (Viégas & Wattenberg, 2007). According to Thomas and Cook, it is facilitated by interactive visual interfaces, since it is used to synthesize information and derive insight from data (Thomas & Cook, 2005). Visual analytics is a multi-disciplinary field, a point of convergence between Information Visualization, data analysis, knowledge management, decision science, and many others (PICTURE 10) 24 — playing an important role in the decision making process (Keim, Mansmann, Schneidewind, & Ziegler, 2006). Visual analytics will enable users to quickly understand what’s happening and ultimately predict certain behaviors. With that in mind, Visual Analytics pops-up as one of the absolutely required and crucial core dimensions to be addressed by the proposed solution.

PICTURE 10

Situation awareness Also known as SA, Situation Awareness is focused on “the knowledge state that's achieved — either knowledge of current data elements, or inferences drawn from these data, or predictions that can be made using these inferences” (Klein, Moon, & Hoffman, 2006). Mica End24

For more information, see http://bit.ly/visualanalytics

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sley (Endsley, 1995) formulated a Situation Awareness theoretical model based on the following three main stages: Perception: the first level of Situation Awareness. Perception is responsible for capturing status, attributes, and dynamics of relevant items and elements of the environment, through monitoring, cue detection and recognition.

That leads to awareness of situational elements like people and their locations — Location awareness.

Comprehension: the intermediate level of Situation Awareness. Comprehension is responsible for the deconstruction of previous situational elements through the process of pattern recognition, interpretation, and evaluation. It will integrate all that information into a meaningful picture — a Visualization, in what concerns to this research.

Projection: the third and highest level of Situation Awareness. Projection is responsible for projecting future actions of the items and elements on the environment, which is achieved through knowledge and management of earlier stages. In a way, it will allow a certain level of predictability, which comes in handy in queue and dwell time prediction.

2.3 Simplicity Simplicity is the property of being simple. In the book that serves as foundation for this topic, The Laws of Simplicity (Maeda, 2006), John Maeda dissects simplicity in ten different and independent laws. The author describes principles of simplicity as intrinsically

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29 Related literature and theoretical focus

related to gestalt principles already mentioned during the previous topic, to real-life scenarios or even by relating it to his personal experience. His vision of simplicity is one of the theoretical foundations upon which this work is based on.

2.3.1 The ten laws John Maeda’s ten laws are grouped in three different groups, as described below.

Basic laws 1. Reduce — The simplest way to achieve simplicity is through thoughtful reduction. Simplification through removal of system functionalities is one of the suggested ways to achieve simplicity. By downgrading or removing unnecessary features and functions without significant penalty, a system becomes simpler, clearer, and easier to handle from a end-user point of view. Maeda proposed a method to be employed after the thoughtful reduction process, SHE, that stands for Shrink - Hide - Embody.

• Shrink — the smaller the object, the more forgiving users can be when it misbehaves (Maeda, 2006). The author mentions iPod-like electronic devices as shrunk designs for the incorporation of attributes like thinness and lightness, often giving the impression of smaller and humbler devices for their fragile look and feel.

• Hide — to hide complexity through brutal force (Maeda, 2006), like obtrusive controls, buttons, etc. As an example, John Maeda mentions the Swiss army knife as a tool that features the ability to hide inactive and irrelevant pieces depending on the context of use. Hiding complexity lowers

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expectations and in most cases gives the user the ability to toggle its visibility (like clamshell mobile phones, some website navigations, etc).

• Embody — after shrinking system functionalities and hiding remaining complexity, the next step is to embody quality. By doing so, products still remain valuable and attractive enough even though they are significantly smaller and less functional than their competitors.

Nonetheless, reducing the solution’s features to its minimum functionality doesn’t mean it will be more usable. Norman stated that “a product that does what is required, and is understandable, may still not be usable” (Norman, 2004, p. 77), suggesting that focusing solely on the product’s functionality is not enough to engage the user in an emotional experience.

2. Organize — Organization makes a system of many appear fewer.

Working with fewer objects makes life simpler when faced with the alternative of having way too many choices (Maeda, 2006). Maeda proposes a series of processes to better organize systems, SLIP ( P I C T U R E 1 1 ) 25 , t h a t

PICTURE 11

stands for Sort - Label Integrate - Prioritize.

• Sort — To categorize all items or objects in a meaningful yet rough arrangement.

25

For more information, see http://bit.ly/maedaslip

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• Label — To assign relevant names to each category or group previously defined.

• Integrate — To merge similar groups into new and wider groups (e.g. by relevance).

• Prioritize — To re-arrange groups according to different priority levels.

The author mentions the importance of tabs as an useful organization method, by providing a quick and recognizable tabular view over an array of apparently unrelated items.

3. Time — Savings in time feel like simplicity. When forced to wait, life seems unnecessarily complex (Maeda, 2006). An average person spends about one hour a day waiting, which could be reduced or reused by doing something else. To do so, John Maeda proposes a recurrent method: SHE. By shrinking (making things faster), hiding (removing time displays from the environment) and embodying (by styling to create the illusion of movement), time can be reduced towards a more simple life and experience.

Intermediate laws 4. Learn — Knowledge makes everything simpler. Taking time to learn often evokes the semblance of wasting time, a clear violation of the previous law. Nonetheless, learning and mastering the basics is one of the most relevant ways to achieve simplicity, no matter how long it takes to read manuals and documentation or perform long trial and error learning sessions. With this in mind, the author proposes a new method to enhance learning processes, BRAIN, that stands for: Basics are the beginning, Repeat yourself often, Avoid creating desperation, Inspire with examples, and Never forget to repeat yourself. Using the BRAIN method, simplicity can be achieved

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by mastering the basics through repetition and obtain inspiration from creative examples without despair.

When mentioning the immediacy of good design and its independence from learning processes, John Maeda synthesizes design as a Relate - Translate - Surprise process (Maeda, 2006, p. 39): “Design starts by leveraging the human instinct to relate, followed by translating the relationship into a tangible object or service, and then ideally adding a little surprise at the end to make your audience’s efforts worthwhile” This Relate - Translate - Surprise model may differ from culture to culture (e.g. the japanese culture couldn’t recognize Apple’s original trash can icon as it wasn’t meaningful at all in Japan) and approach metaphors as “useful platforms for transferring [...] existing knowledge from one context to another” (Maeda, 2006, p. 41) with minimum effort from the users and often requiring no learning at all.

When relating usability and learnability in his list of basic principles for interaction design, Bruce Tognazzini states that “ease of learning automatically coming at the expense of ease of use is a myth” (Tognazzini, 2011), suggesting that reducing the learning curve doesn’t imply a reduction in usability — they’re not mutually exclusive and so they should both be taken under consideration during the design stage of the solution addressed by this work.

5. Differences — Simplicity and complexity need each other. Just like Yin and Yang in chinese philosophy, simplicity and complexity are inter-dependent and correlated concepts. The more complexity there is, the more simplicity stands out and vice-versa. Simplicity can be achieved through differences that stand out (e.g. the use of the gestalt pre-attentive variables already mentioned on topic 2.2.3.1 —

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33 Related literature and theoretical focus

Visual taxonomies) as key aspects of the rhythm of feeling.

6. Context — What lies in the periphery of simplicity is definitely not peripheral. What appears to be of immediate relevance may not be nearly as important compared to everything else around (Maeda, 2006). The environment context is of great influence when making options of any kind by answering to the question “Where am I?”.

Simplicity can so be achieved through the understanding of the ambient and context that surrounds it. This concept of surrounding and environmental context is the same principle behind the Gestalt preattentive variables, where pre-conscious perception is triggered by shifting certain attributes of a given object in contrast to its context.

Deep laws 7. Emotion — More emotions are better than less. After employing reduction by shrinking, hiding, and embodying, simplicity might become aesthetically ugly. Its economic sense drives some users to think of it as actually having less quality than its expensive competitors by its cheap look and feel (e.g. cheap Ikea furniture might be taken as cheap quality furniture by some customers). When emotions are above everything else, complexity should take place by cautiously adding some layers of ornaments. Once again, the fifth law, Differences, paves the way to a more balanced rhythm of feeling towards simplicity by matching it with a subtle complexity.

8. Trust — In simplicity we trust. John Maeda comes up with the suggestion to lean back and take advantage of simplicity. As an example, the author mentions how useful it would be to send a customized greetings email to a close relative just by pressing a simple button. The machine would recognize the user’s preferences and would type a trustful and properly addressed message. He then raises the question “How comfortable would it be to the user?”. Trustfulness is

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Related literature and theoretical focus 34

one of the key attributes to achieve simplicity.

9. Failure — Some things can never be made simple. Simplicity is sometimes slippery and hard to find, as it might turn apparently impossible to pursue it when things are just irreducible. To exemplify, Maeda mentions his own failures of the laws of simplicity upon which this topic is based on: acronym overload (SHE, SLIP, BRAIN), bad gestalts (by mentioning that gestalt is the ability to “fill in the blanks” the author admits it can be confusing if taken logically), and the failure of having way too many laws (ten in total).

The tenth law 10. The one — Simplicity is about subtracting the obvious, and adding the meaningful. By pointing a solution to the collapse of the Japan National Rugby Team caused by its players’ high predictability, the author mentions a subtle and simple analogy: the players should be as unforeseeable as the bubbles in a glass of champagne. When in doubt, this law should be the one to take in consideration in order to achieve simplicity. To do so, the author proposes three keys: • Away — More appears like less by simply moving it far, far away, maintaining a reliable communication with an outsourced task (Maeda, 2006). As an example, the author recalls how a colleague jumped from a MIT mainframe computer to another mainframe computer in Columbia University, simply by typing commands on a terminal.

• Open — Openness simplifies complexity. John Maeda points some open source models like the free open source versus closed proprietary source (e.g. Linux and Windows platforms, respectively), the open APIs (Application Programming Interface) from companies like Amazon.com and at

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35 Related literature and theoretical focus

last the 37signals 26 free for a fee approach (e.g. their web framework Ruby on Rails 27 can be downloaded for free and complemented with extra for-pay services).

• Power — Use less, gain more. The author states that even though he has to charge all his electronic devices to be fully functional, it’s when he uses the last bits and moments of battery that he makes great use of them (e.g. writing his book with his laptop’s battery close to be completely uncharged).

2.3.2 Why it matters As described above, simplicity is more subtle than complexity. Its trimmed, reductive, and yet functional sense easily stands out from the background making the relevant pop in the foreground. Simplicity is a vehicle to a quicker, more effective, and contextualized understanding, paving the way to the idealistic solution addressed by this research project. Its proposed employing methods, SHE, SLIP, and BRAIN, constitute a framework to assist the design and development stages of the proposed visual prototype and is a significant part of the theoretical foundation of this work.

2.4 Discussion Looking back to Location-based services as described during the first topic of this chapter illustrates not only the market niche for the solution to be addressed by this research, but also the privacysensitive challenges related to indoor positioning and the look and 26 27

For more information, see http://37signals.com/ For more information, see http://rubyonrails.org/

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feel of the available solutions and state of the art. These market solutions are complexity-driven RTLS (Real-time locating systems) that do not meet the specifications of excellence through Simplicity.

Simplicity, delineated on the third topic, is grounded by John Maeda’s laws that will serve as theoretical foundation for the design of the proposed visual prototype. Along with this theoretical basis, Information Visualization (as described on the second topic of this chapter) is a core focus for its approach to design patterns (to be analyzed and employed as solutions on the visual prototype), cognition (to serve as strategy to target a proper understanding through gestalt principles and pre-attentive variables), and for its very nature and methods (a visual strategy to improve understanding through awareness enhancement). All in all, these three areas of research merge into a joint theoretical basis for the design and development of the solution.

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Chapter 3

Methodology

This project aims to propose a digital solution to support sense making by facility managers in indoor environments. The first steps taken to address such objective were based on brainstorming and extensive literature revision, followed by sketches and drawings on paper (PICTURE 12) using layers of transparent acetate film to illustrate different datasets (e.g. the plant, stream, and density views).

This first iteration was a selfdefined joint methodology based on experimentalism with the single guideline of answering the research question. To do so, I was

PICTURE 12

based on the IT University of Copenhagen’s ITUitter 28 , a webpage that dynamically maps Bluetooth devices captured within the university, using available Bluetooth positioning infrastructure previously set by BLIP Systems29 . For the purposes of this project, further work will be conducted according to this default environment: the IT University of Copenhagen. In the end of this stage, a first version of the solution was ready to be tested. The methodology employed to gather user feedback about 28 29

For more information, see http://tiger.itu.dk:8000/ITUitter/ For more information, see http://www.blipsystems.com/

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the prototype is described in 3.1 — Explaining the methods.

3.1 Explaining the methods Paper prototyping is a usability testing qualitative methodology that provides substantive user feedback in early development stages towards a quick iterative development. In Paper Prototyping (Snyder, 2003, p. 4) it is defined as: “A variation of usability testing where representative users perform realistic tasks by interacting with a paper version of the interface that is manipulated by a person playing computer, who doesn’t explain how the interface is intended to work” Paper prototyping turns out to be both a flexible solution for iterative design and a quick way to get user input and expert feedback, towards a more quickly refined user interface. The uniqueness and complexity of this project requires usability tests to be performed using a medium/high-fidelity paper prototype, to easily represent the complete GUI (Graphical User Interface) in favor of a low-fidelity representation of its user interaction — limited by the characteristics of the paper medium itself. With this in mind, multiple and complementary methodologies will be used to gather data concerning user feedback, suggestions, and interaction with the paper prototype, in order to capture the maximum possible amount of relevant information through the different research methods employed, as described below: Usability think-aloud and talking with the user A joint assisting qualitative methodology consisting on two distinct usability testing protocols. In usability think-

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aloud, users verbalize their actions while performing certain tasks. Though, this protocol may be slightly unintuitive and unnatural on its own. In other hand, by talking with the users during the usability test enables more refined user input and less reluctancy from the subjects (Snyder, 2003).

Observation and field notes With the support of audiovisual recording, previously authorized by the users. It is intended to be used to capture both the usability tests and the qualitative interviews.

Qualitative Interview Users will be asked to participate in an informal semistructured interview, that will reveal the users’ emotional response, information concerning satisfaction and aesthetics, necessary and unnecessary features, and overall suitability. This method is intended to provide additional understanding on the impact the interaction with the prototype has on the users, not only by analyzing the answers, but body language and emotions as well, while manipulating the paper prototype. A flexible and adaptable script will be used to help conduct the interviews.

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3.2 User inquiries 3.2.1 Participants Three different user profiles were set up to conduct three different user inquiries. Because the interface is based on the university’s floors and facilities, the common profile for these inquiries would be based on employees from the IT University of Copenhagen’s personnel whose function was somehow related to facility management and maintenance. All subjects would have to speak english, be digitally literate (i.e. using a web browser and common web applications), and familiar with the university’s plant and facilities. Age and gender aren’t relevant for the purposes of the inquiries. According to this setup, the following three user profiles were drawn: • The super admin user profile: facility managers or system administrators that have full access to the interface through different devices. For this user profile, a name came up: Michael Bloch (S1), Emergency Manager from the Facility Management department of the IT University of Copenhagen.

• The services employee user profile: services employees or operators that have limited access to the interface and only through mobile devices. For this user profile, two employees participated on the inquiries: Dina, the head of IT University’s cleaning services assisted by Stanley, a cleaning services’ employee (S2).

• The external advertiser user profile: marketing and campaign directors from external companies with the purpose of buying a space within the IT University of Copenhagen facilities to showcase certain ads. Since this user profile fails to meet common ideal characteristics previously described, a persona was created and impersonated by John Paulin (S3), professor at the IT University

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and supervisor in this project. The persona was a marketing director from a company that recruits students for part-time positions and student jobs, with a very strict goal in mind: to select the best and most adequate spot within the ground floor to place the ad.

3.2.2 Material There are some required material to conduct the two usability tests, as listed below with no particular order: • A medium/high-fidelity printed paper prototype • A computer with the digital version of the prototype and audiovisual recording software • Goal and task templates, for inquiry guidelines • Pen and paper for notes and observations • Scripts for qualitative interviews • A timer, to record interview and task timings • A calm and noiseless room with a large table and light

3.2.3 Listing the tasks A set of tasks will be presented to each user, depending on the user profile — which in turn will be based on distinct contexts of use. Further details about the instructions, goals, and steps may be found at Appendix — Inquiries, accessible through the CD-ROM attached to this report. All the results and discussion are available on Chapter 5

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— Results and discussion.

Super admin (S1) 1) Please identify high-density and lowdensity zones

6) Please identify how many users are on the current floor

2) Please identify high-density and lowdensity user streams

3) Please identify any critical issues, if applied

4) Please access the critical zone’s detailed information

5) Please identify the transit time between the front door and the back door

7) Please edit the name for the critical zone

8) Please set the current alarm to trigger at 50%

9) Please analyze the information captured from 24 January to 31 March

10) Please export the current information to a text file

Services personnel (S2) 1) Please identify high-density and lowdensity zones

2) Please identify high-density and lowdensity user streams

3) Please identify any critical issues, if applied

4) Please access the critical zone’s detailed information

5) Please identify how many users are on the current floor

External advertiser (S3)

1) Please identify high-density and lowdensity zones

2) Please identify high-density and lowdensity user streams

3) According to what you know about the top zone (the critical one), would you be interested in placing your ad in such place?

Please explain your answer.

4) According to what you know about that transit time (the highest value on the building), would you be interested in placing your ad in such place?

Please explain your answer.

5) Please point the place you think that best fit your purposes and explain your choices.

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3.2.4 Interviewing By nature, semi-structured interviews don’t follow any particular close set of questions. In this case, the questions will be adapted depending on the users’ performance during the tasks. There are some open questions whose answers could be extremely useful for the purposes of this project, as listed below for each of the user profiles:

Super admin (S1) Would this interface help you accomplish your job tasks?

Did something go wrong? What would you change?

Was the information (on the interface) relevant enough for your tasks?

Would a mobile version of the interface be helpful for you?

Services personnel (S2) Would this interface help you accomplish your job tasks?

Did something go wrong?

What would you change?

Was the information (on the interface) relevant enough for your tasks?

Would a mobile version of the interface be helpful for you?

Head of services: Would you be interested in such interface to manage your team?

External advertiser (S3) Did the interface actually help you select the best spot to place your ad?

Did something go wrong? What would you change?

Was the information (on the interface) relevant enough for your tasks?

Would you like to access this interface later to track your ad performance?

The results of this interview are available on Chapter 5 — Results and discussion.

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3.2.5 Procedure Each one of the three inquiries consists of an usability test using paper prototyping and a semi-structured qualitative interview, conducted according the following script: 1. A simple audiovisual recording setup is prepared: a computer with audiovisual recording software aimed at the users, to capture not only facial expressions, but also the interaction with the paper prototype.

2. The users are informed about the project and the interface, followed by an extensive explanation of the test and the purposes of the study that will follow. If applied, the user is explained about the persona to be impersonated.

3. The users are requested to perform the think-aloud protocol and maintain a dialog during the paper prototyping usability test. The paper prototype is introduced and the users are given a set of tasks to accomplish.

4. The users are asked to answer an oral post-test qualitative interview whose duration and structure may differ accordingly to the users’ performance during the usability test. Depending on the users feedback, the digital version of the paper prototype may be shown.

5. The test is concluded and the users’ participation is acknowledged, followed by a quick review of the inquiry to help to capture the most relevant points into notes and observations.

This is the common procedure for the three distinct inquiries: Inquiry A (conducted for the super admin user profile, impersonated

by S1), Inquiry B (conducted for the services personnel user profile,

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45 Methodology

impersonated by S2), and Inquiry C (conducted for the external advertiser user profile, impersonated by S3).

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46 Results and discussion

Chapter 4

Results and discussion

This chapter illustrates the visual prototype in its pre-alpha stage and then reveals the results from the inquiries conducted in the previous chapter. These results are qualitative findings regarding usability issues, user emotional response and product suitability and evaluation, establishing the foundations of this study. It is then followed by a descriptive walk-through of the proposed visual prototype in its definitive state (alpha) — as a result of the inquiries’ findings and critical analysis.

4.1 Visual prototype: Pre-alpha Solely based on literature revision and brainstorming already lined up on Chapter 2 — Related literature and

PICTURE 13

theoretical focus, this topic reveals the first stage of the visual prototype as used during the inquiries — mentioned as pre-alpha prototype throughout this chapter for its relation with the software release life cycle activities prior to testing

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and inquiries. It illustrates how the interface was designed according to the design patterns and gestalt principles previously described.

The visual prototype’s name, VIA, is an acronym that stands for Visualizing (human-density based) Indoors Activity (PICTURE 13)30 .

The prototype’s interface is structured according to the main areas described below. The design patterns and decisions on design are dissected in topic 4.1.1 — Basics and decisions on design.

PICTURE 14

• Display: the main visualization display upon which the facility plant is shown, with a set of display checkboxes on the top left corner. Those checkboxes enable multiple visual layers, depending on the following filters: • Plant: a simple map of the building and its floors, featuring low-fidelity representations of all facilities. This filter is activated on PICTURE 14.

• Antennas: a spatial mapping of all data-collecting points on the plant. For the purpose of this project, the visual display 30

For a video keynote, see http://vimeo.com/joaoramos/via

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filter and its symbology on the plant is related to Bluetooth technology, but may differ according to the available datacollecting system already described in 2.1 — Location-based services. Each data-collecting point (or Bluetooth beacon, in this case) is represented by a white circle containing the symbol of the data-collecting technology (the Bluetooth pictogram). This filter is selected on PICTURE 14.

• Density: a representation of human density, mapped on top of the white circles revealed by enabling the previous display filter. Each density visualization is represented by a grey circle: the bigger and darker it is, the higher the density for that particular zone; the smaller and lighter it is, the lower the density for that particular zone. This filter is selected on PICTURE 14.

• Stream: a visual representation of the user flow between multiple zones. It is only revealed when hovering (or single tapping on touch screen devices) a certain zone — it will then show all the relative user streams starting or ending in the selected zone. This filter is selected on PICTURE 14.

The display region is accessible through all devices and by all user profiles, although some users do have restricted interaction based on their user access level. Super admins and service operators can interact with the two following plant objects: • Zones: a data-collecting point with a visual representation of the current human density. All zones are visible on PICTURE 14.

• Inter-zones: the human stream between multiple zones, containing a series of white lines. More lines means a denser flow; less lines means a thinner flow. Each line represents an

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indiscriminate exchange of 5 customers as set by default, but the value can be adjusted by the super admin. All inter-zones related to the selected zone are visible on PICTURE 14.

• Sidebar: a dynamic zone on the right part of the display with the title of the current floor and access to a horizontal navigation through all the building’s floors. It features three vertical panels: • Visual editor: the first sidebar panel allows the super admin to edit the name for the selected zone for a proper labeling, assign a category to easily sort and order facilities by relevant attributes on potential third-party applications, assign user subscriptions to allow certain employees access to relevant information and alerts, and set individual alarms depending on the zone’s specific maximum capacity. This panel is active only when at least one zone is selected. It is the selected panel shown on PICTURE 14.

• Timeline browser: the second sidebar panel enables the super admin to go back in time and visualize activity snapshots from the past. Besides allowing going back to the previous day, week and month, it also gives the opportunity to set a specific timespan by setting the start and ending dates.

• Settings panel: the last sidebar panel is where the super admin can set the individual maximum density for each zone, set the timeline browser to show between maximum values or average values, export the current data to a text file (.CSV, .SQL, .XML or .RTF), change the password for the current username and send a support ticket to the help desk.

• Status bar: a dynamic zone on the bottom part of the display that reveals relevant data depending on the following selected sidebar

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panels: • Visual editor: the status bar shows the total amount of customers on the entire building in contrast to the total amount of customers on the selected floor and the average human density value (average number of customers per zone). It is the selected status bar shown on PICTURE 14.

• Timeline browser: depending on the selected timespan (current day, last day, last week, last month or a specific date), the status bar will show a timeline ruler with a draggable slider that will enable the super admin adjust both the position and duration of the selected timespan, by free transforming the width of the slider and moving the slider across the timeline ruler.

• Settings panel: when this sidebar panel is selected, the status bar will show the API Key for the current device, the total amount of devices with access to the interface and, from that same number, the total amount of active devices.

4.1.1 Basics and decisions on design Why density One of the first challenges of designing the visual prototype during its first iteration was how to best illustrate human behavior in indoor environments. According to John Maeda’s TRS method, Translate Relate - Surprise, that challenge requires a simple yet meaningful metaphor to represent movement and shifts in the environment in such a way that super admins would easily, quickly and instinctively recognize and apprehend the meaning and function of such a solution. Potential attributes would boil down to the amount of custom-

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ers and human density — but which attribute should be used to solve the challenge of representing human behavior? The answer came to mind when taking into consideration the environment. The indiscriminate amount of customers had no contextual meaning, although an attribute that related the amount of customers and a maximum value per respective zone seemed to make sense. Only by using the joint attributes of the total amount of customers and maximum capacity was it possible to provide both the location and environmentstatus awareness necessary to fulfill the challenge in hand. Each zone would then illustrate a density percentage that would translate into the ratio of total amount of customers per zone-specific capacity limit. This way each zone would represent relative levels of human capacity depending on their specific characteristics and ambient. In what concerns to security and emergency management, this seemed to be a good solution as well.

Why timeline navigation Time is a crucial dimension when evaluating and analyzing behavior patterns. Having the ability to go back in time and track specific incidents or overall pictures of happenings in the past gives super admins the tools to predict what might happen in the future. The timeline is that tool. Timeline navigation assists super admins on their management and monitoring tasks, giving them access to one of the dimensions unavailable in similar applications and competing solutions. By understanding average customer paths and hotspots for the previous months, customer services can adapt their operations for the following months, making the timeline navigation a core feature.

Why environment-driven instead of plant-driven Ekahau’s ERC, Cisco’s WCS and Zonith’s Bluetooth positioning module are three of the state of the art solutions available on the IPS market, all immersing the user in a plant-driven interface. This

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common solution seems to inherit its look and feel from architectural plants with all sorts of irrelevant information (e.g. accurately designed facilities with doors, windows, etc). Plant-driven interaction requires users to fully understand the plant and a set of extremely complex network-oriented tools and controls. These tools are targeted mostly for IT purposes and its manipulation demands way too much time for learning. On the other hand, the environmentdriven solution requires very little previous knowledge and interaction. Users can easily depict location-aware behaviors and monitor the simple yet functional range of data-collecting points without having to trigger any sort of events (i.e. click, mouse hover, tapping).

Simple interaction reveals meaningful targeted data and allows super admins to relate multiple zones. This minimal layout setup was employed the SHE method: controls were shrunk to their minimum yet functional sizes, irrelevant and obnoxious information was hidden or even removed and the overall aesthetics was delicately designed with proper contrast to involve the user in an enriched, peaceful and pleasant experience.

Why the design patterns Some important decisions concerning layout design and user interaction were taken based on the design patterns mentioned at the theoretical framework chapter. Below are assembled and categorized the most relevant design patterns used on the visual prototype.

The subject of simplicity is also present in the examples illustrated.

Display patterns — the Dwell time over the last 24h dialog (PICTURE 14) not only represents a timeline of quantitative values (Continuous

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Quantities), but also enables a correlation between multiple datasets (Correlations), when comparing the dwell time between multiple zones. These two patterns emerge into a visualization that engages the user in a visual comparison between two different places, during a certain selected timeline span. When selected one single zone on the plant, only one of these two datasets will show up, revealing the fluctuation of density values for that specific zone during the selected time. By selecting a second zone, both datasets will overlap into a single comparison visualization solution. The given example (PICTURE

13) not only demonstrates the value for the current average

dwell time for the active zone (in orange), it also enables the user to notice the value is significantly lower than the inactive zone (in blue). The Floor stream dialog (PICTURE 15) enables the comparison between absolute magnitudes through a stacked bar graph (Discrete Quantities) relating the flow of customers between the different floors, from -1 to 5.

By mapping two different dimensions,

PICTURE 15

the source (Coming from, in blue) and the destiny of the movement (Going to, in orange), this example can easily fulfill the role of a Correlation as well as previously exemplified. The disposal and relative arrangement of discrete quantities on a vertical axis paves the way to a quick observation: customers are mostly coming from the fourth (4) and fifth (5) floors and going mainly to the ground (0) and underground (-1) floors, while there’s a scarcely exchange rate between the second and third floors. The arrangement is set by employing John Maeda’s SLIP method, properly sorting, labeling and integrating both the floors and the dimensions

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PICTURE 14

Results and discussion 54

into a single visualization solution.

Now let’s get back to the summary view of the interface. A fully enabled overview of the plant not only reveals the proportional human density levels between the zones, but illustrates as well the spatial arrangement of the several zones/ data-collecting points (PICTURE 16).

The given example reveals a spatial positioning of eight zones/data-

PICTURE 16

collecting points spread around on one of the building’s floors (Spatial Configuration). Each one of the zones, illustrated by a white isometric circle, contains an inner grey circle that reveals the proportional human density magnitude related to the other zones (Proportion): the bigger and darker the circle, the higher the density for that particular zone.

Behavior Patterns — Navigation-driven design patterns are crucial for the user experience on the interface. Users can zoom in and out and perform panning on the plant view, visualize targeted details depending on the selected zone or even by dragging the timeline browser slider (PICTURE 17). These

PICTURE 17

behavior patterns enable interface manipulation towards a more flexible and re-actionable solution. Filtering is another pertinent behavior pattern, responsible for the visibility of relevant data layers. As exemplified (PICTURE 18), a set of

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controls toggle the visibility of respective datasets: the plant, the data-collecting points (beacons), the human density, and the customer stream between PICTURE 18

zones. By hovering (or single-tapping in touchscreen devices) the path of the Dwell time over last 24h dialog it is possible to see a data-tip showing the average dwell time for that specific time (PICTURE 19). This pattern (Exploration) is remarkably relevant to hide complexity by showing only what is

PICTURE 19

truly important for the visual analysis. By hiding certain details and controls, the interface remains as simple as it could be. Throughout all the user experience and navigation, transitions and animations assist users perceiving visual shifts. Regulated by the refresh rate of the interface, the inner grey circles, feature slight size transitions every time their density value is updated. This visual shift is as well noticeable when manipulating the timeline browser slider to go back in time.

Interaction Patterns — Some sidebar panel menus allows users to check certain options according to their needs, like the Export to file menu checkboxes (PICTURE 20)

on the Configurations panel — a boo-

lean selection pattern. Also, some of the menus PICTURE 20

are expandable, another flavor of the same pattern. Linear adjustment is the interaction pattern responsible for the interaction with some of the sidebar menu sliders, like the Maximum density menu on the Configurations panel (PICTURE 21), where users can drag the horizontal slider to adjust the respective value according to a large set of available values.

As previously described for the navigation behavior pattern, users

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PICTURE 21

Results and discussion 56

are able to spatially navigate through the plant view by panning and zooming, but also by selecting one or multiple ob-

PICTURE 22

jects (e.g. zones), as shown on the example (PICTURE 22).

Why the gestalt principles This topic collects some of the gestalt principles used while designing the prototype, as a crucial visual strategy to enhance critical thinking and assist the user in the process of understanding the environment. The subject of simplicity is also present in the examples illustrated below.

Pre-attentive variables — By using similarly shaped objects to represent human density on top of a contrasting geometric plant and by using different sizes and hues to distinguish between progressive levels of human density, the interface provides users the proper rhythm described in the fifth law of Simplicity, Difference, by shifting some of the pre-attentive variables of the gestalt principles — hue, brightness, orientation, size, and shape. While hue, brightness, and size are used to illustrate higher densities (captivating PICTURE 23

the users attention), the contrasting orientation and shape of the plant, its facilities, and the zones enable a more defined relationship between the background (the plant view) and the foreground (the density awareness).

Shape is also strategically used, along with hue and saturation, to trigger the user’s attention to a potential critical issue, illustrated by the yellow warning sign (PICTURE 23). The white lines that represent

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user flow between zones are also instinctively grouped by sharing the same properties: hue, alignment, orientation, size, and shape.

Grouping and alignment techniques — The layout and general arrangement of the interface is set by grouping together all the filters on the top left corner, all the menus on the right sidebar and the panels on the bottom right corner of the interface. This is a proximity setup of objects and elements that enhances user perception about the interface and its parts. By mapping and styling all the sidebar menu buttons the same way and applying an homogeneous visual formatting to elements with the same function, the interface is entirely illustrated around the concept of similarity (PICTURE 24).

To do, Maeda’s SLIP and SHE methods were employed: not only for sorting and properly arranging the items across the menus and the menus across the panels, but also to minimize the expectancy of the user regarding the relevance of the menu controls.

4.1.2 User interaction Besides the basic interaction with the sidebar panels, their controls and the display filters, it is also possible to interact with the previously described plant objects (i.e. zones and inter-zones). By clicking (or double tapping on touch screen devices) on these objects, users will access targeted information and details concerning customer activity on that specific place, as described below: • Zones: by interacting with a zone, users will access a set of four informative dialogs. All of them are labeled with the name of the

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PICTURE 24

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zone and the current density percentage according to its maximum density. The first dialog, Floor stream, allows users to understand where customers are coming from and where they are going to, relative to the currently selected floor. The second dialog, Density over the last 24h, shows a vertical bar graph of the last 24 hours density values for the selected zone — a horizontal line indicates the boundary between acceptable density values and over-capacity values. The third panel, Type of users, categorizes all customers in two groups: “passing by” customers that are just walking down that area and “static” customers that are in a fixed position. The fourth dialog, Dwell time over last 24h, illustrates the average dwell time over the last 24 hours on a line chart.

• Inter-zones: by interacting with the stream white lines between two zones, the user will access an informative dialog, Average transit time, that reveals the average transit duration between the two selected zones.

When selecting a second object without unselecting the first one, users will be shown a visual comparison between both objects. This feature is enabled up to three selected objects (zones or inter-zones).

If the selected objects are zones, all comparative dialogs will show each ones values or data with a 50% opacity layer and a different color. In case the selected objects are inter-zones, all values will be normalized and relative to the smallest value.

Mobile devices Even though HTML5 doesn’t include any compass API, the compass’ values can be fetched through JavaScript. With this in mind, a new feature is enabled for all mobile devices with a compass: compasssupported rotation. Users accessing the interface through their mobile devices won’t need to rotate their devices to the real position of

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the facilities — instead, the device itself will do that for them.

4.2 Results from the inquiries 4.2.1 Usability tests All subjects have identified issues with the interface. The following table lists all the problems pointed (some of them are mutual) or stumbled upon by the subjects during the usability test, both while performing their tasks and thinking-aloud. A matching color coding relates each issue with a critical analysis in 4.2.1.1 — Considerations, as used for the second part of this topic. An extensive list of all observations may be accessed via Appendix — Inquiries 31 .

Issue

31

Description

Subject(s)

#A1

The subject took up to 10 seconds to understand the symbology for the user flow between zones

S1

#A2

The subject seemed confused with the task goal and did not recognize the critical issue

S1

#A3

The subject required some help to understand how to interact with the the transit time between zones

S1

#A4

The subject didn’t recognize how many users were on the shown floor

S1

#A5

The user didn’t understand the goal of the task (to export the data to a text file)

S1

#A6

Even though the subject pointed the most highdensity zones as they seemed to be the most critical places on the shown floor, he explained that the information would only be useful to predict patterns for the following day (not the current)

S1, S2

Task templates adapted from http://paperprototyping.com

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#A7

The subject required more data about density values and percentages to accomplish his task

S1, S3

#A8

The subject didn’t seem interested about taking the average transit time between two zones in consideration to accomplish his task

S3

#A9

The subject required a more extended timeline span and more tabular data to accomplish his task

S3

#A10

The subject expressed concern about how the density values were calculated and required a way to understand (or even manipulate) the values individually for each zone

S1, S3

#A11

The subject pointed out that security measures for all the facilities point out maximum values for capacity, not density

S1

4.2.1.1 Considerations All subjects seemed to have had a pleasant experience with the visual prototype, regarding both its paper and digital versions. After having been explained the very basics of its parts, all subjects quickly understood the interface and the symbology used to represent relative density among zones. Even though there was one common procedure, all the different tasks and the inquiries from the different user profiles converged into an homogeneous set of results that led to the considerations discussed below.

Features meet subject expectations While accomplishing their tasks, it was noticeable that subjects often asked for more control over the interface and its displayed information — they were then revealed if there was a feature for that request or not. The subjects only mentioned core features already addressed by the solution as basic functionality, leaving no room for feature creeping (adding features that go beyond the basic goals of

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the solution).

Density values — When pursuing their task goals, subjects S1 and S3 (being the only subjects whose user profiles required a process of discovering information by navigating through the interface panels) asked how they could understand in detail how density levels were set for each zone so they could relate the given example to the real world (#A7). In fact, subject S1 revealed a certain interest in knowing in detail how the maximum capacity levels were set for each zone during task #A7, a relevant fact for the purpose of his role as an emergency manager: “How did you set the maximum values for this [Scroll Bar’s area, from the IT University of Copenhagen’s plant] zone? I know it can only stand around 300 people. If I notice its density is reaching its maximum value for that zone, will the effective value be close to reaching the maximum of 300 people as well?”

Subjects S1 and S3 again mentioned they should have control over the density values and manipulate them accordingly to certain environmental variables (#A10). Even though all subjects were shown how to edit the maximum capacity value for each zone, the issue seemed way too corroborated and supported to be left as it is, so the menu item responsible for this action should be transferred to a more proper place and given a more identifiable label (using the SLIP method). As it wasn’t clear for the subjects the concept of local density (amount of people per zone), a note was taken to better label and optimize the relationship between local density and capacity values.

Timeline navigation — In one of his tasks, S3 required a way to manipulate the interface in such a way that he could go back in time and analyze potential temporal patterns to help him decide rather a certain zone is suitable for his purposes or not (#A9). Because of S3’s

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external advertiser user profile characteristics, only S1 (super admin user profile) is somehow required to fully understand how to manipulate and handle the timeline, so some measures could be taken to help super admins quickly understand the timeline panel. Nonetheless, because paper prototyping lacks of any user interaction and also because a small learning curve and error margin is expected to fully understand some of the prototype functionalities, is it plausible that S1 didn’t quickly understand the timeline panel. In fact, some user interaction is required not only to drag and move the timeline browser slider shown on the bottom status bar when the timeline panel is selected, but also to reproduce with paper all kinds of visual feedback that the timeline browsing would reveal on the screen.

Still, for that matter, some visual tweaking could be useful to enable more recognizable display patterns.

Predictability over immediacy Subjects S1 and S2 mentioned that although immediate feedback is useful and helpful when accomplishing certain tasks, it’s certainly more adequate to predict behavioral patterns for the immediate future, like the following days (#A6). Predictability over immediacy is a completely unforeseeable attribute — instead of focusing on the immediate picture shown by the location-based data collected, the interface is taken more as a predictability tool than an immediate monitoring tool, at least for certain services. This phenomenon could be taken both as an advantage (issues predictability is a powerful and useful feature for the purpose of this project) and as a drawback (as the interface might not be used to monitor immediate changes in favor of being used to plan cleaning routes and optimize services for the following day). At this point, it would be useful to re-enable the “immediacy” feature over the “predictability” one and at the same time to find a solution for a issue detected in #A9 when S3 required tabular data to read immediate values of the current activity on the building’s floors, by providing a tabular data interface where users

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could import files previously exported by the interface. This would also be useful to assist super admins, as they could be asked to attach an exported file with a snapshot of the activity of their facilities in order to get targeted support from the helpdesk.

Previous knowledge S1 couldn’t perform the first tasks until he was explained some of the basics and symbology of the interface. It is quite noticeable (#A1, #A2, #A3, #A4 and #A5) that this subject took a bit longer to understand some tasks and accomplish some goals than what was expected, which may have been caused by many uncontrollable variables and/or external factors. Although, it was clear that the subject was continuously applying his previous knowledge based on his emergency-driven feedback while thinking-aloud: “I’m trying to look for it [the information about the total amount of customers on the current floor]... I know that these two zones have a combined total capacity of 350 people.. but how should I know the current amount of people?”

The subject seemed to be particularly focused on the purposes of his job, which deal with emergency routes and rules and crisis-based management (i.e. the subject continuously mentioned fire extinguisher placement, security regulations, etc).

Average transit time The amount and relevance of zone-centric information seemed to be enough for subjects S1 and S3 during their performance on Inquiries A and C, respectively. The average transit time doesn’t seem to be representative or relevant to understand human dynamics on such places — inter-zones. Both subjects mentioned that an average value

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for transit time could present a huge amplitude dramatically influenced by many irrelevant cases like customers randomly passing by, or running from one side to the other or even just standing there waiting for someone. Even though average transit time might be useful in punctual cases, this opinion is indeed valid and corroborated and so removing the feature will be taken under consideration.

Premium features When questioned about the utility of accessing the interface to track and monitor ad performance on latter stages of the ad campaign, S3 revealed a certain level of interest: “Well, yes! It could certainly be useful, now that I think about it.�

From the point of view of an external advertiser, it would be certainly captivating to be able to quickly scan an ad dashboard or a specific panel that would provide the advertiser targeted information about hypothetical conversion rates, average density nearby the electronic billboard, any eventual electric power failure, etc. This feature could account for a premium package of extra functionalities that super admins would have to purchase as an upgrade for their own solutions. This way only sine qua non features would remain part of the basic product plan. Other premium features could include access to external and third-party monitoring systems such as light, temperature, water, and electric monitoring or even extended capabilities for basic features like setting more customizable alarms or a more refined and flexible user categorization, etc.

4.2.2 Qualitative interviews During the semi-structured, informal qualitative interviews, all subjects pointed out suggestions and solution regarding the applicabil-

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ity of the proposed solution. Relevant issues are described below.

Issue

Description

Subject(s)

#B1

The subject expressed concern about how the density values were calculated and required a way to understand (or even manipulate) the values individually for each zone

S1, S2, S3

#B2

The subject required control over temporal data to get more relative information about density

S1, S3

#B3

The subject mentioned that the information would only be useful to predict patterns for the following day (not the current)

S2

4.2.2.1

Considerations

After the usability tests, all subjects were shown the digital version of the prototype in order to confront expectations and get more user feedback.

Features meet subject expectations Density values — When questioned if the information available on the paper prototype was relevant enough for their tasks, all subjects mentioned and restated the same issue identified during the usability tasks, later recognized by S2 (Dina, head of services, assisted by her colleague Stanley) in the final moments of Inquiry B as: “Very important to us [cleaning services]. We need to understand if a high-density zone is indeed a over-populated zone, because different facilities have different capacities.”

Timeline navigation — During the interviews, subjects S1 and S3 reiterated that it would be crucial to understand what happened in

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the past in order to understand potential patterns and hidden behaviors (#B2), and even to apply it in the future, as S3 pointed out: “I would need to understand how customers moved in the past in order to predict the optimal spot for my ad.”

This event reiterates what happened during the usability tests (#A7). Timeline navigation seems to be an appealing feature for all subjects. When asked if the overall solution would be useful to manage the cleaning services personnel, Dina and Stanley (S2) quickly demonstrated a positive feedback: “Of course! We could analyze yesterday’s activity in order to predict today’s cleaning routes. You know, sometimes we start our shifts by cleaning spaces that are not that dirty. And then sometimes we reach the actually dirty spaces when students start to take their places.”

Predictability over immediacy S2 clearly explained during the qualitative interview that some of the services (like the cleaning services) could only take advantage of the immediate feedback of the interface if using it to predict user behavior for the following day (#B3). When asked to explain the meaning of that statement, S2 responded: “We never know how dirty things are before we start our morning shifts. That’s why it would be useful for us to know how populated it [IT University of Copenhagen] was the day before. That way we could also predict any potential dirtier spaces for the following day as well.”

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Previous knowledge During the interview, S1 continuously mentioned security guidelines and emergency-centric suggestions that revealed the subject’s predisposition to apply his previous knowledge about emergency plans and facility security guidelines, by asking similar questions such as: “But can you set individual capacity values for each one of these zones? This place [IT University of Copenhagen’s Design Lab] can take up to 50 students inside. Can I set that value as maximum capacity for this zone, and then set other levels depending on my specifications?”

This susceptibility may have been one of the reasons why this subject required a longer learning curve, as discussed in 4.2.2.1 — Considerations (related to the usability task results). In the end of the inquiry, though, the user was shown how to handle and solve some of the previously approached tasks and quickly understood the controls and functionalities. This explanation was assisted by the digital version of the interface, which may have helped the subject abstract from the lack of user interaction of the paper prototype. All in all, this scenario uncovered an extremely relevant point: users’ interaction with the interface may be highly influenced by their previous knowledge and their work environment — while security managers might tend to evaluate emergency exits depending on the zone’s current density, cleaning services personnel may use the interface to plan next day’s cleaning path and external advertisers may only pay attention to hotspots and zones with a high amount of customers passing by.

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4.3 Visual prototype: Alpha According to the all the feedback gathered, some relevant changes were carried out for the alpha version of the visual prototype. These major changes were indiscriminately based on the converging issues identified during the usability tests and qualitative interviews.

Example

Description Added more contrast to the sidebar menus. Renamed the Settings panel “Maximum Density” to “Maximum Capacity” and moved it to the Zone Editor panel (previously named Visual Editor). Added labels to all panels.

Renamed “Configurations” panel to “Settings Panel”, added “Import from file” menu and renamed “Timeline scale” to “Timeline values”.

Added a tabular data interface to read data from an imported file, visible only when the plant layer is hidden.

Added labels to the three sidebar panels (only visible on hover).

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69 Results and discussion

Added more contrast to the status bar information and renamed the Settings status bar item “Points of Access” to “Active Devices”.

Added contrast when multiple comparative dialogs are active.

Added “Inter-zone exchange ratio” to the Settings panel and deleted the average transit time dialog and interaction between zones.

Renamed dialog “Type of users” to “Customers”, its categories to “Static” and “Dynamic” and deleted redundant human pictograms.

Added dates to the Timeline browser slider.

Added contextual help.

The final version of the solution is available in Appendix — Visual Prototype, with more than thirty screenshots showcasing all the panels, controls and layers.

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Results and discussion 70

4.3.1 Reflections After two iterative design stages inspired by a solid theoretical foundation and targeted user feedback, the visual prototype has reached its alpha stage — the digital solution to the research problem stated in the beginning of this research. When looking back at the user input on the visual prototype, one of the most curious and unpredictable points is how users took the presented solution as a tool to predict and plan future activity. Understanding the past indoor activity towards a more predictable future seems to be one of the most meaningful and valuable contributions of this digital solution. By visualizing activity snapshots of the past, users are able to manipulate their context-aware knowledge and map a new dimension on top of it: time. This ability to apply knowledge translates into Shedroff’s concept of Wisdom by enabling, supporting and inspiring users to achieve a higher level of awareness. This level of abstraction is quite useful for the context of use targeted by this research, which deals with facility and services management in indoor facilities. Being able to manage time is also a way to amplify situation awareness, which makes this solution an atypical multi-dimensional tool (previously presented state of the art solutions fail to allow temporal visual navigation and manipulation). Unlike its competitors, this solution transcends data-driven approaches to indoor positioning, paving the way to a new kind of location-based knowledge visualizations.

All subjects recognized the environment status and overall situation awareness, successfully relating the information to the real-life facilities. This role is fulfilled by a crucial dimension that was present throughout all the design iterations of the solution: Visual Analytics.

One of the tools that best assisted the emulation of such dimension was Ben Fry’s fourth stage of the data understanding process, Mine, by mapping data in a way that users can easily recognize relevant patterns. This process is highly influenced by the visual cognition

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strategies of pre-conscious perception. All in all, time turned out to be an unexpectedly core aspect of this solution and the timeline navigation functionality can only benefit from that, on the definitive version of the visual prototype.

During the early stages of the second design iteration, I requested expert input from targeted industry leaders, according to the list of companies that attended the Passenger Terminal Expo 2011 32 in Copenhagen, an international airport conference and exhibition. One of the most valuable contributions came from Catherine Davey-Stovin, from Damarel (industry leaders in software solutions for airport operations), raising the questions: “I’d like to be able to set a time threshold for people moving from A to B and present an alert if it exceeds a certain value. Can I do this with the solution? I’d also like to overlay information about typical routes through the facility, historical ‘dashboard’ style information about metrics (what and where are my peak times and why?), etc. Can a third party system be interfaced to the underlying database?”

This contribution raises relevant questions. Their context summarizes what I believe to be the next step for this solution as an indoor positioning interface for non-IT purposes. Let’s take a closer look at Catherine’s questions. While the current proposed visual prototype enables super admins to visually keep track of zone-centric spaces, it doesn’t include a similar tool for the space between zones. Although this feature was dismissed during the second iteration of the visual prototype as it didn’t seem to be relevant for the inquiry subjects, further research could be conducted to better target that challenge without compromising the core functionality of the solution. An32

For more information, see http://www.passengerterminal-expo.com

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other important point raised by Catherine was the possibility to visualize “typical routes”. This suggestion could raise customer privacy issues and amplify their anxiety if employed using accurate tracking. Unlike that scenario, this work addresses a zone-based (i.e. assets are clustered in zones, instead of being geographically positioned in accurate coordinates), privacy-safe solution. Catherine mentions an historical dashboard featuring peak-driven metrics as well. Although this solution doesn’t have a specific panel or section for that feature, it can be easily recreated by combining the timeline browser and the overall plant view. Catherine’s suggestions don’t seem to imply the requirement for new additional features, which may be a evidence that the solution properly fits its purposes.

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73 Conclusion

Chapter 5

Conclusion

This work discussed the panorama of location-based services. It started by providing an insight on the technology used by locationbased positioning systems and then described the state of the art for indoor positioning solutions, while covering the subject of privacysensitive information.

It then dissected Information Visualization as a strategy for cognitive enhancement, by defining the key aspects of gestalt perception and three of its most relevant attributes for the purposes of this work: situation awareness, visual analytics and aesthetics.

Visualization-driven design patterns were described as frameworks for the prototype design stages.

The theoretical basis was complemented by John Maeda’s concept of Simplicity. Its ten laws were summarized as design principles for the visual prototype. The methods widely employed during this work include SHE and SLIP. It then presented the methodology used to conduct the user inquiries. Paper prototyping was the solution taken to design and evaluate the visual prototype, even though its lack of user interaction was

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Conclusion 74

then noticed.

Before presenting the findings obtained, the first iteration of the visual prototype was presented, dissecting the basics. The design patterns and gestalt principles employed were then discussed, along with the methods curated by John Maeda as the theoretical basis of the iteration.

The findings from the user inquiries were then considered and examined. Relevant identified cases include the high level of predictability over immediacy of use, user expectations on available features and the ability to apply previous knowledge. A special subject described on all inquiries revealed how relevant it was for users to access and manipulate the timeline navigation.

Based on the critical analysis of these results, a second iteration was taken to commit some changes on the previous version of the prototype (the pre-alpha or pre-inquiries solution). The alpha version of the visual prototype was then presented, containing targeted solutions for all the issues identified during the user inquiries.

This visual prototype in its definitive state is my answer to the question raised in the beginning of this research, “How to best support facility management in their decision-making tasks through a digital interface that can inform them on the movements of people and objects in indoor environments?". Nevertheless, there is very little research on the subjects of aesthetics-driven digital solutions for indoor positioning using indiscriminate IPS technology, specifically for non-IT purposes and using zone-based tracking visualizations like the one proposed.

This work advances concerns related to aesthetics and simplicity in positioning interfaces and raises awareness on privacy enforcement through zone-based visualization. It strives for the pursuit of Visu-

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75 Conclusion

alization methods and cognitive strategies towards a more pleasant, understandable and flexible visual analysis. The visual prototype is a contribution that can inspire further industry-driven research and development on the areas of interface design in indoor positioning systems.

If airport passengers begin to feel secure about their zone-based location being handled by trustworthy service operators (Hansen, et al., 2009), then other zone-based, privacy-sensitive LBS can take advantage of that and promote more market-driven research for similar services in indoor facilities such as universities, hospitals, shopping centers, etc.

Further research could also approach the role of emotions in facility management and operations. How could designers manipulate user’s emotions towards a more supported decision-making? Norman recognized the same challenge when stating (Norman, 2004, p. 10): “Without emotions, your decision-making ability would be impaired. Emotion is always passing judgments, presenting you with immediate information about the world�

Aesthetics, one of the most relevant attributes of the visual prototype, seems to be one of the many approaches for the challenge described above. Because aesthetically pleasing objects enable users to work better, they become easier to deal and produce more harmonious results (Norman, 2004, p. 10).

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References 76

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Appendix 80

Appendix This research is supported by complemental documentation and resources available in the CD-ROM attached to this report. These contents are arranged in the following directories: Inquiries — set of PDF files containing the usability tests’ notes and observations and informal qualitative questions: • S1.pdf (first inquiry) • S2.pdf (second inquiry) • S3.pdf (third inquiry) • Paper prototype.pdf (as used on inquiries) Visual Prototype — multimedia documentation showcasing the visual prototype: • Pre-Alpha (screenshots from Pre-Alpha visual prototype) • Alpha (screenshots from Alpha visual prototype) • Video keynote.mov (5 minutes video keynote showcasing the Pre-Alpha prototype, also available at http://vimeo.com/joaoramos/via) Report — multiple copies of this report in other file formats: • Thesis.pdf (PDF version) • Thesis.pages (Pages version) • Thesis.doc (MS Word version, bad formatting)

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