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SENSEABLE CITY LAB

Touching Transport - A Case Study on Visualizing

Metropolitan Public Transit on Interactive Tabletops

Till Nagel

1,2, Martina Maitan

3, Erik Duval

2, Andrew Vande Moere

4,

Joris Klerkx

2, Kristian Kloeckl

1, Carlo Ratti

1

1MIT Senseable City Lab,

Massachusetts Institute of Technology

2Department of Computer Science,

KU Leuven

3Future Urban Mobility Group,

SMART

4Department of Architecture

Urbanism and Planning, KU Leuven

ABSTRACTDue to recent technical developments, urban systems gener-ate large and complex data sets. While visualizations havebeen used to make these accessible, often they are tailoredto one specific group of users, typically the public or expertusers. We present Touching Transport, an application thatallows a diverse group of users to visually explore publictransit data on a multi-touch tabletop. It provides multipleperspectives of the data and consists of three visualizationmodes conveying tempo-spatial patterns as map, time-series,and arc view. We exhibited our system publicly, and evalu-ated it in a lab study with three distinct user groups: citi-zens with knowledge of the local environment, experts in thedomain of public transport, and non-experts with neither lo-cal nor domain knowledge. Our observations and evaluationresults show we achieved our goals of both attracting vis-itors to explore the data while enabling gathering insightsfor both citizens and experts. We discuss the design consid-erations in developing our system, and describe our lessonslearned in designing engaging tabletop visualizations.

Categories and Subject DescriptorsH.5.m [Information Interfaces and Presentation]: Misc.

General TermsDesign, Human Factors

Keywordsdata visualization; multi-touch; exhibition; urban mobility

1. INTRODUCTIONIn recent years, more and more urban data is digitally col-

lected from municipal systems, sensors, and services. Var-ious commercial initiatives in the field of smart cities fromlarge technology companies such as IBM or Siemens work on

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new possibilities of analyzing the urban environment for ex-perts in public and private institutions. However, the ques-tion has been raised how to better involve the general public.Hill makes a case for active engaged citizens in order to un-lock the potential of technology in a city [10]. While the in-creasing dissemination of mobile devices and location basedservices enable citizens employing urban data for tasks suchas getting directions or finding a restaurant, these tools of-ten lack supporting free-form exploration that can empowerpeople to gain more general insights. In the larger process ofworking towards an informed discourse on smart cities, wedeem reaching out to both experts and citizens with a toolthat facilitates understanding of a city as an important step.One way of achieving that is to publicly exhibit visualiza-tions of urban data in an urban demo. These urban demos[18] aim to provide visual and interactive access to informa-tion concealed in large urban data sets, engage public andindustry partners in such operations, and collect feedbackand ideas from users.

In this paper, we introduce Touching Transport, a casestudy enabling casual exploration of urban mobility in Sin-gapore through a set of visualizations on a multi-touch table-top. Our aim is to enable users to discover personally rele-vant stories and explore suitable subsets of data, while mak-ing visualizations more attractive and easy to use throughplayful interactions and a visually engaging design. Touch-ing Transport is part of the LIVE Singapore! project [18],whose objective is to develop technologies and interface modal-ities to collect, combine and distribute large urban real timeand historic data. In this case study, we focus on publictransit data as it has various tempo-spatial characteristicstypical in urban data, and is of interest to a wider audience.

After exhibiting our system at a symposium, we followedthe semi-public deployment with a lab study informed bythe exhibition to evaluate how and what kind of insightsusers get, and how they di↵er between di↵erent user groups.

In the following sections we will describe our design goals,introduce the system and explain our design decisions, re-port on public deployment and user study, and discuss ourfindings and lessons learned.

2. DESIGN GOALSExperts from Singapore’s land transport authority were

continuously involved in the development through expertinterviews, design goal discussions, and feedback rounds forvisualization experiments. Furthermore, the design require-

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ments were informed by case studies on tabletop visualiza-tion for exhibitions [11, 12, 15, 27], and our own previousresearch [21, 22]. We will describe the goals, explain howwe aim to achieve them, and provide an overview on re-lated work for each. The first two are general design goalsof the urban demo, which influenced the latter three con-cerned with specific requirements for our system. As Touch-ing Transport is intended for casual exploration at an ex-hibition, our emphasis is on attractive visual design, andsimple interactivity, while still allowing di↵erent views intothe data set.

Enable access to data for different user groups.We see it as beneficial to bring together various stake-

holders, and to bridge the gap between experts and laypeo-ple for engagement regardless of their background. Interac-tive visualizations are an established way to communicatecomplex data to a larger audience [3]. They can providea common language for di↵erent user groups. Visually, itshould be appealing and easily understood without oversim-plifying or distorting the information. Data needs to bepersonally meaningful to users for them to be explored ina casual context [26]. An interactive visualization of publictransit should enable citizens to discover personally relevantpatterns, for instance by looking at the time they are com-muting, or by highlighting the areas around their neighbor-hood. Lastly, the system should employ interaction meth-ods to support di↵erent exploration styles [12] in order tobe usable by both laypeople and professionals with varyingexpertise.

Support gathering insights.Informing citizens allows them to discuss public transit,

and more generally participate in the debate of urban mo-bility. A side goal in our urban demo is to encourage usersto express ideas and wishes. Our visualization system thusshould support users in identifying interesting aspects, for-mulating questions, and ultimately gaining insights. In theirrecent position paper, Isenberg et al. [16] argue that data vi-sualization on interactive surfaces can help making insightformation more attainable. Designing our system for a largetabletop aims to make data more accessible due to the abil-ity to show detailed visualizations and to provide simpleinteractions.

Entice curiosity.We aim to engage a broader audience and invite them to

explore the system by enticing curiosity with attractive vi-sualizations and large-scale tabletops. Interactive tabletopswith large displays are seen as appealing to novice users [1],attract people’s attention in public spaces [15], and supportmore casual visualization settings [16].

Visual and interface design are guided by principles of in-formation aesthetics [19], with the goal to provide accuratedata representation with easy-to-use interactivity. Aesthet-ics not only concern the visual form, but also aspects suchas originality, innovation, and other subjective factors com-prising the user experience [29]. While the novelty of a tech-nique or system is not a value per se, it has been shown thatnovelty in design is an important factor to elicit aestheticresponse from users [14]. Our goal was to use an attractivevisual language resulting in a system easily understood andenjoyed by the users.

Provide casual exploration.Our system is intended as a walk-up-and-use system so

users can casually explore the data in an exhibition sce-nario, and aims to support contemplative usage [23] in asemi-public setting. The system should o↵er simple touchinteractions so users can explore the data without learn-ing complex gestures [16]. This lowers the cost for initialuse [26]. Thus, we selected basic interaction methods basedon well-established techniques fitting for tempo-spatial data.To invite users exploring the data for longer, the systemshould provide fluid interactions including high responsive-ness and smooth view transitions [5]. Overall, we aim toprovide an enjoyable user experience in order to engage visi-tors. And when they are accustomed and more experiencedwith the visualizations they might investigate urban mobil-ity in more depth.

Offer multiple perspectives.Multivariate data in the domain of urban mobility has

many interdependent properties. Our goal is to enable usersexploring the various facets, and discovering spatial andtemporal patterns of public transit [6]. Thus, our systemis intended to provide di↵erent visualizations, each o↵eringa specific perspective into the data set. The challenge is too↵er multiple linked views without being perceived as toocomplex in order to not discourage users from exploration[31].

3. TOUCHING TRANSPORTFollowing our design goals we implemented Touching Trans-

port as a functional prototype for a tabletop with multi-touch capabilities1. It was built with Processing and theUnfolding Maps library. We developed it in an iterativedesign process, including various visualization experiments,discussions with experts and test users, and incorporatingresults therefrom into the system. In the following, we willdescribe the data set, the visualizations and interactions ofthe prototype, and discuss our design considerations.

3.1 Public transit dataIn Singapore, passengers pay the distance-based fare by

tapping in and out on boarding and alighting subways andbuses. These actions are continuously recorded by smartcard readers, and collected by the Land Transport Authority(LTA), the government agency responsible for road tra�cand public transport. The data set consists of (i) geospatial(stations, routes, etc.), (ii) temporal (schedule, headways,etc.), and (iii) anonymized passenger data (tap ins and tapouts). From this, we derived further information such asorigin-destination paths for each ride.

We used a subset from the various sources available withinthe Singapore public transport system, and settled on busdata for the prototype. With over 2 millions passengers perday, Singapore’s bus network is an integral part of the sys-tem. We selected a sample of eight routes with di↵erentcharacteristics (e.g. express lines with few stops) to reflecta broad range of usage. These criteria were based on discus-sions with experts from the transport authority, and allowedus to examine the applicability for a set of transit data withdi↵erent properties.

1See video at http://youtu.be/wQpTM7ASc-w.

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Figure 1: Multi-touch tabletop with (a) map, (b) time-series, and (c) arc visualization of passenger data.

3.2 Three interactive visualizationsOur system consist of three interlinked visualization modes,

each supporting to look into di↵erent aspects of the data.It enables users to actively explore Singapore’s bus net-work, and see where passengers get on and o↵, how peopletravel between the island’s areas, and the way these patternschange throughout the day. While an interface at the bot-tom of the screen provides data filtering mechanisms, themost prominent part of the screen contains one of the threevisualizations. Together with a large full-HD screen display,this design of showing a single visualization aims to attractcasual users through its simplicity.

3.2.1 Map ViewThe map view shows passenger data for a selected bus

line on a background map of Singapore (Figure 1a). Foreach stop boarding and alighting passengers are visualizedas glyph consisting of two circles. The map view enablesthe understanding of passenger behavior in geo-spatial con-text, from analyzing and comparing single stops, to clustersof stops with similar characteristics, to neighborhoods andlarger urban areas.

3.2.2 Time-Series ViewThe time-series view displays multiple charts of the same

bus line for di↵erent times (Figure 1b). Each row shows allbus stops for a specific time of day. In a row, the stops of abus line are displayed sequentially, with boarding and alight-ing passengers shown for each stop. The resulting columnsvisualize passengers for a single bus stop over time. Thus,users can observe di↵erent times at a glance, and identifytempo-spatial patterns, e.g. locate geo-spatial clusters ofstops with similar properties over time.

A feature of this visualization is its adaptability to vary-ing time ranges. Users can adapt the time range to see fineror coarser grained data aggregations. The number of rows(and the time span used for aggregating the displayed data)depends on the selected time range. Thus, users can iden-tify hour-based temporal patterns, e.g. at what time a busline begins getting crowded, as well as compare longer termranges such as morning vs afternoon rush hours.

3.2.3 Arcs ViewThe arc view visualizes rides of a bus line (Figure 1c).

Each arc signifies passengers traveling from one station toanother, with the arc thickness indicating the number ofpassengers. This visualization enables users to see passengerflow, as well as connectivity of bus stops based on actual

bus rides. The number of passengers are double encodedas arc transparency, so that the most frequented bus ridesare easily discernible. Now, the characteristics of bus stopsbecome visible, e.g. a stop with many thin arc connections,or a public transit hub with some strong connections.

3.3 Visualization Design ConsiderationsFor displaying boarding and alighting passengers on the

map, we use concentric circles both being scaled accordingto their value. We used a diverging binary color schema withturquoise for boarding and orange for alighting. In order toease learning for casual users, we used the same encoding inthe time-series view. While various other time-series graphtechniques exist this ensured consistency.

While both map and arc modes support animating throughtime, the time-series view shows how passenger behaviorevolve over time in a single view. We decided on the smallmultiples technique [28] which has been shown to be moree↵ective than animation [24], and more e�cient for compar-isons across time series than combinations in one chart [17].In order to reduce visual clutter and to support focusing onthe sequential nature of a bus line, a linear layout for thebus stop sequences has been applied in this view. We optedagainst an equidistant positioning, where the screen distancebetween each two stops would have been equal, and chose adistance-based layout algorithm, where the screen distancebetween two stops is relative to their geographical distance.In that way, users still can recognize spatial groups of stops(e.g. city center), or low density line segments (e.g. expressroutes).

The arc visualization uses the same linear layout with thesame glyphs per stop below, with the arcs showing directedrides above. While the results of a user study on visualizingdirected edges [13] suggest using tapered connections, wecould not apply a visual mapping with varying thickness asthis was used for encoding the number of passengers. Thus,we mapped the direction to color gradient, with the samecolor schema as in all other visualizations, which also keepsconsistency in regard to color coding.

3.4 InteractionsTouching Transport supports direct view manipulation as

well as auxiliary controls. Users can directly interact withinthe visualizations, as well as filter the displayed data in thebottom bar, such as to select line direction, or change thetime frame and duration. These interactions were basedon well-established touch gestures, design patterns for datavisualizations, and studies for map interactions [7].

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Figure 2: (a) Tap to select a bus stop, (b) free-form map manipulation, and (c) dual finger time range adaptation.

3.4.1 Direct Interactions to select areas and stopsIn all three visualizations, users can tap on one (or mul-

tiple) bus stops to get details-on-demand with informationon the stop and bar charts plus precise values for detailedcomparison of the passenger data (Figure 2a). In the mapview, users are able to select a region by panning and zoom-ing the map with basic finger gestures. Users can drag topan, rotate to re-orient, and pinch to zoom the map (Fig-ure 2b). Even though more complex map manipulations arepossible, we opted for simple navigation patterns in order tokeep interaction complexity low.

3.4.2 Auxiliary controls to filter data and timeThe bottom bar is visible at all times. A legend shows

color coding, and visually explains how the data is mappedto its visual representation (Figure 3b). Tapping one of thebus route buttons (Figure 3c) selects the respective line inthe main visualization. All data is shown with the currentlyselected filters applied.

The timeline contains an integrated histogram for pas-senger load and fluctuation (Figure 3e), and provides threefunctions: animation control, time selection, and time rangeselection. Tapping on the play button starts an animationthrough the day, giving users a quick impression of tempo-ral travel patterns. Dragging the slider allows selecting anytime of the day, for instance to compare morning and eveningrush hour. Furthermore, users can investigate di↵erent timeclusters, altering the range dynamically in 30 minute inter-vals by adjusting the time range slider (Figure 2c). Thisresults in visual aggregations of the equivalent time inter-val. All time filtering methods update the displayed data in

Figure 3: The interface with a) the main visualization

(here: map), and the bottom bar containing b) legend,

c) bus filters, d) visualization selector, and e) time range

slider.

all visualizations. All passenger data glyphs are updated in-stantaneously, and always reflect the currently selected timerange. Their visual properties switch to the next state inan animated transition enabling users to visually track thechange, and to easier identify changes in the behavior ofpassengers.

3.5 Switching visualization modesUsers can switch between the three visualization modes

(Figure 3d). Showing one visualization at a time providesa simple entry point to exploring the data. We did notopt for the more classic approach of having multiple coor-dinated views [4] in order to reduce visual complexity, andnot overload a casual user with too much information. Intheir study on serendipitous discoveries through informationvisualization, Thudt et al. [27] used multiple views, describehow some of their users felt a↵ected by its visual complexity,and suggest showing only one visualization at a time as apotential remedy. Hence, in Touching Transport only onevisualization is displayed in the main screen area. Yet, allthree visualizations are interlinked. Selected bus stops in themain view, as well as all data filters chosen in the bottompanel stay consistent in all modes to maintain context, andto enable comparing the same data in di↵erent views [31].

When a user switches to another visualization the appli-cation animates between the old and the new view. Thesetransitions support the perception of changes between dif-ferent data graphics [8]. When, for instance, a user switchesfrom arcs to map view, the arcs fade out and the back-ground map fades in, while the visual representations of allbus stops animate to their new position, resulting in thewhole route morphing from linear to geographical order. Inthis way, our system supports users understanding patternsin di↵erent views while keeping the spatial context.

To summarize, the goals of the three visualizations are (i)to show spatial patterns, and provide an interface to selectareas of interest, (ii) to show tempo-spatial clusters, andallow comparing di↵erent times at a glance, and (iii) to showflows of passengers, and connectivity between bus stops andurban areas. Switching between these lets users focus on oneview, and still enables them to select the best fitting one inorder to gain insights based on di↵erent data properties.

4. DEPLOYMENT AT EXHIBITIONTouching Transport has been exhibited at an urban mo-

bility symposium at the National University Singapore (seeFigure 4). Open to researchers, civic employees, and invitedcitizens, this allowed us to informally observe usage in anon-study setting.

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Figure 4: Visitors exploring Touching Transport at an

exhibition in Singapore.

In two days over hundred visitors interacted with our sys-tem. The multi-touch table had been set up with a slightinclination in order to attract passers-by. Many visitors no-ticed the large screen even from a distance, and approachedto look at the visualizations. Some people first watched itfor a while, some directly started tapping the surface and ex-ploring the system. We noticed how some users tried simplytouching the screen without any apparent target, assumedlyto see if the system is interactive. These were more inclinedto explore it further when the map was shown, as this viewreacts wherever the user touched the screen.

Visitors had have few problems interacting with the visu-alizations, were able to switch bus lines and view modes, anddragging the time slider. Although our system is intendedfor single user interaction, the large interactive screen at-tracted visitors when others already were gathering aroundthe table. We have observed this honey pot e↵ect [2] inde-pendent from whether the earlier visitors were only watchingand/or discussing, or already actively exploring the system.

Visitors seemed to like the visualizations based on theirfacial expressions or their feedback in informal discussions.We also observed persons coming back and bringing friendsand colleagues along to show them specific stories they hadfound earlier. Urban demos act as a catalyst bringing to-gether actors from di↵erent fields, and enabling them havingan informed debate. We have seen various discussions takingplace around the tabletop, often within groups of strangers.

Exhibiting Touching Transport at the symposium was asuccess from our and our partners view: (i) Members fromthe LTA have expressed their wish to deploy the prototypein the foyer at their main o�ce, as they highly liked howvisitors were attracted to the tabletop. (ii) The LTA hasasked us since to help bring an extended version of the sys-tem in-house. We did not expect this as our system was in-tended for casual information visualization, and the author-ity possess and utilize their own specialized analytic tools.They explained that our visualizations were more engagingto use, and that multi-touch interactions could lower theaccess barrier for non-IT experts. (iii) Currently, TouchingTransport is exhibited in our lab in Singapore, and demon-strated to visitors to act as starting point for discussing thevalue of data visualizations in developing smart cities.

5. EVALUATION STUDYInformation visualization systems for visual analysis com-

monly aim to provide expert level insights. Visitors in exhi-

bitions may not have clearly defined questions in mind, butexplore visualizations based on spontaneous interest [15].We observed visitors investigating some questions and huncheswhile playing with the system in the exhibition. In informaldiscussions we learned they occasionally arrived at insightsabout specific parts of urban mobility. We found it di�cultto record users gathering insights in an exhibition setting aswe observed visitors exploring Touching Transport contem-platively. Even when discussing their findings with others,users not necessarily explained their inner monologue. Thus,after the first public exhibition, we designed a user study toinvestigate in detail how participants use the system whilefollowing ideas and questions. We evaluated how TouchingTransport supports di↵erent user groups, collect what kindof insights users could gain, and verify that di↵erent levelsof understanding of urban mobility can be reached. We re-port on the setup, the participant groups, and insights andsatisfaction results in this section.

5.1 Study designThe study consisted of five parts: (1) The introduction

with explanation of the study and the system, (2) a periodto freely explore the application to get accustomed with it,(3) the main part where the participant had to come up withinsights, (4) a questionnaire to measure satisfaction, and (5)an open discussion for further feedback and suggestions. Forparts (2) and (3) we asked the participant to think-aloud,and recorded the audio, while the interviewer observed andrecorded the behavior of the participants, and how they in-teracted with the system. Then, we asked for three insightsin three separated sub-sessions of 5 minutes each (and notfor just insights in one large session). We intended the threesub-sessions to encourage participants to come up with dif-ferent findings and/or to use di↵erent visualizations.

Each session had one participant. The study was per-formed in a lab setting, i.e. a room with the tabletop withadjusted light settings and no disruptions. This was donedue to allow participants to discuss occurring usability prob-lems, and to motivate reporting more expansive insights viathe think-aloud method. Overall, including the (4) post-testsatisfaction survey and the (5) open discussion, a sessiontook between 40-60 minutes.

5.2 ParticipantsWe tested Touching Transport with 27 participants from

three user groups: citizens with knowledge of the local en-vironment (LOC), experts in the domain of urban mobil-ity (EXP), and non-experts with neither local nor domainknowledge (NON). For the first group (LOC), we recruitedeleven members from our institution in Singapore (five mem-bers of non-research sta↵ and six researchers from othergroups). For the second group (EXP), we had six partic-ipants from the transport authority. For the third group(NON) we recruited ten students from KU Leuven, a uni-versity in a medium-sized European city, with majors incomputer science or architecture. Overall, we had 6 femaleand 21 male participants, aged 18 to 40 years (median: 28).

All participants had prior experience with touch devices,and eight (29%) with large-scale multi-touch devices. Theyhad to self-assess their experience with data visualization,and their knowledge about Singapore and public transit.Expert participants had most experience with visualization,and highest knowledge of public transit in comparison to

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Figure 5: Questionnaire results for each user group as normalized stacked bar chart, grouped by agreement (blue) and

disagreement (red). The lower charts each show four bars for overall system, map, time-series, and arc visualization.

the other two groups. Experts also estimated their knowl-edge about Singapore highest, with local citizens second,and Leuven students last. The results are according to ourexpectations and verify the classification of our user groups.

5.3 InsightsTo verify whether users were able to gain more advanced

levels of knowledge, we followed the suggestion of VandeMoere et al. to also request reports of meaning instead ofonly asking for facts [30]. We codified and classified insightsparticipants made (see Table 1) while exploring the data intoone of the following three categories, and for each describeexemplary insights participants made.

5.3.1 Basic insightsThe first category consists of insights into elementary spa-

tial and visual properties. Participants understood the shapeof Singapore and its topological features. They could em-ploy the map to relate visual representations of bus stopsto their positions, and understand spatial relations such asestimations of their distance. All participants were able toidentify starting and ending stops of a line. Furthermore,they recognized outstanding stops of a bus line, and for in-stance found the ones with large amounts of passenger flow.They were also able to compare two elements in a singlevisualization, and for instance identify the lesser used busstop out of two.

5.3.2 Medium insightsThe second category consists of insights into more ad-

vanced spatial patterns, and basic tempo-spatial patterns.Participants were able to recognize spatial clusters of similarbus stops. Furthermore, participants compared di↵erent el-ements over time, and were able to see basic commuting pat-terns. Most participants could correctly deduce dwelling andworking areas, based on the amount of passengers boardingin one and alighting in the other in the morning hours. Evenparticipants from NON with no local knowledge could iden-tify areas in this way. Participants found the morning peakto be shorter in time, while the evening peak to be spreadout by using the time slider, and analyzing the temporal pat-terns in the passenger histogram. Some participants wenton and inferred that this might be due to a wider durationof time in which people leave the o�ce, or are active at night

Table 1: Insights in each category per participant group

Basic Medium Advanced Total AverageNON (10) 16 8 4 28 2.8LOC (11) 12 17 7 36 3.3EXP (6) 4 5 12 21 3.5

such as running errands or having dinner before retiring forthe day.

5.3.3 Advanced insightsThe third category consists of insights into more complex

tempo-spatial patterns. Here, participants needed to filterthe data, for instance by updating the time-range or select-ing di↵erent bus lines. In this way, they could identify whichof multiple bus lines had most passengers at a specific time.Some also used this to further investigate commuting pat-terns, and discovered how these are inverted in the evening.Participants from Singapore (LOC and EXP) used their lo-cal knowledge to identify transfer stations, such as bus stopswhich are nearby metro stations. Five out of six expert andhalf of the citizen participants were able to associate move-ments with areas of Singapore. Some created hypotheses onmore specific reasons for passenger flow, for instance thatafternoon people travel towards VivoCity, a shopping mallin Singapore.

Furthermore, advanced insights were gathered by compar-ing the three di↵erent visualizations. Participants switchedbetween views to investigate if there were relationships be-tween spatial areas. They were for instance able to see that aspecific stop (whose large glyph on the map indicated manyalighting passengers) was getting passengers from variousstops in the arc view, and deduced that these were pas-sengers riding from their homes towards a transport hub.Participants from EXP were able to apply their expertiseand linked the visualized data with specific prior knowledgein order to gather more advanced insights.

5.3.4 SummaryThese results suggest our system empowers audiences with

divergent backgrounds acquiring a better understanding ofurban mobility. Overall, our prototype supports users un-derstanding public transit by getting various insights. In ourstudy, non-expert participants were able to gather a generalunderstanding, citizens a deeper sense of their city, and ex-perts higher levels of insights.

5.4 SatisfactionAt the end of the study participants completed a brief

post-test satisfaction survey with 21 questions. The sur-vey was based on Lund’s USE Questionnaire [20], and useda 5-point Likert scale ranging from Strongly agree (5) toStrongly disagree (1).

Most participants found Touching Transport useful, andagreed or strongly agreed it helps them understand publictransit in general (8/10 NON, 6/11 LOC, 4/6 EXP). Theywere satisfied with the system and its ease-of-use, found it

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fun to use (24 out of 27), and aesthetically pleasing (26 outof 27) (see Figure 5 for details). While the results for allthree groups were mostly similar, some areas had di↵erencesbetween groups. Firstly, citizens (LOC) were less satisfiedwith the ease-of-use of the overall system and with each vi-sualization compared to the other groups (3.18 vs NON:4.4,EXP:4.3). One explanation is that our citizen participantshad lower prior experience with data visualization. Sec-ondly, all groups agreed to the statement the overall systemwas fun to use, and beautiful. However, the fun-to-use re-sults varied in regard to the di↵erent views. Non-experts(NON) preferred the map the most (4.7 vs EXP: 4.0, LOC:4.1), while experts favoured the time-series (EXP: 4.2 vsNON: 3.7, LOC: 3.4). As both groups rated the ease-of-usefor the map visualization equally, this di↵erence could in-dicate that non-experts prefer familiarity as they are mostaccustomed to reading and interacting with maps. On theother hand, experts in the field of public transit seem to pre-fer abstract visualization methods as it allows them to viewmore complex aspects of the data.

6. DISCUSSIONIn the following we discuss and generalize some of the

lessons we learned in designing our system in order to informfuture case studies with the goal of enabling insights intocomplex data sets.

Crafting aesthetics: Visual style and ResponsivenessIn the field of information visualization there typically is adistincation between systems with a high appeal to com-municate stories to a general audience on the one hand,and expert systems to support analyzing data and gener-ating insights on the other. While visualizations in publicexhibitions have been studied for casual users, our goal, incontrast, was to enable both experts and citizens gaining in-sights. The focus of our research was to employ establishedvisualizations adapted to a specific set of urban data. Ourcontribution lies in their aesthetic and usable composition,and the description of our design considerations.

We tried balancing appeal and accuracy in our visualiza-tions. A visual design with the sole goal of being aestheticmay have a negative impact on readability of precise datavalues. While multiple expert participants autonomouslyverified the general validity of our visualizations with theirprior knowledge, the intent of casual visualizations is not tocome to crystalized conclusion [23], nor exact qualitiativeestimation of values for each visual element [15]. Based onpeople’s insights we conclude that our system enables peopleto understand di↵erent patterns correctly. We have shownthat it is possible to design an exploration tool that helps adiverse range of users gaining new insights, while attractingcasual users with an appealing style.

In both the exhibition as well as the lab study, users werepleased with the fluidity of the interactions, and liked thehighly responsive interactivity. Some users expressed theirjoy about how“snappy”the time slider felt. Touching Trans-port supports traversing the visualization pipeline with suchperformance that every filtering of the data or adaptationof the view is instantly reflected (<0.3s) in the graphicaldisplay. While it might be di�cult to provide immediate re-sponse for voluminous and complex datasets [25], investingtime and e↵ort in the technical implementation of the sys-tem contributes to a enjoyable user experience. Our study

results show that carefully designing for performance fos-ters keeping people engaged with a visualization system ina casual setting.

In summary, Touching Transport has been found to be en-joyable and aesthetically pleasing, and to foster an engaginguser experience. Thus, we underline the importance of craftin designing high quality visualization systems.

Multiple coordinated vs single modal viewsTouching Transport shows one visualization at a time, ratherthan all three simultaneously, in order to lower visual com-plexity for casual users. We observed users switching viewsto verify a hypothesis about the displayed data, for instancefrom arc to map view (and vice versa) in order to understandthe spatial properties of the visualized bus route. While wedid not evaluate through a comparative study whether thebenefit of coordinated multiple views in visualizations out-weighs the possible distractions they might create [9], withour case study we demonstrated that switching modes work,and can provide multiple perspectives to support obtaininginsights. Moreover, the cognitive load of switching views canbe lowered with animation. Participants in our study highlyliked the transitions between the three visualization modes,and also found it to ease following currently focused dataelements. We believe the trade-o↵ of mentally maintainingcontext is worth the simplicity of a single view. Having oneview at a time allowed us furthermore to attract users withone visualization they are familiar with. In our case we uti-lized the map view as simple entry point to invite visitorsto start exploring.

Exhibiting prototypes as part of the design processA further contribution are the results from both a publicdeployment and a controlled evaluation study of the sys-tem in use. In our design process of creating and evaluat-ing an urban demo, our in-the-wild study primed the labstudy. Our contribution lies in the reflection on why wechose this methodological setup, and in the systematic studywith three groups of users. We described the di↵erences andsimilarities in their behaviour, and the varying levels of in-sights they made.

Citizens and non-experts expressed their wishes for similarvisualizations helping them to improve personal travel, whileexperts asked for having such system for visual analytics tosupport both planning and real-time control. The fact thatusers propose such features demonstrates that the systemworks for idea generation. Our urban demo helped peoplethink about yet new ways of understanding public transitthat go beyond the system’s current possibilities.

7. CONCLUSIONWith our case study on public transit data, we demon-

strated how interactive visualizations on tabletops can con-tribute to the emerging field of smart cities. The three vi-sualization modes facilitate getting di↵erent perspectives onthe data set, while the fluid interactivity integrates theminto a unified user experience. Together, they provide peoplevisual and tangible access to information about the publictransit network, and enable understanding some of the vitaldynamics of their city. A deployment at an exhibition andthe results from our user study suggest that our design goalswere largely met: visitors were attracted to the system andcasually explored the data. Participants of our study were

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able to obtain insights, ranging from understanding basicssuch as transport hubs to more advanced such as commutingpatterns.

While supporting concrete citizen participation is an im-portant subject, it was beyond the scope of our research.In this paper, we argue that a well-designed system visu-alizing urban data can inform citizens and enable them togain insights. Our intention was to create an urban demo topublicly exhibit visualizations in order to encourage discus-sion and gather feedback on mobility. Yet, our applicationis but one step in the larger process of bringing togetherdi↵erent stakeholders of a city, and ultimately encouragingparticipation.

We see the lessons learned to be generalizable for othertempo-spatial data sets aimed at a diverse audience. WithinLive Singapore, we are currently developing a system allow-ing citizens to combine and explore di↵erent urban data sets.

8. ACKNOWLEDGMENTSWe would like to thank Oliver Senn for his excellent sup-

port in data preparation and analytics. We also thank theparticipants of our user study, and the Singapore Land Trans-port Authority for providing data and expert feedback.

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