Descripción de las Formaciones

In order to involve people along the street, throughout the event, a distributed approach was chosen, inspired by the Visualising Mill Road study. Input technology was distributed at four data collection points (see Figure 5.5), to cover the event’s main area. These locations were selected based on the information provided by the event organisers, which suggested the middle part of Mill Road would likely be the most crowded. In all four locations, sound

sensors were attached to lamp posts, researchers were positioned to collect perceptions from attendees, and photos were taken of the crowd at regular intervals.

Figure 5.5: The four locations where people could vote (black dots) and the central location where the data was visualised (red dot)

5.2.5 Visualisation design

A key aspect of the intervention was the public visualisation of all collected data during the event. This visualisation was designed to communicate the data in an accessible man- ner by relying on simple representations, making it easy for passers-by to understand what was visualised. The aim was to give a comprehensive overview of all four types of data: subjective noisiness, objective noisiness, subjective crowdedness and objective crowdedness. Furthermore, the visualisation was designed to allow people to compare the levels of noise and crowdedness over time.

Figure 5.6: Sketch of the visualisation of objective and subjective data gathered during the one-day community fair

The primary challenge for the visualisation design was representing all four types of data in an understandable way. The sketching process spanned over several weeks, exploring a range of representations suggested during the brainstorm session, ranging from simplified 2D maps to 3D balloons. Representing both spatial data (differences in noisiness and crowdedness along the road) and temporal data (differences in noisiness and crowdedness over the course of the day) proved to be complex. Therefore, the decision was made to focus the visualisation on the differences over time, by representing the data along a timeline.

To allow for easy comparison between subjective and objective data, the collected objective data was divided into three categories: low, medium, and high. For example, photographs with a relatively low people count were categorised as ‘low’ crowdedness. As this categori- sation was based on relative crowdedness and noisiness during the day, it was expected that small adjustments would have to be made throughout the event (e.g. when a people count is first considered ‘high’, but photograph taken at a later stage has a far higher people count, the former would be corrected to ‘medium’). The three-level categorisation mapped onto the answer options provided through the tablet application, and as a result enabled direct comparison.

For the visualisation of noisiness, the choice was made to adapt a highly familiar represen- tation of sound: three curved lines emerging from a speaker. To simplify the visualisation, these curved lines were transformed into straight lines, which were coloured in either partly or fully, depending on the collected data (see Figure 5.6). For the visualisation of crowd- edness, use was made of baubles, as an abstract representation of people when viewed from above. The more baubles, the more crowded the street during that time. Baubles were also attached in triangular shaped, to visually match the aesthetics of the sound representations (see Figure 5.6). Large labels were displayed left of the representations, indicating whether they showed data collected from the deployed sensors (‘sensor’) or from the attendees (‘you’). In addition, the labels ‘sound’ and ‘crowd’ were added to the visualisation, to communicate which measurements were visualised. The decision was made to use these annotations, as opposed to ‘noisiness’ and ‘crowdedness’, to avoid possible negative associations with these terms. Furthermore, because these terms were shorter, the labels could be displayed in a larger font size, making them more visible. Building on the street art theme of Visualising Mill Road, use was made of neon tape to visualise the data. In addition, plastic neon Christ- mas baubles were used to visualise crowdedness, to fit the Christmas theme of the fair. 5.2.6 Choice of visualisation location

To ensure high visibility, the railway bridge in the middle of the street was chosen as the most suitable location to situate the public visualisation (see Figure 5.5). While there were various alternative locations available, the aim was to make sure as many people as possible would walk past the visualisation. Due to its central position on the road, the vast majority of people at the fair would cross the bridge at some point during the day. Furthermore,

the walls located at the sides of the bridge provided a large canvas, suitable for the public visualisation: 30 meters in width by 2 meters in height. For this deployment, the decision was made to present the visualisation on a vertical surface, rather than to spray the data on the street again, as the crowdedness during the day would have likely made it difficult for people to notice and read a visualisation positioned on the ground.

5.3

In-the-wild study design

On the morning before the start of the event, the Smart Citizen Kits were mounted on lamp posts in the four selected data collection points along Mill Road. Furthermore, a team of researchers covered up the bridge with black plastic sheets, thereby creating a blank canvas. The layout of the visualisation was then outlined on this canvas, including all labels and time stamps. Four researchers positioned themselves at the four data collection points to approach people with the custom tablet voting application. Two researchers were positioned on the bridge, to regularly update the visualisation. One researcher moved along Mill Road, taking hourly photographs of the crowd in the four data collection locations.

Every hour, both the subjective and objective data were collated by the researchers positioned on the bridge. The initial measurements at the start of the fair, when it was still very quiet and none of the activities had started, were used as baseline ‘quiet’ measurements, for both crowdedness and noisiness. All measurements taken later in the day were compared to these ‘quiet’ measurements and categorised accordingly (see Figure 5.8). Seeing as the close to real- time visualisation required the data to be interpreted and compared continuously, this also meant the visualisation had to be adjusted as the day progressed. When, for example, lower- than-baseline sound measurements were taken, the sound visualisations were re-categorised. Again a mixed methods approach was used to evaluate people’s engagement with the visu- alisation and the four data collection points. The following data was collected to examine engagement: (i) logged votes from the tablet applications; (ii) observations in-situ at the data collection points and the public visualisation.

5.4

Findings

The Mill Road Winter Fair attracted, as expected, approximately 10,000 attendees over the course of the day (see Figure 5.7). Throughout the day, researchers approached attendees to

ask them to submit their perceptions of the noisiness and crowdedness at that time and loca- tion. While a large number of people agreed to participate, who casted a total of 1093 votes, the study also revealed that the chosen input technology caused confusion amongst attendees of the event, who associated the use of tablets with salespeople. Similarly, the combination of the input technology and the chosen topics gave some attendees the impression that the noise and crowd levels were being measured by local authorities, in order to monitor the extent to which the fair was a nuisance for the neighbourhood. While the event revealed a number of flaws in the design of the intervention, the public visualisation did evoke curios- ity, provoke discourse, encourage comparison, and invite tactile interactions, as described in detail in the following sections.

10:00 12:00 14:00 16:00

Figure 5.7: Two example time sequences of crowdedness during the fair

5.4.1 Contributions

During the event, a total of 1093 votes were cast via the tablets. Of these, 553 answered the question related to crowdedness, and 540 answered the question related to the sound level. Thirteen people did not complete the second question. From observations it emerged this was primarily caused by people who voted quickly while walking past, without realising a second question would follow. As shown in Table 5.1, all four locations received similar numbers of votes, approximately 270 per data collection point. For both the crowdedness and noisiness question, the majority of votes were cast for ‘medium’ (respectively 58.2% and 52.6%). Overall, relatively many people perceived high crowdedness (25.5%). In contrast, 37.6% of the people who voted perceived low noisiness, as shown in Table 5.1.

uestion 1: crowdedness uestion 2: sound

Location Low Med High Total Low Med High Total

1 23 95 18 136 59 67 6 132 2 20 69 53 142 33 83 26 142 3 25 94 30 149 72 71 8 151 4 22 64 40 126 39 63 13 115 Total 90 322 141 553 203 284 53 540 Total (%) 16.3% 58.2% 25.5% 100% 37.6% 52.6% 9.8% 100% Table 5.1: Overview of votes per location

The observations by the researchers at the four data collection locations revealed two key reasons for people not contributing. Firstly, because only one researcher was situated in each data collection location, only a small number of the attendees that walked past could be approached. While the other people may have come across the project at one of the other data collection points, it is also highly likely that not all people who attended the event came across the input devices. Secondly, it emerged that people associated the use of tablets in combination with researchers actively approaching attendees with salespeople – and assumed that they were being asked to pay for a product or service (e.g. “Are you trying

to sell me something?”). In addition, some attendees presumed that the questions were being

asked by, or on behalf of, local authorities, to regulate nuisance caused by the fair. These two misconceptions, caused by the chosen input technology and topics, were found to act as a barrier to participation for a proportion of attendees by researchers at all four data collection locations.

5.4.2 Curiosity

During the fair, a continuous stream of people passed by the visualisation, many of whom stopped to take a look at it. For those who had already come across one of the researchers on the road, who had asked them to enter their perceptions of crowdedness and noisiness, the visualisation provided an opportunity to compare their personal perceptions with those of others, and the objective data. However, many people who approached the researchers on the bridge had not come across the data collection locations yet, and instead approached the visualisation to learn more about how the data was collected and why. This curiosity sparked conversations between strangers, where one explained the visualisation and aim of the measurements to the other.

Furthermore, it was observed that adults as well as children had a tendency to touch the visualisation while trying to interpret the data. The playful and colourful design, combined with the three-dimensional baubles and the texture of the neon tape, was found to evoke curiosity and attract tangible interaction (see Section 5.4.6).

The input devices were not found to evoke curiosity. Instead, people generally had to be actively approached by one of the researchers in order for them to notice the custom tablet application. The small size of the tablets, and the crowdedness of the event, likely affected the visibility of the input technology.

5.4.3 Revisitation

The regular updates of the visualisation ensured that new data would be available every hour. This rhythm of updating, inspired by the Visualising Mill Road study, motivated some people to return to the installation (e.g. “I’ll come back in an hour, to see how it has changed”). The researchers observed several people who viewed the visualisation two or more times throughout the day. However, this did not appear to be a common occurrence. No one was observed returning to the input devices.

5.4.4 Discourse

Many of the attendees were found to be keen to discuss their perceptions of the crowdedness and noisiness, both while entering information into the tablet application and when looking at the visualisation. Some people emphasised their positive experience of the crowdedness and noisiness (e.g. “It is noisy, but a good noisy”; “It is a wonderful level of crowdedness and noise”;

“When the band starts, it gets lovely and noisy”; “It’s a funky noise, I like it!”), while others found

the fair to be lacking in noise and crowdedness (“It should be noisier”; “I expected it to be noisier.

The noise is patchy, I’d expect more people in groups – talking”; “I’d expect it to be a lot more crowded. I mean, it’s Cambridge, isn’t it?”. The input technology and visualisation were observed to

be sparking conversations within groups of attendees, between strangers, and between at- tendees and the researchers. Many of the discussions revolved around comparisons with previous years and experiences elsewhere, as described in the following section.

5.4.5 Comparison

When discussing the crowdedness and noisiness, people were observed often comparing their perceptions with their knowledge of what the fair was like in previous years (e.g. “Not crowded

at all compared to last year”; “It will soon change!”; “It will be noisier later! This is what I’d expect at this time”), or with their experiences elsewhere. The latter consisted both of comments

on cultural differences (e.g. “Maybe for British people this is noisy, but I’m Spanish, we love noise”;

“We’re from the continent, we like it loud”; “Not crowded at all, we’re from London!”) as well as

different events (e.g. “It’s like Glastonbury, but colder and on a street”).

The large visualisation also enabled people to compare data collected at different times dur- ing the day. This was found to encourage people to walk past the bridge’s wall to view all data.