Datasets and Methodology

As previously described in Section3.4(Capturing Viewer Mobility Data, p.65), a number of techniques can be used to capture viewer mobility data. In particular, we consider datasets that

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are captured through mobility models (synthetic mobility traces), and viewer-based tracking (originating from Tacita). We note that the use of infrastructure-based data capture techniques was not possible due to the lack of an appropriate Wi-Fi location tracking system at Lancaster.

4.2.1.1 Synthetic Analytics

We consider the use of synthetic movement traces created using the synthetic analytics approach first introduced in Section3.4.3(Synthetic Analytics, p.76) utilising the following mobility models:On-Campus Student,Off-Campus StudentandRandom Building Navigator

(see Section3.4.3.2, p.78for more details).

Using these models, we executed the simulation with 2,000 agent instances for each mobility model simultaneously resulting in a total of 6,000 agents constantly moving across the spatial model of the University campus. We simulated a specific time period of 62 days (1 October 2015 until 1 December 2015) and combined the computed mobility traces and display sightings resulting from the simulation with the logs of content played from the identical time period that were captured using Pheme (the dataset captured through Pheme was initially described in Section3.3, p.59). The resulting dataset therefore consists of a combination of both real and synthetic analytics data and provides a set of display sightings and a log of content seen for each instance of an agent. This dataset enables us to create analytical insights for individual content items across the signage network and the experiences of viewers and passers-by – without the limitation to single displays or isolated spatial areas of the deployment. These complex signage analytics insights would have otherwise required the deployment of comprehensive tracking technology of individuals – going beyond what current state-of-the-art analytics tools (e.g. face recognition software) are able to offer in the digital signage domain.

We note that accuracy of reports generated using synthetic analytics are highly dependent on the quality of the underlying mobility models. In future, movement and mobility traces collected through various tracking technology could be used to inform the design of corre- sponding models and ultimately improve the quality of the simulation. We recognise that our approach is limited due to the use of simplistic mobility models and the lack of the considera- tion of contextual events. For example, lecture timetables, bus schedules and automatically collected room occupancy metrics (e.g. through the use of attendance monitoring software) across the University campus could be used to inform the design of mobility models.

A complete description of the dataset captured through Pheme is provided in Section6.2 (Pheme: Display-oriented Data Collection, p.125) as part of the evaluation of the system.

4.2.1.2 Tacita

In addition to synthetic synthetic analytics as a source for capturing and generating mobility traces, we also consider data captured from Tacita. The dataset resulting from Tacita has been initially described in Section3.4.1.4(Opportunities for Data Collection, p.71). In particular, we are able to capture the following insights: configuration parameters of Trusted Content

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Providers (i.e. the viewers’ personalisation preferences), display sightings of viewers (i.e. viewers are detected in proximity of displays), and the Trusted Content Provider that was shown on the display when the viewers were detected in proximity of the display. We note that we have conducted a long-term, large-scale trial that will be described in more detail in Section6.2(Pheme: Display-oriented Data Collection, p.125). The dataset emerging from Tacita is used to create similar types of analytics reports and, in addition, a set of reports that are specific to display networks that support the delivery of personalised content.

4.2.1.3 Comparing the Applicability of Synthetic Analytics and Tacita

Both synthetic analytics and Tacita have advantages and disadvantages as potential sources for viewer mobility data. Whilst both approaches can be used to underpin the same categories of analytics reports (as we illustrate in the subsequent sections), the data capture process differs significantly and each of the approaches are applicable in certain contexts.

The synthetic analytics approach is particularly practical if certain technical, legal or ethical constraints prevent space owners from applying viewer- or infrastructure-based tracking mechanisms. Synthetic analytics relies purely on appropriate viewer mobility models and does not require additional hardware to be deployed. If, for example, the deployment of Bluetooth Low Energy beacons is impractical, synthetic analytics can be used as a solution in order to gain insights on interactions and movement patterns. Whilst we applied synthetic analytics to capture display sightings of viewers, additional information can be encoded in the model to capture a broader set of insights – such as more broad behaviour and navigation patterns that go beyond simple display sightings and provide further insights on peoples’ interactions across a space.

In contrast, Tacita provides insights that are potentially of higher accuracy as display sightings are based on Bluetooth Low Energy sightings instead of a synthetic model of user mobility. Tacita also presumes that viewers (or users of the display personalisation system) have explicitly opted in to the required display proximity tracking – leaving it up to the user to decide whether to contribute data to the system (an opt-in and opt-out in the synthetic analytics approach does not make sense). The use of Tacita, or more generally viewer-based analytics tracking approaches, allows for additional features such as personalisation as described in previous chapter to provide a visible benefit to the user in exchange for location tracking. We note, however, that the deployment of a system such as Tacita requires substantial investment from display and space owners: displays need to be equipped with appropriate hardware infrastructure (e.g. Bluetooth Low Energy beacons), and a corresponding mobile phone application and backend components are required to allow for capture and reporting of user locations in the space.