## 3 Evaluation
### Section C: Understanding the Data Visualisations  
This section introduced the participant of the study to the data visualisations selected and adapted to enable the analysis of the data. The data visualisations used in this work have been chosen for displaying key spatial and temporal aspects previously discussed.  
(a) Visualisation 1: This diagram shows a summary of the average percentage of sensor events (monitored resident visits) per time interval of 1 h in a selected location from the dataset. Overall, this visualisation aims to provide an insight into the times of day when the monitored subject is most likely to visit the location selected for the analysis, for example the bedroom in Figure 4. This visualisation uses a dynamic variable, the target location (e.g., the bathroom), which can be manually modified to adapt the information presented.  
(b) Visualisation 2: This graph shows a summary of the average duration of the sensor events (monitored resident visits) at a selected location in 5 min time intervals for a selected time window (Figure 5). This diagram uses three dynamic variables that allow the information presented to be manually adjusted. These variables are the target location, and the start time and the end time of the time window requested for analysis (e.g., bathroom, from 00:00 to 00:25).  
Figure 3: Tube-map visualisation.  
The aim was to use this information in combination with the previous information shown in visualisation 1. Thus, it is possible to determine how often on average the monitored resident visits the selected location and how long he/she spends there on average.  
(c) Visualisation 3: This graph shows the sequence or order of locations that the monitored resident passed through between different days. In order to simplify the content of this graph, no time information about the duration of the events is shown. The time window over which the sequences are drawn can be manually selected by specifying the start and end times, e.g., from 05:00:00 to 06:00:00, Figure 6.  
Figure 4: Side–by–side graph proposed for analysing the average number of sensor events representing the occupancy of a selected location per hour within the 24 h of a day.  
Figure 5: Side–by–side graph proposed for analysing the average duration of the sensor events (monitored resident visits) in a selected location per 5 min within a selected time window.  
(d) Visualisation 4: The aim of this diagram is to show additional temporal information to the previously shown sequences for a selected day. Therefore, a layer of temporal context is added, which can be used to estimate the start time, duration and end time of each event on a selected day (Figure 7). The date can be manually selected, e.g., 12 May 2021.  
After completing the description of these visualisations, participants were asked the following questions to assess the process and the data visualisations evaluated:  
* Would you have been able to identify the identity of the monitored resident or other people from the dataset used?
* Were the data visualisations useful in accepting or refining your preliminary hypothesis?  
Figure 6: Node graph proposed to display the sequence of visits to different locations of a monitored resident.  
Figure 7: 24 h clock visualisation proposed for the study of temporal information associated with the daily routine of a monitored resident.  
Could you please explain why the information provided through this process is important and could be useful to you, based on your personal or professional experience?  
To conclude, volunteers were given the option to submit further comments or suggestions. Their answers and feedback are discussed in the following section.