**3. Results**

#### *3.1. Phase A: Electricity Analysis*

Four spaces with a total count of 1,047,498 inputs were assessed: 1C (central cluster), 2I, 2D (lateral clusters) and 6C (community space, coworking and social life). Figure 4 shows the average electricity consumption per space: a clear difference in trends is highlighted for the cluster spaces (1C, 2D, 2I). The double peak linear trend showed an increase of the intensity during lunch and dinner times, particularly high in 1C. The Cluster Central was associated with having larger common spaces accessible to the whole community; colivers used this space for shared dinners with other colivers. Instead, the study identified that Community Coworking 6C showed a very different trend, with a single smooth one-lump shape with a peak at 6 pm that corresponded to a different use of this space. Figure 5 focused on 6C during the complete year: the electricity consumption shows a sensible increase from 9:00 to 23:00 in a plateau shape due to moderate electricity consumption related to use of laptops and lighting during autumn, winter, and spring seasons. During the summer season, the plateau shape stressed to a peak shape during the afternoons impacted by the use of air-conditioning (AC) because of the western orientation of the space that increased the temperature.

**Figure 4.** Average yearly electricity consumption per cluster (Average Wh/per hour) data records from 1 June 2020 to 1 June 2021. Visualization with PowerBI.

Figure 6 compares weekdays to weekend days. Cluster kitchens were being used more during weekdays than during weekends, especially dinners (Friday, Saturday, Sunday). Lateral clusters 2D and 2I and the Community Coworking 6C showed the only trend of a later start in activities from 9 h during the weekdays, up to 13 h during Saturday and Sunday.

#### *3.2. Phase B: Access Analysis*

In total, 75 different doors of the building (75 smart locks) were monitored; the spaces had different levels of access depending on the time of day (Table 2). The data respond to the different spaces: 1C Central Cluster with common kitchen, 2D & 2I Lateral Cluster with small kitchen, 6C Community Coworking and the other community spaces were also considered for this filtering.

Data for 6C (community space for a coworking) were missing for Mondays, Tuesdays and Wednesday; this is a sample of colivers' interaction with space as they decided to leave that door open during the day for its constant use as a coworking and meeting space.

Each coliver visits an average of 3.58 shared spaces (excluding private studios) from the coliving space, apart from their own studio, with a range that goes from one to eight shared spaces (median of 3) per coliver. Table 5 shows that among all their favorite spaces, the most visited is the Gym(0I), being used by 68% of the inhabitants. After the gym, the central apartments (1C) are the most popular, despite hosting 25% of the private studios and colivers, visited by 67% members. These central apartments with larger kitchens and commons spaces act as a catalyst of communal activities such as dinners, reinforced in the electricity consumption records.

**Table 5.** Percentage of colivers visiting the different shared spaces per weekday, "S" is the % of colivers that visited a space at least once during the sample period. Entrance corresponds to the main gate of the building, therefore 100% of colivers transit it. Visualization from PowerBI. (The numbers are the ordinal representation of the weekdays 1 = Monday, 2 Tuesday ... ). 1 May 2021 to 1 June 2021.


Table 6 shows the entries to the spaces. The number of colivers that share one cluster varies from 3 to 6. The most popular clusters visited were the central clusters "C" with the larger kitchens and living spaces. 1C was the most popular space with up to 31 colivers, 43% of the sample community visiting the space at least once—the average number of colivers that visit common spaces is 23% (1C, 2D, 2I). Figure 7 shows the habits and patterns of the visits, when and what are the most visited spaces and the comparison between weekdays when mobility increases within the Coliving from 8 to 9 am and at 8 pm and especially at the gym(0I) and weekends when the overall activity decreases and is concentrated opposingly during night hours and late morning.

**Figure 5.** *Cont*.

**Figure 5.** Seasonal electricity consumption. Electricity consumption per month (**a**) Community Coworking 6C (**b**) 1C Cluster Central Space (**c**) 2D, 2I Cluster lateral spaces in Community Space 6C (Average Wh/per hour) data records from 1 June 2020 to 1 June 2021. Visualization with PowerBI.

**Table 6.** Number of colivers visiting each space from 1 May 2021 to 1 June 2021. Visualization from PowerBI.


**Figure 6.** Average yearly electricity consumption per weekday (Average Wh/per hour) data records from 1 June 2020 to 1 June 2021. Visualization with PowerBI. (**a**) 1C Central Cluster with common kitchen, (**b**) 2D & 2I Lateral Cluster with small kitchen, (**c**) 6C Community Coworking space.

**Figure 7.** *Cont*.

**Figure 7.** (**a**) Number of visitors per common space weekdays. (**b**) Visiting routines weekdays. (**c**) Number of visitors per common space weekends (**d**) Visiting routines weekends. (**e**) Visiting routines of the gym. (TAG is every entry by a coliver) from 1 May 2021 to 1 June 2021. Visualization from PowerBI.

#### *3.3. Phase C: Network Cross-Check*

The Wi-Fi had the advantage of seamlessly capturing data visualization of the colivers mobile devices (laptop, mobile phone, tablet, watch) in each space, enabling the identification of different behavioral routines depending on the space. Figure 8 represents the devices seen per space and per hour as a daily average of the month. (a) shows the profile of all the spaces analyzed, (b) focused on the gym, with peak on activity at 1 pm and another peak the afternoon and evening during the weekdays (the night connections were also linked to the use of the common spaces next to the gym that had the Gym SSID

as the closest network, highlighted by colivers, and cross-checked by the Access Analysis). (c) the Community Coworking area and events show a distributed activity starting at 12 pm until night in office working areas and dinner time more frequent during weekdays, but both charts were very different to (d) Cluster spaces 2I, 2D and 2I that all perform at low intensity during the day but peak between 9 pm and 11 pm during the weekdays, dinner and after dinner time, when colivers that are regularly active in their private spaces or common cluster spaces—this input is essential for the spaces profiling as it identifies the spaces clearly by the behavior within them. During the weekends, similarly to the access controls, there was considerably less use of internet and movement within the areas; 50% of connections in the coworking space 6C and even less in the Gym and surroundings and in the private spaces.

**Figure 8.** The graphs represent the daily average total devices seen by the APs per hour in the different spaces. (**a**) Average daily total devices seen per AP. (**b**) Total devices seen at the gym (0I) per hour, (**c**) Total devices seen at the Community space Coworking and events (6C) per hour, (**d**) Total devices seen in the different cluster spaces (2D, 2I, 2I) per hour, Count of De . . . (Count of Device). From 1 May 2021 to 1 June 2021.Visualization developed with Power BI.

#### *3.4. Phase D: Space Profiling*

After concluding Phases A, B and C, the indicators of Table 1 were crosschecked with the space classification Table 4 and synthetized it to develop the SPs. The corresponding author developed the first SP and the co-authors, technology, head of local operations, IT expert, Chief of Operations and Head of Innovation reviewed and complemented the information. It was an iterative process complemented by adding details and helped understanding of the profiling Figure 9.
