# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 2 Materials and Methods
### Methodology
#### 2.1.1 Analysis of Spaces via the BIM Model--Phase 1  
The first step in creating a DT was to create a complete BIM model, integrating both architectural and mechanical aspects of the building. The modelling of a multi-tenant building was based on available technical documentation, such as floor plans and the as-built documentation of the installations. The BIM model was enriched with details of the fit-out and coding of the interior spaces, in addition to the simple transposition of the floor plans, information that proved crucial for the spatial analyses in the subsequent phases. Using software such as Autodesk's Revit 2024, a leader in the BIM sector, it is possible to implement a high level of detail and accuracy, integrating structural and non-structural aspects such as the arrangement of furniture and the technical specification of spaces, thus facilitating the entire building management and analysis process [60]. The information in the BIM model is divided into environmental components, such as walls, floors, and roofs, and model components, such as stairs, windows, doors, and furniture. These are described in detail, including dimensions, location, orientation and fabrication, as well as assembly and installation specifications, to a level of detail (LOD) of 400 [61]. The adoption of a detailed LOD in the BIM model has made it possible to accurately classify several categories of data that are critical [62] to SM practices, such as the management of workplace and meeting room reservations. This information is crucial for optimizing the management of available space, improving resource efficiency, and identifying areas of overutilization or underutilization. This level of detail allows for more targeted and effective interventions, ensuring that space and people management responds dynamically to the real needs of the organization [63]. In this phase, the role of IoT sensors becomes evident, as they are capable of detecting motion and occupancy. This functionality enables the programming of devices to facilitate the operation of lighting, the control of HVAC systems, and the activation of pre-programmed energy-saving modes when no one is present in the space. The sensors employed in this project are from Shelly Motion 2, a line of innovative microprocessor-controlled devices that permit the remote control of electrical circuits via a smartphone, personal computer, tablet, or home automation device. This type of sensor is capable of detecting motion, temperature, and brightness. The devices are capable of operating autonomously within a local Wi-Fi network. The sensors can be accessed, controlled, and monitored remotely from any location where the user has Internet connectivity, provided that the devices are connected. The response time of Shelly Motion 2 is 200 m/s, which is equivalent to the blink of an eye. This speed of communication allows the device to alert anyone of any incident in a timely manner. The devices are installed in the vicinity of  
Figure 4: Proposed methodology workflow: outline of phases. the workstation with the objective of detecting the presence or absence of the worker by interpolating data on presence and temperature/humidity. Following the collection of data over a specified period, which is dependent on the number of workstations and employees, as well as the shift cycle, an initial space management analysis can be conducted. This assesses the number of workstations that can be utilized in practice, as opposed to those that remain unused. The data obtained from the sensors are validated through a double comparison with the web app system and the badge recognition system at the building entrance for security purposes. The comparison process is conducted as follows: data from the sensors, which monitor presence parameters, are periodically collected and stored in CSV format. Concurrently, the access data recorded by the badge recognition system and the web app are exported in CSV format.