# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 2 Materials and Methods
### Methodology
#### 2.1.2 Optimizing Space Management through Data Analysis (Statistics)
To optimize data visualization and enrich the user experience, data processed through KNIME were transferred and visualized in dashboards on Microsoft PowerBI [74; 75]. These dashboards were further enriched through the use of BLogic's VCAD, an application that allows the advanced integration and manipulation of visualized data. Figure 5: Proposed data analytics workflow for space management optimization. This integration and synergy between advanced analysis and visualization tools transforms complex datasets into clear and effective visual insights. This has significantly improved not only the understanding of spaces and their usage dynamics, but also the ability to make more informed strategic decisions. Through these technologies, it is possible to quickly identify areas of overutilization or underutilization, optimize the distribution of resources and anticipate future needs, making a decisive contribution to the efficient management of corporate space. Figure 6: Space management web/smart device app for booking stations. Figure 7: Extraction of data from web app. Figure 8: Month view of booking trends. Figure 9: Schedule of national holidays. alerts that can assist them. For example, an alert that warns of a high probability that the occupancy rate for a given day/period will be close to 100% (of the whole building or a specific floor) can allow the building manager to have an optimized number of staff available at any given time. To make the prediction reliable, ML techniques have been used [76]. ML is a variant of the traditional approach to computer programming. Traditional programming involves four basic elements: the source data; the program, i.e., the code to be executed [77]; a computer that executes the code using the source data as input; and the results, i.e., new output data (Figure 10). The traditional method of programming requires the presence of a programmer who is able to encode, i.e., formally describe, the steps required to get from the source data to the result. This requirement is replaced by the ML approach [78], where the machine is asked to \"understand\" how data and results are related. It is conceivable that the machine will be able to evaluate much more data together than a human can, and therefore identify extremely complex relationships between data and results. Sometimes these patterns are unusual, unexpected, or simply unrecognizable to human intelligence. Consequently, in some cases, humans can learn something explicit from the data using ML [79], which gives us the advantage of being able to \"create knowledge\". The goal of an ML approach applied to space management is to forecast the occupancy rate on a given floor (and subsequently, the entire building) for a period of up to 30 days, based on the historical booking data. The initial dataset, which was subsequently subjected to cleansing, comprised booking start and end dates and the booked location code. Through the application of specific algorithms and nodes (Figure 11), the following variables were identified and added:  
* Season (winter, spring, summer, autumn);
* Reference month;  
Figure 10: Training and utilization workflow of machine learning process.