The continuous improvement of spatial design has always been the aim of architectural, construction and engineers (AECD practitioners) as well as the real estate sector. The necessity to understand and meet the needs of the users drives practitioners to analyse and innovate when designing new spaces or refurbishing the current ones. On the other side, the market, due to its endless search for profit maximization and spatial optimisation, also enhances this race for human-driven spatial optimization. The role of technology in spatial analysis has added a new dimension, bringing new resources, like data analytics, to drive spatial performance assessment by providing insights to evaluate the use, operations, and wellbeing of users within spaces.The main contribution of this thesis is the design of a tool, in service of architects, to quantify the possibilities of use, dimensioning, occupancy and relationships between people and in spaces. The tool evaluates the existing technological infrastructure in a space or building with the aim of understanding what data can be obtained from the space under study, and how this data may or may not contribute to the spatial analysis providing insights to enable building performance analysis, environmental footprint analysis or to improve the user experience. All this is according to the objectives set for this space.The methodology created for the tool is based on a classification of the existing data sources in the building, their categorisation according to the information provided and the description of the data that the sources provide to measure parameters such as occupancy, space usage patterns, user satisfaction or the level of comfort and emissions. By applying the methodology, the tool assesses the level of digital maturity of the building in its current state. This allows us to understand how to assess and improve the design of future spaces based on the results of the analysis. The tool is corroborated through the implementation of two pilot studies.Using data analysis to evaluate the use of active spaces has received little attention. It is just now when data analysis is being more common and the need to improve spaces, rise performance and reduce expenditure that Post-Occupancy Evaluation (POE) methods and tools like the current research are rapidly escalating. Until now, technology in the spaces was mostly implemented to improve the construction process of the buildings (like automation of building