In urban planning, one of the common units of measurement for population density is the amount of households per hectare. However, the actual size of the households is seldom considered, neither in 2D nor in 3D. This thesis proposes a method to calculate the average size of the households from existing urban areas based on available open data and to use it as a design parameter for new urban developments. The proposed unit of measurement is comprehensive of outdoor and indoor spaces, the latter comprising both residential and non-residential space. As a test case, the Haven-Stad project in Amsterdam was chosen, specifically its second phase, Sloterdijk One, envisioned by the municipality of Amsterdam to host 11220 households and 7480 working places by 2040. The sizes of typical households, as well as a series of other KPI’s (Key Performance Indicators) were computed in different neighbourhoods of Amsterdam based on their similarities with the vision of Sloterdijk One. In the second part of this thesis, the resulting size of the households were used as a design parameter in a custom-made prototype tool to generate semi-automatically several design proposals (called scenarios) for urban developments. Additionally, each scenario can be converted into a Geo format to visualize it in web-based virtual platforms such as Google Earth and Cesium JS. Significant differences among the resulting design proposals based on this new unit of measurement were encountered, meaning that the average size of a household plays indeed a major role.

Every day, terabytes of information is generated, filling storage devices around the world. However,the human brain have limited capacities to read and understand raw data from a computer screen.That is why data specialists need to ingeniously create better ways to display, process and analyzemassive amounts of data.Our research project is not about avoiding subsidence, not even about cracks on buildings; it ispurely data analysis and interpretation. This study will help professionals understand and fightagainst building subsidence. Our task was to create, manipulate and make sense of charts like theone below (a real line graph from InSAR data), then translate them into useful information forstakeholders in the local, national and global community.The aim of the project was to understand if ground sensor technologies are comparable to othersources of information. In our analysis different strategies to analyze building subsidence wereimplemented, e.g. homogeneous subsidence, heterogeneous subsidence and for water levels,interpolation and cross correlation methods. In addition, other techniques like sensor fusing wereimplemented to compare data from different sources.As a result from all these strategies, we can say that the water level sensors placed in our researchbuilding, have a high similarity with citizens and municipality data. In contrast, InSAR data is notcomparable with the subsidence sensors placed in the building because they have differentreferences and the period of study was too short to get accurate results from satellite data. Finally,an idea for future implementation strategies was proposed. On this idea, measurements of levelscan be carried out taking as a reference the NAP level and comparing the subsidence between ahealthy-foundations building and another one with wooden-piles foundation.