Data analysis, processing and interpretation from different sources

Abstract

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.