I am a Master student in Engineering and Policy Analysis in the Technology, Policy and Management Faculty at TU Delft. During my Bachelor, my main focus was on sustainable energy development and system dynamics. Now, during my master, my fascination broadened to social data science, where data and people come together. On the one side I believe that data can provide us with meaningful information about human behaviour, which might help us gain some insight in how to help others. On the other side I think that it is nearly impossible to represent human behaviour with data, seeing that there are many circumstantial drivers that influence how people react in different situations. And that word ’nearly’ is what strikes me to find where we cán actually find relevant patterns in human behaviour.

One such situation in which analysis of human behaviour can lead to meaningful pattern recognition is around disasters. For my Master Thesis I am researching patterns that can be found in the spatiotemporal moving behaviour of populations after a flood. Here, I am exploring the possibilities of using Call Detail Records from mobile phones to find drivers that tell us something about displaced persons. How many people will leave their houses? Where do they move to? For how long? And is there a way we can guide them to saver places? Answering these kinds of questions could help organisations like the Red Cross Foundation to improve their Early Warning Early Action programs. I work together with 510, an initiative of the Red Cross with the mission of shaping future humanitarian aid by converting data into understanding. And also I work together with Flowminder, an organisation with the mission of creating a world in which the lives of the most vulnerable are improved by the best data systems. Both these organisations have visions that I personally feel strongly about, motivating me to work hard on the Thesis project.