About the project

Although awareness around gathering, storing and managing data has increased in the public sector. At the same time, there is a constant thrive to become more information oriented (data-driven) when it comes to operations, strategic decision making and policy making. This project investigates the possibility of applying novel data science methods to data from the public sector. In particular, it considers data related to infrastructure and the human environment, i.e., transport over roads and water. 

The focus is on the application and development of new methods in two subdisciplines of data science. The first is machine learning, where historic data is used to predict unknown or future aspects from the data. The second is network science, a field of research in which not the data objects within some system are investigated, but instead the interactions between these objects play a central role. For both of these fundamental directions of research, it holds that, either by utilizing historic data or by analyzing emergent behavior at the system level, new and previously unknown insights can be uncovered. The aim is to do so in the context of the aforementioned domain of infrastructure and human environment, leading to more efficient and information-driven operations. 

 

• data science
• machine learning
• network science

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