The aim of this thesis was to research the possibilities of classifying road risks by combining datasets from different geographical data sources and merging these sets into one final dataset. The purpose of this dataset is to serve as data input for a data analytics solution containing one or more map visualisations that can be used to analyse road risks. The commissioning party is the HAMK Smart Research unit and a potential client in the logistics sector was mentioned.

In order to research the possibilities of this geographical data, a thorough literary research was performed. The results of this research were then used to create such a dataset and was tested in the form of a basic data analytics solution. The merged dataset was created using the RStudio toolchain and the analytics solution was created in Power BI Desktop.

This resulted in a compact, but clean and efficient dataset, accompanied by a functioning analytics solution. This solution remains just a demo but could already be used to draw some conclusions about road safety. This proves that classifying road risks by using geographical data from different sources is feasible. However, in order to make this process more optimal, other technologies than the ones used in this thesis should also be researched.