Assessment and testing are among the biggest challenges for the release of automated driving. Up to this date, the exact procedure to achieve homologation is not settled. Current research focuses on scenario-based approaches that represent driving scenarios as test cases within a scenario space. This avoids redundancies in testing, enables the inclusion of virtual testing into the process, and makes a statement about test coverage possible. However, it is unclear how to define such a scenario space and the coverage criterion.This work presents a novel approach to the definition of the scenario space. Spatiotemporal filtering on naturalistic highway driving data provides a large amount of driving scenarios as a foundation. A custom distance measure between scenarios enables hierarchical agglomerative clustering, categorizing the scenarios into subspaces. The members of a resulting cluster found through this approach reveal a common structure that is visually observable. We discuss a data-driven solution to define the necessary test coverage for the assessment of automated driving. Finally, the contribution of the findings to achieve homologation is elaborated.