  The importance of human mobility analyses is growing in both research and
practice, especially as applications for urban planning and mobility rely on
them. Aggregate statistics and visualizations play an essential role as
building blocks of data explorations and summary reports, the latter being
increasingly released to third parties such as municipal administrations or in
the context of citizen participation. However, such explorations already pose a
threat to privacy as they reveal potentially sensitive location information,
and thus should not be shared without further privacy measures.
  There is a substantial gap between state-of-the-art research on privacy
methods and their utilization in practice. We thus conceptualize a standardized
mobility report with differential privacy guarantees and implement it as
open-source software to enable a privacy-preserving exploration of key aspects
of mobility data in an easily accessible way. Moreover, we evaluate the
benefits of limiting user contributions using three data sets relevant to
research and practice. Our results show that even a strong limit on user
contribution alters the original geospatial distribution only within a
comparatively small range, while significantly reducing the error introduced by
adding noise to achieve privacy guarantees.
