  Several datasets exist which contain annotated information of individuals'
trajectories. Such datasets are vital for many real-world applications,
including trajectory prediction and autonomous navigation. One prominent
dataset currently in use is the Stanford Drone Dataset (SDD). Despite its
prominence, discussion surrounding the characteristics of this dataset is
insufficient. We demonstrate how this insufficiency reduces the information
available to users and can impact performance. Our contributions include the
outlining of key characteristics in the SDD, employment of an
information-theoretic measure and custom metric to clearly visualize those
characteristics, the implementation of the PECNet and Y-Net trajectory
prediction models to demonstrate the outlined characteristics' impact on
predictive performance, and lastly we provide a comparison between the SDD and
Intersection Drone (inD) Dataset. Our analysis of the SDD's key characteristics
is important because without adequate information about available datasets a
user's ability to select the most suitable dataset for their methods, to
reproduce one another's results, and to interpret their own results are
hindered. The observations we make through this analysis provide a readily
accessible and interpretable source of information for those planning to use
the SDD. Our intention is to increase the performance and reproducibility of
methods applied to this dataset going forward, while also clearly detailing
less obvious features of the dataset for new users.
