  Over the last decade, the term spatial computing has grown to have two
different, though not entirely unrelated, definitions. The first definition of
spatial computing stems from industry, where it refers primarily to new kinds
of augmented, virtual, mixed-reality, and natural user interface technologies.
A second definition coming out of academia takes a broader perspective that
includes active research in geographic information science as well as the
aforementioned novel UI technologies. Both senses reflect an ongoing shift
toward increased interaction with computing interfaces and sensors embedded in
the environment and how the use of these technologies influence how we behave
and make sense of and even change the world we live in. Regardless of the
definition, research in spatial computing is humming along nicely without the
need to identify new research agendas or new labels for communities of
researchers. However, as a field of research, it could be helpful to view
spatial data science as the glue that coheres spatial computing with
problem-solving and learning in the real world into a more holistic discipline.
