Spatial Data Science: Closing the human-spatial computing-environment loop

TL;DR

This paper discusses the evolving field of spatial computing, highlighting its definitions, technological advances, and the potential of spatial data science to unify research and practical problem-solving in real-world contexts.

Contribution

It proposes viewing spatial data science as a unifying framework that connects spatial computing technologies with real-world problem-solving and learning.

Findings

Spatial computing has dual definitions in industry and academia.

Spatial data science can serve as a cohesive discipline linking technology and real-world applications.

The field is progressing without the need for new research labels.

Abstract

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.