  The global sports analytics industry has a market value of USD 3.78 billion
in 2023. The increase of wearables such as GPS sensors has provided analysts
with large fine-grained datasets detailing player performance. Traditional
analysis of this data focuses on individual athletes with measures of internal
and external loading such as distance covered in speed zones or rate of
perceived exertion. However these metrics do not provide enough information to
understand team dynamics within field sports. The spatio-temporal nature of
match play necessitates an investment in date-engineering to adequately
transform the data into a suitable format to extract features such as areas of
activity. In this paper we present an approach to construct Time-Window Spatial
Activity Graphs (TWGs) for field sports. Using GPS data obtained from Gaelic
Football matches we demonstrate how our approach can be utilised to extract
spatio-temporal features from GPS sensor data
