  Analysing and learning from spatio-temporal datasets is an important process
in many domains, including transportation, healthcare and meteorology. In
particular, data collected by sensors in the environment allows us to
understand and model the processes acting within the environment. Recently, the
volume of spatio-temporal data collected has increased significantly,
presenting several challenges for data scientists. Methods are therefore needed
to reduce the quantity of data that needs to be processed in order to analyse
and learn from spatio-temporal datasets. In this paper, we present the
k-Dimensional Spatio-Temporal Reduction method (kD-STR) for reducing the
quantity of data used to store a dataset whilst enabling multiple types of
analysis on the reduced dataset. kD-STR uses hierarchical partitioning to find
spatio-temporal regions of similar instances and models the instances within
each region to summarise the dataset. We demonstrate the generality of kD-STR
with 3 datasets exhibiting different spatio-temporal characteristics and
present results for a range of data modelling techniques. Finally, we compare
kD-STR with other techniques for reducing the volume of spatio-temporal data.
Our results demonstrate that kD-STR is effective in reducing spatio-temporal
data and generalises to datasets that exhibit different properties.
