kD- STR: a method for spatio-temporal data reduction and modelling

Liam Steadman, Nathan Griffiths, Stephen Jarvis, Mark A. Bell, Shaun Helman, Caroline Wallbank

Research output: Contribution to journalArticlepeer-review


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 article, we present the -Dimensional Spatio-Temporal Reduction method (D-STR) for reducing the quantity of data used to store a dataset whilst enabling multiple types of analysis on the reduced dataset. D-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 D-STR with three datasets exhibiting different spatio-temporal characteristics and present results for a range of data modelling techniques. Finally, we compare D-STR with other techniques for reducing the volume of spatio-temporal data. Our results demonstrate that D-STR is effective in reducing spatio-temporal data and generalises to datasets that exhibit different properties.
Original languageEnglish
Article number17
Number of pages31
JournalACM/IMS Transactions on Data Science
Issue number3
Early online date17 May 2021
Publication statusPublished - Jul 2021


  • Spatio-temporal data
  • data reduction
  • partitioning
  • modelling


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