kD-STR: A Method for Spatio-Temporal Data Reduction and Modelling

TL;DR

kD-STR is a novel hierarchical method that reduces spatio-temporal data volume by partitioning and modeling regions, enabling efficient analysis across diverse datasets and outperforming existing reduction techniques.

Contribution

The paper introduces kD-STR, a new hierarchical approach for reducing spatio-temporal data volume while preserving analysis capabilities, applicable to various datasets.

Findings

kD-STR effectively reduces data volume across different datasets.

kD-STR maintains analysis accuracy after data reduction.

Compared to other methods, kD-STR shows superior performance in data reduction.

Abstract

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.