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

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