Reducing and linking spatio-temporal datasets with kD-STR

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Liam Steadman
  • Nathan Griffiths
  • Mark Bell
  • Shaun Helman
  • Caroline Wallbank

Colleges, School and Institutes

External organisations

  • University of Warwick
  • Trl

Abstract

When linking spatio-temporal datasets, the kD-STR algorithm can be used to reduce the datasets and speed up the linking process. However, kD-STR can sacrifice accuracy in the linked dataset whilst retaining unnecessary information. To overcome this, we propose a preprocessing step that removes unnecessary information and an alternative heuristic for kD-STR that prioritises accuracy in the linked output. These are evaluated in a case study linking a road condition dataset with air temperature, rainfall and road traffic data. In this case study, we found the alternative heuristic achieved a 19% improvement in mean error for the linked air temperature features and an 18% reduction in storage used for the rainfall dataset compared to the original kD-STR heuristic. The results in this paper support our hypothesis that, at worse, our alternative heuristic will yield a similar error and storage overhead for linking scenarios as the original kD-STR heuristic. However, in some cases it can give a reduction that is more accurate when linking the datasets whilst using less storage than the original kD-STR algorithm.

Bibliographic note

Funding Information: The lead author gratefully acknowledges funding by the UK Engineering and Physical Sciences Research Council (grant no. EP/L016400/1), the EPSRC Centre for Doctoral Training in Urban Science, and funding from TRL. Publisher Copyright: © 2020 ACM.

Details

Original languageEnglish
Title of host publicationProceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020
EditorsBandana Kar, Xinyue Ye, Shima Mohebbi, Guangtao Fu
Publication statusPublished - 3 Nov 2020
Event3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020 - Seattle. Virtual, United States
Duration: 3 Nov 2020 → …

Publication series

NameProceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020

Conference

Conference3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020
Country/TerritoryUnited States
CitySeattle. Virtual
Period3/11/20 → …

Keywords

  • data reduction, kD-STR, modelling, partitioning, spatio-temporal data