Reducing and linking spatio-temporal datasets with kD-STR

Liam Steadman; Nathan Griffiths; Stephen Jarvis; Mark Bell; Shaun Helman; Caroline Wallbank

doi:10.1145/3423455.3430317

Reducing and linking spatio-temporal datasets with kD-STR

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

Engineering and Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020
Editors	Bandana Kar, Xinyue Ye, Shima Mohebbi, Guangtao Fu
Publisher	Association for Computing Machinery
Pages	10-19
Number of pages	10
ISBN (Electronic)	9781450381659
DOIs	https://doi.org/10.1145/3423455.3430317
Publication status	Published - 3 Nov 2020
Event	3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020 - Seattle. Virtual, United States Duration: 3 Nov 2020 → …

Publication series

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

Conference

Conference	3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020
Country/Territory	United States
City	Seattle. Virtual
Period	3/11/20 → …

Bibliographical 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.

Keywords

data reduction
kD-STR
modelling
partitioning
spatio-temporal data

ASJC Scopus subject areas

Building and Construction
Computer Networks and Communications
Information Systems
Civil and Structural Engineering

Access to Document

10.1145/3423455.3430317

Cite this

Steadman, L., Griffiths, N., Jarvis, S., Bell, M., Helman, S., & Wallbank, C. (2020). Reducing and linking spatio-temporal datasets with kD-STR. In B. Kar, X. Ye, S. Mohebbi, & G. Fu (Eds.), Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020 (pp. 10-19). (Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020). Association for Computing Machinery . https://doi.org/10.1145/3423455.3430317

Steadman, Liam ; Griffiths, Nathan ; Jarvis, Stephen et al. / Reducing and linking spatio-temporal datasets with kD-STR. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020. editor / Bandana Kar ; Xinyue Ye ; Shima Mohebbi ; Guangtao Fu. Association for Computing Machinery , 2020. pp. 10-19 (Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020).

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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.",

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Steadman, L, Griffiths, N, Jarvis, S, Bell, M, Helman, S & Wallbank, C 2020, Reducing and linking spatio-temporal datasets with kD-STR. in B Kar, X Ye, S Mohebbi & G Fu (eds), Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020, Association for Computing Machinery , pp. 10-19, 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020, Seattle. Virtual, United States, 3/11/20. https://doi.org/10.1145/3423455.3430317

Reducing and linking spatio-temporal datasets with kD-STR. / Steadman, Liam; Griffiths, Nathan; Jarvis, Stephen et al.
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020. ed. / Bandana Kar; Xinyue Ye; Shima Mohebbi; Guangtao Fu. Association for Computing Machinery , 2020. p. 10-19 (Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Griffiths, Nathan

AU - Jarvis, Stephen

AU - Bell, Mark

AU - Helman, Shaun

AU - Wallbank, Caroline

N1 - 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.

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N2 - 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.

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Steadman L, Griffiths N, Jarvis S, Bell M, Helman S, Wallbank C. Reducing and linking spatio-temporal datasets with kD-STR. In Kar B, Ye X, Mohebbi S, Fu G, editors, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020. Association for Computing Machinery . 2020. p. 10-19. (Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2020). doi: 10.1145/3423455.3430317

Reducing and linking spatio-temporal datasets with kD-STR

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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