Abstract
The increase of high-resolution spatial data and methodological developments in recent years has enabled a detailed analysis of individuals' experience in space and over time. However, despite the increasing availability of data and technological advances, such individual-level analysis is not always possible in practice because of its computing requirements. To overcome this limitation, there has been a considerable amount of research on the use of high-performance, public cloud computing platforms for spatial analysis and simulation. In this paper, we aim to evaluate the efficiency of spatial analysis in cloud computing platforms. We compared the computing speed for calculating the Moran's I index between a local machine and spot instances on clouds, and our results demonstrated that there could be significant improvements in terms of computing time when the analysis was performed parallel on clouds.
| Original language | English |
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| Title of host publication | 10th International Conference on Geographic Information Science, GIScience 2018 |
| Editors | Amy L. Griffin, Stephan Winter, Monika Sester |
| Publisher | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |
| ISBN (Print) | 9783959770835 |
| DOIs | |
| Publication status | Published - 1 Aug 2018 |
| Event | 10th International Conference on Geographic Information Science, GIScience 2018 - Melbourne, Australia Duration: 28 Aug 2018 → 31 Aug 2018 |
Publication series
| Name | Leibniz International Proceedings in Informatics, LIPIcs |
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| Volume | 114 |
| ISSN (Print) | 1868-8969 |
Conference
| Conference | 10th International Conference on Geographic Information Science, GIScience 2018 |
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| Country/Territory | Australia |
| City | Melbourne |
| Period | 28/08/18 → 31/08/18 |
Bibliographical note
Publisher Copyright:© Changlock Choi, Yelin Kim, Youngho Lee, and Seong-Yun Hong.
Keywords
- Cloud services
- Parallel computing
- Spatial analysis