2026-07-24T12:06:04Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9352 2024-03-06T10:44:07Z ddc:004 open_access

Evaluating Efficiency of Spatial Analysis in Cloud Computing Platforms (Short Paper) Choi, Changlock Kim, Yelin Lee, Youngho Hong, Seong-Yun spatial analysis parallel computing cloud services 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Changlock Choi and Yelin Kim and Youngho Lee and Seong-Yun Hong 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.24 urn:nbn:de:0030-drops-93521 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.24 eng https://creativecommons.org/licenses/by/3.0/legalcode