Evaluating Computational Efficiency of Spatial Analysis in Cloud Computing Platforms

클라우드 컴퓨팅 기반 공간분석의 연산 효율성 분석

  • 최창락 (경희대학교 이과대학 지리학과) ;
  • 김예린 (경희대학교 이과대학 지리학과) ;
  • 홍성연 (경희대학교 이과대학 지리학과)
  • Received : 2018.10.25
  • Accepted : 2018.12.13
  • Published : 2018.12.31


The increase of high-resolution spatial data and methodological developments in recent years has enabled a detailed analysis of individual experiences 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. The purpose of this paper is to empirically evaluate the efficiency and effectiveness of spatial analysis in cloud computing platforms. We compare the computing speed for calculating the measure of spatial autocorrelation and performing geographically weighted regression analysis between a local machine and spot instances on clouds. The results indicate that there could be significant improvements in terms of computing time when the analysis is performed parallel on clouds.


cloud computing;parallel computing;spatial analysis;spatial data;efficiency;effectiveness


Supported by : 한국연구재단


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