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Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users' quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.

Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users' quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.

Genome sequencing, assembly and annotation were conducted by the Novogene Bioinformatics Institute, Beijing, China; mutual contracts were No. NHT140016 and NVT140016004. This work was supported by funding from the Scientific Project of Shenzhen Urban Administration (201519) and a Major Technical Research Project of the Innovation of Science and Technology Commission of Shenzhen (JSGG20140515164852417). Additional funding was provided in particular by the Scientific Research Program of Sino-Africa Joint Research Center (SAJL201607). We thank X.Q. Wang, G.W. Hu, Z.D. Chen and Y.H. Guo for comments on gnetophyte phylogenetic relationships and ecological issues; H. Wu and X.P. Ning for discussion of related organ development; K.K. Wan and S. Sun for additional help on the analysis of repeats. We also thank X.Y. for support of funding coordination. Y.V.d.P. acknowledges the Multidisciplinary Research Partnership 'Bioinformatics: from nucleotides to networks' Project (no. 01MR0310W) of Ghent University, and funding from the European Union Seventh Framework Programme (FP7/2007-2013) under European Research Council Advanced Grant Agreement 322739-DOUBLEUP.