{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:20:17Z","timestamp":1765268417927,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,6]],"date-time":"2017-06-06T00:00:00Z","timestamp":1496707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>With the increase in size and complexity of spatiotemporal data, traditional methods for performing statistical analysis are insufficient for meeting real-time requirements for mining information from Big Data, due to both data- and computing-intensive factors. To solve the Big Data challenges in geostatistics and to support decision-making, a high performance, spatiotemporal statistical analysis system (Geostatistics-Hadoop) is proposed in this paper. The proposed system has several features: (1) Hadoop is enhanced to handle spatial data in a native format and execute a number of parallelized spatial analysis algorithms to solve practical geospatial analysis problems; (2) the Oozie-based workflow system is utilized to ease the operation and sharing of spatial analysis services; and (3) a private cloud platform based on Eucalyptus is leveraged to provide on-the-fly and elastic computing resources. Experimental results show that Geostatistics-Hadoop efficiently conducts rapid information mining and analysis of big spatiotemporal data sets, with the support of elastic computing resources from a cloud platform. The adoption of cloud computing and the Hadoop cluster to parallelize statistical calculations significantly improves the performance of Big Data analyses.<\/jats:p>","DOI":"10.3390\/ijgi6060165","type":"journal-article","created":{"date-parts":[[2017,6,6]],"date-time":"2017-06-06T10:53:09Z","timestamp":1496746389000},"page":"165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A High Performance, Spatiotemporal Statistical Analysis System Based on a Spatiotemporal Cloud Platform"],"prefix":"10.3390","volume":"6","author":[{"given":"Baoxuan","family":"Jin","sequence":"first","affiliation":[{"name":"Yunnan Provincial Geomatics Centre, Kunming 650034, Yunnan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Geoinformation Science, Kunming University of Science and Technology, Kunming 650504, Yunnan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Yunnan Provincial Geomatics Centre, Kunming 650034, Yunnan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Wei","sequence":"additional","affiliation":[{"name":"Yunnan Provincial Geomatics Centre, Kunming 650034, Yunnan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Geography, Eastern China Normal University, Shanghai 200062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongyao","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing Information and Engineering, Wuhan University, Wuhan 430071, Hubei, China"},{"name":"Beijing Yunhe Spatiotemporal Tehnology Co. Ltd, Beijing 100080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1890\/1540-9295(2008)6[282:ACSFTN]2.0.CO;2","article-title":"A continental strategy for the National Ecological Observatory Network","volume":"6","author":"Keller","year":"2008","journal-title":"Front. Ecol. 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