{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T16:36:20Z","timestamp":1769877380438,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,14]],"date-time":"2016-07-14T00:00:00Z","timestamp":1468454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Sciences Foundation of China","award":["41471374"],"award-info":[{"award-number":["41471374"]}]},{"name":"National Sciences Foundation of China","award":["41001296"],"award-info":[{"award-number":["41001296"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This paper addresses how to manage planar spatial data using MongoDB, a popular NoSQL database characterized as a document-oriented, rich query language and high availability. The core idea is to flatten a hierarchical R-tree structure into a tabular MongoDB collection, during which R-tree nodes are represented as collection documents and R-tree pointers are expressed as document identifiers. By following this strategy, a storage schema to support R-tree-based create, read, update, and delete (CRUD) operations is designed and a module to manage planar spatial data by consuming and maintaining flattened R-tree structure is developed. The R-tree module is then seamlessly integrated into MongoDB, so that users could manipulate planar spatial data with existing command interfaces oriented to geodetic spatial data. The experimental evaluation, using real-world datasets with diverse coverage, types, and sizes, shows that planar spatial data can be effectively managed by MongoDB with our flattened R-tree and, therefore, the application extent of MongoDB will be greatly enlarged. Our work resulted in a MongoDB branch with R-tree support, which has been released on GitHub for open access.<\/jats:p>","DOI":"10.3390\/ijgi5070119","type":"journal-article","created":{"date-parts":[[2016,7,14]],"date-time":"2016-07-14T10:25:08Z","timestamp":1468491908000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A MongoDB-Based Management of Planar Spatial Data with a Flattened R-Tree"],"prefix":"10.3390","volume":"5","author":[{"given":"Longgang","family":"Xiang","sequence":"first","affiliation":[{"name":"State Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"Juntao","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, China"}]},{"given":"Xiaotian","family":"Shao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, China"}]},{"given":"Dehao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1126\/science.1197962","article-title":"Challenges and opportunities of open data in ecology","volume":"331","author":"Reichman","year":"2011","journal-title":"Science"},{"key":"ref_2","first-page":"1","article-title":"Big data: Big gaps of knowledge in the field of Internet","volume":"7","author":"Snijders","year":"2012","journal-title":"Int. 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