Spatial data handling in big data era : select papers from the 17th IGU Spatial Data Handling Symposium 2016
- 作者： International Symposium on Spatial Data Handling (17th : 2016 : Beijing, China)
- 其他作者：
- 其他題名：
- Advances in geographic information science.
- 出版： Singapore : Springer Singapore :Imprint: Springer
- 叢書名： Advances in geographic information science,
- 主題： Geographic information systems--Congresses. , Geography. , Geographical Information Systems/Cartography. , Data Mining and Knowledge Discovery. , Data Storage Representation. , Earth Sciences, general.
- ISBN： 9789811044243 (electronic bk.) 、 9789811044236 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Big geographical data storage and search -- Data-intensive geospatial computing and data mining -- Visualization of big geographical data -- Multi-scale spatial data representations, data structures and algorithms -- Space-time modelling and analysi -- Geological applications of Big Data and multi-criteria decision analysis.
- 摘要註: This proceedings volume introduces recent work on the storage, retrieval and visualization of spatial Big Data, data-intensive geospatial computing and related data quality issues. Further, it addresses traditional topics such as multi-scale spatial data representations, knowledge discovery, space-time modeling, and geological applications. Spatial analysis and data mining are increasingly facing the challenges of Big Data as more and more types of crowd sourcing spatial data are used in GIScience, such as movement trajectories, cellular phone calls, and social networks. In order to effectively manage these massive data collections, new methods and algorithms are called for. The book highlights state-of-the-art advances in the handling and application of spatial data, especially spatial Big Data, offering a cutting-edge reference guide for graduate students, researchers and practitioners in the field of GIScience.
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讀者標籤：
- 系統號： 005399610 | 機讀編目格式
館藏資訊
This proceedings volume introduces recent work on the storage, retrieval and visualization of spatial Big Data, data-intensive geospatial computing and related data quality issues. Further, it addresses traditional topics such as multi-scale spatial data representations, knowledge discovery, space-time modeling, and geological applications. Spatial analysis and data mining are increasingly facing the challenges of Big Data as more and more types of crowd sourcing spatial data are used in GIScience, such as movement trajectories, cellular phone calls, and social networks. In order to effectively manage these massive data collections, new methods and algorithms are called for. The book highlights state-of-the-art advances in the handling and application of spatial data, especially spatial Big Data, offering a cutting-edge reference guide for graduate students, researchers and practitioners in the field of GIScience.