Editors:
- Presents the latest research on the handling of massive data collections
- Introduces new methods, algorithms and applications of spatial data
- Provides an important contribution to the popular topic of Big Data
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Geographic Information Science (AGIS)
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Table of contents (15 chapters)
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Front Matter
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Data Intensive Geospatial Computing and Data Quality
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Front Matter
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Web and Crowd Sourcing Spatial Data Mining
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Front Matter
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Visualization of Big Geographical Data
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Front Matter
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Spatial Analysis and Simulation
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Front Matter
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About this book
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.
Editors and Affiliations
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State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Chenghu Zhou, Fenzhen Su, Jun Xu
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Leibniz Institute for Regional Geography, Leipzig, Germany
Francis Harvey
About the editors
CHENGHU ZHOU received his PhD from the Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, with a focus on Cartography and GIS. He is currently an Academician at the Chinese Academy of Science.
FENZHEN SU completed his PhD in GIS and Cartography at the Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing. He is currently Director of the State Key Lab of Resources and Environmental Information Systems, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
FRANCIS HARVEY completed his PhD at the University of Washington, Seattle, Washington. He has been head of the Department of Cartography and Visual Communication, Leibniz Institute for Regional Geography, since 2015.
JUN XU received his PhD in Geographical Information Systems from the Department of Geography, State University of New York at Buffalo. Her research interests are in the fields of geographical ontology, spatial knowledge representation and qualitative reasoning, and spatial data mining. She is now an Associate Professor at the State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Bibliographic Information
Book Title: Spatial Data Handling in Big Data Era
Book Subtitle: Select Papers from the 17th IGU Spatial Data Handling Symposium 2016
Editors: Chenghu Zhou, Fenzhen Su, Francis Harvey, Jun Xu
Series Title: Advances in Geographic Information Science
DOI: https://doi.org/10.1007/978-981-10-4424-3
Publisher: Springer Singapore
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2017
Hardcover ISBN: 978-981-10-4423-6Published: 12 May 2017
Softcover ISBN: 978-981-13-5133-4Published: 12 December 2018
eBook ISBN: 978-981-10-4424-3Published: 04 May 2017
Series ISSN: 1867-2434
Series E-ISSN: 1867-2442
Edition Number: 1
Number of Pages: XIII, 237
Number of Illustrations: 84 b/w illustrations
Topics: Geographical Information Systems/Cartography, Data Mining and Knowledge Discovery, Data Storage Representation, Earth Sciences, general