Big Data Computing for Geospatial Applications
Li, Zhenlong
Big Data Computing for Geospatial Applications - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (222 p.)
Open Access
The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.
Creative Commons
English
books978-3-03943-245-5 9783039432448 9783039432455
10.3390/books978-3-03943-245-5 doi
Research & information: general
Geography
task workflow geospatial problem-solving knowledge base social media big data fine-grained emotion classification spatio-temporal analysis hazard mitigation missing road city blocks topology big mobile navigation trajectory data geographic knowledge representation geographic knowledge graph formalization GeoKG overlay analysis shape complexity massive data cloud parallel computing geovisual analytics machine learning smart card data transit corridor mobility community trip CA Markov land-use change prediction Hadoop MapReduce cloud computing ETL ELT sensor data IoT geospatial big data climate science metadata web cataloging service big geospatial data geospatial cyberinfrastructure topographic surface terrain modeling global terrain dataset geospatial computing cyberGIS GeoAI spatial thinking
Big Data Computing for Geospatial Applications - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (222 p.)
Open Access
The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.
Creative Commons
English
books978-3-03943-245-5 9783039432448 9783039432455
10.3390/books978-3-03943-245-5 doi
Research & information: general
Geography
task workflow geospatial problem-solving knowledge base social media big data fine-grained emotion classification spatio-temporal analysis hazard mitigation missing road city blocks topology big mobile navigation trajectory data geographic knowledge representation geographic knowledge graph formalization GeoKG overlay analysis shape complexity massive data cloud parallel computing geovisual analytics machine learning smart card data transit corridor mobility community trip CA Markov land-use change prediction Hadoop MapReduce cloud computing ETL ELT sensor data IoT geospatial big data climate science metadata web cataloging service big geospatial data geospatial cyberinfrastructure topographic surface terrain modeling global terrain dataset geospatial computing cyberGIS GeoAI spatial thinking