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Big Data Computing for Geospatial Applications
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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.
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Keywords
- big data
- big geospatial data
- big mobile navigation trajectory data
- CA Markov
- city blocks
- climate science
- cloud
- Cloud computing
- cyberGIS
- ELT
- ETL
- fine-grained emotion classification
- formalization
- GeoAI
- geographic knowledge graph
- geographic knowledge representation
- Geography
- GeoKG
- geospatial big data
- geospatial computing
- geospatial cyberinfrastructure
- geospatial problem-solving
- geovisual analytics
- global terrain dataset
- Hadoop
- hazard mitigation
- IoT
- knowledge base
- land-use change prediction
- Machine learning
- MapReduce
- massive data
- Metadata
- missing road
- mobility community
- overlay analysis
- parallel computing
- Reference, information & interdisciplinary subjects
- Research & information: general
- sensor data
- shape complexity
- smart card data
- Social media
- spatial thinking
- spatio-temporal analysis
- task
- terrain modeling
- topographic surface
- Topology
- transit corridor
- TRIP
- web cataloging service
- Workflow
Links
DOI: 10.3390/books978-3-03943-245-5Editions