Big Data Computing for Geospatial Applications

• Other Authors: Li, Zhenlong (Editor), Tang, Wenwu (Editor), Huang, Qunying (Editor), Shook, Eric (Editor), Guan, Qingfeng (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.