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Big Data Computing for Geospatial Applications

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 ap...

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Format: Online
Sprog:engelsk
Udgivet: MDPI - Multidisciplinary Digital Publishing Institute 2021
Fag:
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
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography
Online adgang:ONIX_20210501_9783039432448_1075
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Summary: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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