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Books > Earth & environment > Geography > Cartography, geodesy & geographic information systems (GIS) > Remote sensing
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The Rise of Big Spatial Data (Paperback, Softcover reprint of the original 1st ed. 2017)
Loot Price: R6,521
Discovery Miles 65 210
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The Rise of Big Spatial Data (Paperback, Softcover reprint of the original 1st ed. 2017)
Series: Lecture Notes in Geoinformation and Cartography
Expected to ship within 10 - 15 working days
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This edited volume gathers the proceedings of the Symposium GIS
Ostrava 2016, the Rise of Big Spatial Data, held at the Technical
University of Ostrava, Czech Republic, March 16-18, 2016. Combining
theoretical papers and applications by authors from around the
globe, it summarises the latest research findings in the area of
big spatial data and key problems related to its utilisation.
Welcome to dawn of the big data era: though it's in sight, it isn't
quite here yet. Big spatial data is characterised by three main
features: volume beyond the limit of usual geo-processing, velocity
higher than that available using conventional processes, and
variety, combining more diverse geodata sources than usual. The
popular term denotes a situation in which one or more of these key
properties reaches a point at which traditional methods for geodata
collection, storage, processing, control, analysis, modelling,
validation and visualisation fail to provide effective solutions.
>Entering the era of big spatial data calls for finding
solutions that address all "small data" issues that soon create
"big data" troubles. Resilience for big spatial data means solving
the heterogeneity of spatial data sources (in topics, purpose,
completeness, guarantee, licensing, coverage etc.), large volumes
(from gigabytes to terabytes and more), undue complexity of
geo-applications and systems (i.e. combination of standalone
applications with web services, mobile platforms and sensor
networks), neglected automation of geodata preparation (i.e.
harmonisation, fusion), insufficient control of geodata collection
and distribution processes (i.e. scarcity and poor quality of
metadata and metadata systems), limited analytical tool capacity
(i.e. domination of traditional causal-driven analysis), low visual
system performance, inefficient knowledge-discovery techniques (for
transformation of vast amounts of information into tiny and
essential outputs) and much more. These trends are accelerating as
sensors become more ubiquitous around the world.
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