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The Rise of Big Spatial Data

  • Igor Ivan
  • Alex Singleton
  • Jiří Horák
  • Tomáš Inspektor

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Table of contents

  1. Front Matter
    Pages i-xxvii
  2. Jan Ježek, Karel Jedlička, Tomáš Mildorf, Jáchym Kellar, Daniel Beran
    Pages 13-26
  3. Arnaud Palha, Arnadi Murtiyoso, Jean-Christophe Michelin, Emmanuel Alby, Pierre Grussenmeyer
    Pages 27-39
  4. Pablo Fernández, Jose M. Santana, Sebastián Ortega, Agustín Trujillo, Jose P. Suárez, Jaisiel A. Santana et al.
    Pages 41-53
  5. Martin Hubáček, Lucie Almášiová, Karel Dejmal, Eva Mertová
    Pages 69-84
  6. Jiří Horák, Igor Ivan, Tomáš Inspektor, Jan Tesla
    Pages 85-106
  7. Handoko Pramulyo, Agung Budi Harto, Saptomo Handoro Mertotaroeno, Arnadi Murtiyoso
    Pages 107-120
  8. Ľudovít Kovanič, Peter Blišťan, Vladislava Zelizňaková, Jana Palková
    Pages 121-129
  9. Milan Lazecky, Fatma Canaslan Comut, Yuxiao Qin, Daniele Perissin
    Pages 131-139
  10. Jaroslav Hofierka, Michal Gallay, Ján Kaňuk, Ján Šašak
    Pages 141-154
  11. Ville Luoma, Mikko Vastaranta, Kyle Eyvindson, Ville Kankare, Ninni Saarinen, Markus Holopainen et al.
    Pages 155-166
  12. Topi Tanhuanpää, Ninni Saarinen, Ville Kankare, Kimmo Nurminen, Mikko Vastaranta, Eija Honkavaara et al.
    Pages 167-181
  13. Ville Kankare, Markus Holopainen, Mikko Vastaranta, Xinlian Liang, Xiaowei Yu, Harri Kaartinen et al.
    Pages 183-195
  14. Jiří Pánek, Vít Pászto, Lukáš Marek
    Pages 211-224
  15. Ľudmila Jánošíková, Peter Jankovič, Peter Márton
    Pages 225-239
  16. Süleyman Eken, Umut Kizgindere, Ahmet Sayar
    Pages 263-272
  17. Lucie Augustinková, Vladimír Fárek, Jiří Klepek, Aneta Krakovská, Martin Neruda, Iva Ponížilová et al.
    Pages 289-306
  18. Jan Růžička, David Kocich, Lukáš Orčík, Vladislav Svozilík
    Pages 307-314
  19. Václav Čada, Jindra Marvalová, Barbora Musilová
    Pages 315-335
  20. Szymon Szewrański, Jan Kazak, Marta Sylla, Małgorzata Świąder
    Pages 337-349
  21. Jan Růžička, Lukáš Orčík, Kateřina Růžičková, Juraj Kisztner
    Pages 351-358
  22. Karel Janečka, Libor Váša
    Pages 359-372
  23. Lukáš Karell, Milan Muňko, Renata Ďuračiová
    Pages 373-386
  24. Jana Faixová Chalachanová, Renata Ďuračiová, Juraj Papčo, Rastislav Jakuš, Miroslav Blaženec
    Pages 387-405
  25. Back Matter
    Pages 407-408

About these proceedings

Introduction

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.

Keywords

Big Spatial Data Heterogenous Spatial Data Sources Complex Spatial Analysis Spatial Data Mining Visualisation of Big Data

Editors and affiliations

  • Igor Ivan
    • 1
  • Alex Singleton
    • 2
  • Jiří Horák
    • 3
  • Tomáš Inspektor
    • 4
  1. 1.Institute of GeoinformaticsVŠB-Technical University of OstravaOstrava, MoravskoslezskyCzech Republic
  2. 2.Department of Geography and PlanningUniversity of LiverpoolLiverpool, MerseysideUnited Kingdom
  3. 3.Institute of GeoinformaticsVŠB-Technical University of OstravaOstrava, MoravskoslezskyCzech Republic
  4. 4.Institute of GeoinformaticsVŠB-Technical University of OstravaOstrava, MoravskoslezskyCzech Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-45123-7
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Earth and Environmental Science
  • Print ISBN 978-3-319-45122-0
  • Online ISBN 978-3-319-45123-7
  • Series Print ISSN 1863-2246
  • Series Online ISSN 1863-2351
  • Buy this book on publisher's site