Ivan I., Singleton A., Horák J., Inspektor T. (eds.) The Rise of Big Spatial Data

Springer, 2017. — 418 p. — (Lecture Notes in Geoinformation and Cartography). — ISBN: 3319451235, 9783319451237, 9783319451220This 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.Application of Web-GIS for Dissemination and 3D Visualization of Large-Volume LiDAR Data
Design and Evaluation of WebGL-Based Heat Map Visualization for Big Point Data
Open Source First Person View 3D Point Cloud Visualizer for Large Data Sets
Web-Based GIS Through a Big Data Open Source Computer Architecture for Real Time Monitoring Sensors of a Seaport
Deriving Traffic-Related CO2 Emission Factors with High Spatiotemporal Resolution from Extended Floating Car Data
Combining Different Data Types for Evaluation of the Soils Passability
Sparse Big Data Problem. A Case Study of Czech Graffiti Crimes
Towards Better 3D Model Accuracy with Spherical Photogrammetry
Surveying of Open Pit Mine Using Low-Cost Aerial Photogrammetry
Sentinel-1 Interferometry System in the High-Performance Computing Environment
Modelling Karst Landscape with Massive Airborne and Terrestrial Laser Scanning Data
Errors in the Short-Term Forest Resource Information Update
Accuracy of High-Altitude Photogrammetric Point Clouds in Mapping
Outlook for the Single-Tree-Level Forest Inventory in Nordic Countries
Proximity-Driven Motives in the Evolution of an Online Social Network
Mapping Emotions: Spatial Distribution of Safety Perception in the City of Olomouc
Models for Relocation of Emergency Medical Stations
Spatio-Temporal Variation of Accessibility by Public Transport—The Equity Perspective
MapReduce Based Scalable Range Query Architecture for Big Spatial Data
The Possibilities of Big GIS Data Processing on the Desktop Computers
Utilization of the Geoinfomatics and Mathematical Modelling Tools for the Analyses of Importance and Risks of the Historic Water Works
Creating Large Size of Data with Apache Hadoop
Datasets of Basic Spatial Data in Chosen Countries of the European Union
Spatial Data Analysis with the Use of ArcGIS and Tableau Systems
Processing LIDAR Data with Apache Hadoop
Compression of 3D Geographical Objects at Various Level of Detail
Applicability of Support Vector Machines in Landslide Susceptibility Mapping
Integration of Heterogeneous Data in the Support of the Forest Protection: Structural Concept