The Rise of Big Spatial Data

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.

Table of Contents

Chapter 1 Application of Web-GIS for Dissemination and 3D Visualization of Large-Volume LiDAR Data
Chapter 2 Design and Evaluation of WebGL-Based Heat Map Visualization for Big Point Data
Chapter 3 Open Source First Person View 3D Point Cloud Visualizer for Large Data Sets
Chapter 4 Web-Based GIS Through a Big Data Open Source Computer Architecture for Real Time Monitoring Sensors of a Seaport
Chapter 5 Deriving Traffic-Related CO2 Emission Factors with High Spatiotemporal Resolution from Extended Floating Car Data
Chapter 6 Combining Different Data Types for Evaluation of the Soils Passability
Chapter 7 Sparse Big Data Problem. A Case Study of Czech Graffiti Crimes
Chapter 8 Towards Better 3D Model Accuracy with Spherical Photogrammetry
Chapter 9 Surveying of Open Pit Mine Using Low-Cost Aerial Photogrammetry
Chapter 10 Sentinel-1 Interferometry System in the High-Performance Computing Environment
Chapter 11 Modelling Karst Landscape with Massive Airborne and Terrestrial Laser Scanning Data
Chapter 12 Errors in the Short-Term Forest Resource Information Update
Chapter 13 Accuracy of High-Altitude Photogrammetric Point Clouds in Mapping
Chapter 14 Outlook for the Single-Tree-Level Forest Inventory in Nordic Countries
Chapter 15 Proximity-Driven Motives in the Evolution of an Online Social Network
Chapter 16 Mapping Emotions: Spatial Distribution of Safety Perception in the City of Olomouc
Chapter 17 Models for Relocation of Emergency Medical Stations
Chapter 18 Spatio-Temporal Variation of Accessibility by Public Transport—The Equity Perspective
Chapter 19 MapReduce Based Scalable Range Query Architecture for Big Spatial Data
Chapter 20 The Possibilities of Big GIS Data Processing on the Desktop Computers
Chapter 21 Utilization of the Geoinfomatics and Mathematical Modelling Tools for the Analyses of Importance and Risks of the Historic Water Works
Chapter 22 Creating Large Size of Data with Apache Hadoop
Chapter 23 Datasets of Basic Spatial Data in Chosen Countries of the European Union
Chapter 24 Spatial Data Analysis with the Use of ArcGIS and Tableau Systems
Chapter 25 Processing LIDAR Data with Apache Hadoop
Chapter 26 Compression of 3D Geographical Objects at Various Level of Detail
Chapter 27 Applicability of Support Vector Machines in Landslide Susceptibility Mapping
Chapter 28 Integration of Heterogeneous Data in the Support of the Forest Protection: Structural Concept