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Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data.
Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information.
With chapters contributed by experts in geoinformatics and in domains such as computing and engineering, the book provides an understanding of the challenges and issues of big data in geoinformatics applications. The book is a single collection of current and emerging techniques, technologies, and tools that are needed to collect, analyze, manage, process, and visualize geospatial big data.
Table of Contents
Chapter 1 Distributed and Parallel Computing
Chapter 2 GEOSS Clearinghouse: Integrating Geospatial Resources to Support the Global Earth Observation System of Systems
Chapter 3 Using a Cloud Computing Environment to Process Large 3D Spatial Datasets
Chapter 4 Building Open Environments to Meet Big Data Challenges in Earth Sciences
Chapter 5 Developing Online Visualization and Analysis Services for NASA Satellite-Derived Global Precipitation Products during the Big Geospatial Data Era
Chapter 6 Algorithmic Design Considerations for Geospatial and/or Temporal Big Data
Chapter 7 Machine Learning on Geospatial Big Data
Chapter 8 Spatial Big Data: Case Studies on Volume, Velocity, and Variety
Chapter 9 Exploiting Big VGI to Improve Routing and Navigation Services
Chapter 10 Efficient Frequent Sequence Mining on Taxi Trip Records Using Road Network Shortcuts
Chapter 11 Geoinformatics and Social Media: New Big Data Challenge
Chapter 12 Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern
Chapter 13 Geospatial Cyberinfrastructure for Addressing the Big Data Challenges on the Worldwide Sensor Web
Chapter 14 OGC Standards and Geospatial Big Data