Abstract
The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section.
Highlights
Introduction to Big Data Computing forGeospatial ApplicationsReceived: 3 August 2020; Accepted: 10 August 2020; Published: 12 August 2020 AbstractThe convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization
Following a series of successful sessions organized at the American Association of Geographers (AAG) Annual Meeting since 2015, this special issue on “Big Data Computing for Geospatial Applications” by the ISPRS International Journal of Geo-Information aims to capture the latest efforts on utilizing, adapting, and developing new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges for supporting applications in different domains, such as climate change, disaster management, human dynamics, public health, and environment and engineering
The articles included in this issue make significant contributions to the use of big data computing for tackling various geospatial problems by incorporating novel methodologies, data structures, and algorithms with advanced computing frameworks
Summary
Earth observation systems and model simulations are generating massive volumes of disparate, dynamic, and geographically distributed geospatial data with increasingly finer spatiotemporal resolutions [1]. Following a series of successful sessions organized at the American Association of Geographers (AAG) Annual Meeting since 2015, this special issue on “Big Data Computing for Geospatial Applications” by the ISPRS International Journal of Geo-Information aims to capture the latest efforts on utilizing, adapting, and developing new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges for supporting applications in different domains, such as climate change, disaster management, human dynamics, public health, and environment and engineering This special issue aims to address the following important topics: (1) geo-cyberinfrastructure integrating spatiotemporal principles and advanced computational technologies (e.g., GPU (graphics processing unit computing), multicore computing, high-performance computing, and cloud computing); (2) innovations in developing computing and programming frameworks and architecture (e.g., MapReduce, Spark) or parallel computing algorithms for geospatial applications; (3) new geospatial data management strategies and storage models coupled with high-performance computing for efficient data query, retrieval, and processing (e.g., new spatiotemporal indexing mechanisms); (4) new computing methods considering spatiotemporal collocation (locations and relationships) of users, data, and computing resources; (5) geospatial big data processing, mining, and visualization methods using high-performance computing and artificial intelligence; (6) integrating scientific workflows in cloud computing and/or a high-performance computing environment; and (7) other research, development, education, and visions related to geospatial big data computing. This editorial provides a summary of the ten articles included in this issue and suggests future research directions in this area based on our collective observations
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