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| 1 | GIScience research challenges for realizing discrete global grid systems as a Digital Earth显示文摘Increasing data resources are available for documenting and detecting changes in environmental,ecological,and socioeconomic processes.Currently,data are distributed across a wide variety of sources(e.g.data silos)and published in a variety of formats,scales,and semantic representations.A key issue,therefore,in building systems that can realize a vision of earth system monitoring remains data integration.Discrete global grid systems(DGGSs)have emerged as a key technology that can provide a common multi-resolution spatial fabric in support of Digital Earth monitoring.However,DGGSs remain in their infancy with many technical,conceptual,and operational challenges.With renewed interest in DGGS brought on by a recently proposed standard,the demands of big data,and growing needs for monitoring environmental changes across a variety of scales,we seek to highlight current challenges that we see as central to moving the field(s)and technologies of DGGS forward.For each of the identified challenges,we illustrate the issue and provide a potential solution using a reference DGGS implementation.Through articulation of these challenges,we hope to identify a clear research agenda,expand the DGGS research footprint,and provide some ideas for moving forward towards a scaleable Digital Earth vision.Addressing such challenges helps the GIScience research community to achieve the real benefits of DGGS and provides DGGS an opportunity to play a role in the next generation of GIS. | Majid Hojati Colin Robertson Steven Roberts Chiranjib Chaudhuri | 2022 | Big Earth Data2022，6，3： | 4 |
| 2 | Area and shape distortions in open-source discrete global grid systems显示文摘A Discrete Global Grid System(DGGS)is a type of spatial reference system that tessellates the globe into many individual,evenly spaced,and well-aligned cells to encode location and,thus,can serve as a basis for data cube construction.This facilitates integration and aggregation of multi-resolution data from various sources to rapidly calculate spatial statistics.We calculated normalized area and compactness for cell geometries from 5 open-source DGGS implementations-Uber H3,Google S2,RiskAware OpenEAGGR,rHEALPix by Landcare Research New Zealand,and DGGRID by Southern Oregon University-to evaluate their suitability for a global-level statistical data cube.We conclude that the rHEALPix and OpenEAGGR and DGGRID ISEA-based DGGS definitions are most suitable for global statistics because they have the strongest guarantee of equal area preservation-where each cell covers almost exactly the same area on the globe.Uber H3 has the smallest shape distortions,but Uber H3 and Google S2 have the largest variations in cell area.However,they provide more mature software library functionalities.DGGRID provides excellent functionality to construct grids with desired geometric properties but as the only implementation does not provide functions for traversal and navigation within a grid after its construction. | Alexander Kmoch Ivan Vasilyev Holger Virro Evelyn Uuemaa | 2022 | Big Earth Data2022，6，3： | 3 |
| 3 | EASE-DGGS:a hybrid discrete global grid system for Earth sciences显示文摘Although we live in an era of unprecedented quantities and access to data,deriving actionable information from raw data is a hard problem.Earth observation systems(EOS)have experienced rapid growth and uptake in recent decades,and the rate at which we obtain remotely sensed images is increasing.While significant effort and attention has been devoted to designing systems that deliver analytics ready imagery faster,less attention has been devoted to developing analytical frameworks that enable EOS to be seamlessly integrated with other data for quantitative analysis.Discrete global grid systems(DGGS)have been proposed as one potential solution that addresses the challenge of geospatial data integration and interoperability.Here,we propose the systematic extension of EASE-Grid in order to provide DGGS-like characteristics for EOS data sets.We describe the extensions as well as present implementation as an application programming interface(API),which forms part of the University of Minnesota’s GEMS(Genetic x Environment x Management x Socioeconomic)Informatics Center’s API portfolio. | Jeffery A.Thompson Mary J.Brodzik Kevin A.T.Silverstein Mason A.Hurley Nathan L.Carlson | 2022 | Big Earth Data2022，6，3： | 2 |
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