A comparison of intercell metrics on discrete global grid systems

Gregory, Matthew J.; Kimerling, A. Jon; White, Denis; Sahr, Kevin

doi:10.1016/j.compenvurbsys.2007.11.003

Cited by 26 publications

(18 citation statements)

References 13 publications

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“…As shown in Figure 6, there are only five Platonic solids [62,63], including the tetrahedron, cube (hexahedron), octahedron, dodecahedron, and icosahedron, which can be used as female parents to construct DGGS. In general, DGGS solutions should be constructed by specifying five substantially independent design choices [59]: a base polyhedron, a fixed polyhedron orientation, a hierarchical spatial partitioning method, transformation, and a method for assigning point representations to grid cells.…”

Section: Cloud Computing For Beodmentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

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Section: Cloud Computing For Beodmentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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“…In general, DGGSs that have a planar square grid have many favourable characteristics including familiarity of square grids for users and compatibility with (i) existing hardware and display devices, (ii) quad-tree based algorithms, (iii) hierarchical data structures, and (iv) coordinate systems (Sahr et al 2003;Gregory et al 2008;Amiri et al 2015). Although many innovative square based DGGSs have been designed, such as (Ma et al 2009) and (Amiri et al 2013), the rHEALPix DGGS appears most often in DGGS related articles and literature.…”

Section: The Rhealpix Dggsmentioning

confidence: 99%

“…The former, which is common in both quadrilateral and triangle cell based DGGSs, means distances to neighbouring cell centroids are not equal. This causes problems for dynamical systems where functions are related to intercell distances as well as in applications involving discrete simulations (Sahr et al 2003;Gregory et al 2008). The latter is a result of combining four triangular Collignon projections into a single square, see figure 8.…”

Section: Key Featuresmentioning

confidence: 99%

The rHEALPix Discrete Global Grid System: considerations for Canada

Bowater

Stefanakis

2018

Geomatica

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Geospatial data is ubiquitous. However, the geographic grid is not designed to manage, store, or integrate huge volumes of heterogeneous geospatial data. One possible solution to these challenges is a Discrete Global Grid System (DGGS), which was recently announced as a new standard by the Open Geospatial Consortium (OGC). If Canada plans to utilize a DGGS in the future to reference geospatial data, research is needed to explore characteristics and suitability of different DGGSs. Although hexagonal- and triangular-based DGGSs are popular, the square-based rHEALPix DGGS mirrors the geographic grid more closely and has many advantages. This paper reviews key features of the rHEALPix DGGS, outlines recent work, and qualitatively considers cell shape and cell orientation with respect to Canada, including how variations can be avoided or exploited by rotating the grid.

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“…1). This is the "latitude-longitude grid" [68,75] or "equal-angle grid" [25]. The points concentrate towards the poles, due to the converging meridians, resulting in high anisotropy.…”

Section: Latitude-longitude Latticementioning

confidence: 99%

Measurement of Areas on a Sphere Using Fibonacci and Latitude–Longitude Lattices

2009

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The area of a spherical region can be easily measured by considering which sampling points of a lattice are located inside or outside the region. This point-counting technique is frequently used for measuring the Earth coverage of satellite constellations, employing a latitude-longitude lattice. This paper analyzes the numerical errors of such measurements, and shows that they could be greatly reduced if the Fibonacci lattice were used instead. The latter is a mathematical idealization of natural patterns with optimal packing, where the area represented by each point is almost identical. Using the Fibonacci lattice would reduce the root mean squared error by at least 40%. If, as is commonly the case, around a million lattice points are used, the maximum error would be an order of magnitude smaller.

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A comparison of intercell metrics on discrete global grid systems

Cited by 26 publications

References 13 publications

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

The rHEALPix Discrete Global Grid System: considerations for Canada

Measurement of Areas on a Sphere Using Fibonacci and Latitude–Longitude Lattices

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