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Discrete Global Grid Systems: Operational Capability of the Current State of the Art

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Discrete Global Grid Systems: Operational Capability of the Current State of the Art Spatial Knowledge and Information Canada, 2019, 7(6), 1 Discrete Global Grid Systems: Operational Capability of the Current State of the Art BEN BONDARUK STEVEN A. ROBERTS COLIN ROBERTSON Department of Geography & Department of Geography & Department of Geography & Environmental Management Environmental Studies Environmental Studies University of Waterloo, Wilfrid Laurier University Wilfrid Laurier University Department of Geography & [email protected] [email protected] Environmental Studies Wilfrid Laurier University [email protected] essential. As a result, new methods for ABSTRACT integrating, transmitting and representing The paper compares two current spatial data are required. Discrete Global implementations of Discrete Global Grid Grid Systems (DGGS) have been proposed Systems as potential new data models for as a new model for spatial data spatial data representation, integration, and representation, integration and analysis analysis. It outlines suitability of such suited to the current data-rich environment structures for spatial data modelling and (Li, 2013; Mahdavi-Amiri et el., 2016). GIS applications, as well as documents the DGGS are hierarchical tessellations of core criteria necessary for their successful regular shaped polygons (Figure 1.0) implementation. An experimental analysis initially designed as a global reference is performed in order to determine the system for mapping and navigational current state of their development and their purposes (Purss, Gibb, Samavati, Peterson practical applicability for data integration, & Ben, 2016). analysis and visualization. The work concludes with a reflection on the current implementations compared to the industry standards and some future projections of geospatial analysis within Discrete Global Grid Systems framework. 1. Introduction Spatial data handling and integration has become one of the prevailing needs in geospatial analysis and computation. In the modern digital context spatial data are usually collected and stored as either raster or vector representations, along with attribute data for these spatial features (Mahdavi-Amiri, Alderson & Samavati, 2016). These representations have evolved to serve a number of specialist communities and workflows in GIS analysis (e.g., Figure 1.0. Example of a hierarchical structure of the Earth satellite-based remote sensing); however surface using hexagon shapes for the cell refinement generated with continuing increases in spatial data for the purposes of this study. heterogeneity and volume, the necessity for efficient data integration has become 2 Discrete Global Grid Systems Over time, due to its discrete construction requirements of the selected software DGGS have also started to be used as a data libraries to the released OGC standards. It is structure for consistent storage, reference very likely that certain requirements might and analysis of spatial data and its attribute not be met by either of the software, information. DGGS suggest a different suggesting further advancing of DGGS approach for geospatial data handling that packages. Both H3 and OpenEAGGR are allows interoperability of resources and open source software available via the elimination of inaccurate and complex data GitHub source code repository (Uber, 2015; synthesis operations (Purss et el., 2016). Riskaware, 2017). In order for a grid network to qualify as For the testing purposes both libraries DGGS it must consist of core elements were built directly from their source in their documented and summarized in the Open natural development environments. This Geospatial Consortium (OGC) standard data step is necessary in order to gain access to protocol (Open Geospatial Consortium, the full list of available functionality, which 2017). The work of Goodchild and Kimerling might not be available through other (2002), on defining DGGS and some of their language bindings. In particular, the H3 core requirements, have contributed greatly library is built from its source using CMake to the overall advancement of this new data packaging software, Visual Studio standard; yet some of the earlier research on development environment with integrated DGGS had already begun in the 1980s C++ compiler; whereas OpenEAGGR is built (Dutton, 1984). Their initial ideas and using the MinGW compiler and Eclipse thoughts served as the basis for a fully development environment. In addition, functional and well-designed DGGS data JavaScript binding for H3 and Python standards set by OGC. OGC requirements binding for OpenEAGGR libraries were also were put in place in order to guarantee the used in order to evaluate their flexibility and explicit resolution, area preservation and ease of use. Although not a requirement the positional uniqueness at each level of libraries were also evaluated based on their hierarchy which account for scale availability for programing language differences and spatial distortion globally. bindings for the user’s convenience. In addition, the unique topological In this study the main focus is put on properties of regular shape tessellation hexagon shape structures due to their might also naturally suggest looking for new availability on both platforms, as well as forms of spatial analysis. The referencing their advantages in sampling, circularity, and indexing mechanisms, on the other packing and uniform connectivity properties hand, provide reliable methods to access, over the other regular shapes (e.g., triangles, store and retrieve data. As a result, various squares) (Li, 2013; Peterson, 2017). The algorithms might integrate the existing particular interest of this study is to evaluate indexing system for data assimilation via the the operational capability of both libraries. refinement methods using the above In other words, the aim is to test practical properties (Peterson, 2017; Purss el al., applications of available DGGS software for 2016). their basic functionality, such as importing The aim of this paper is an in-depth of spatial data, querying spatial analysis analysis of two DGGS implementation: the algorithms as well as exporting and H3 (Uber, 2015) and OpenEAGGR visualizing the end results. (Riskaware, 2017) open source software libraries. In addition, their operational and 3. Results practical applications are also reviewed. A summary of H3 and OpenEAGGR implementations compared to OGC 2. Methods standards is outlined in Table 1.0. The The following section outlines the existing DGGS software provide a methodology for comparing core data model comprehensive approach for geocoding, Discrete Global Grid Systems 3 indexing, addressing and processing consecutive cell’s location of a higher geospatial data into discrete forms. Due to resolution, whereas offset addressing uses their hierarchical structures, positional fixed axis orientation and offset distances uniqueness and discrete representation of from their origin to determine location of a spatial resolution, DGGS are gaining cell (Bush, 2017). Both implementations use popularity and acceptance in the modern an icosahedron as a base polyhedron for geospatial analysis and data integration. creating planar faces approximating a sphere. The grid partitioning method of H3 3.1 Technical specifications uses hexagon aperture 7, whereas OpenEAGGR incorporates both hexagon A detailed technical analysis show basic aperture 3 and triangle aperture 4 functionality of DGGS for modeling Earth’s hierarchical models (Uber, 2015; Riskaware, surface via hierarchical networks of equal 2017). Aperture is a method known to area cells. Both libraries support partition a DGGS cell using additional hierarchical tessellation of regular polygons partial self-similar shapes (e.g., hexagons) at increasingly fine resolutions up to a m2 in order to preserve equal area property and cm2 in areal size for H3 and across multiple resolutions (Figure 2.0). OpenEAGGR respectively (Uber, 2015; Riskaware, 2017). Each cell has a unique index and is accessible throughout the hierarchies. The given software also provides functionality to convert from latitude-longitude coordinates to DGGS indexes and vice versa referencing all cell Figure 2.0. Hierarchical partition of space using hexagon centroids. apertures 3 (left), 4 (middle) and 7 (right) (Sahr, 2013). Since addressing and referencing are two major properties of DGGS (criteria 11-12) The results of this subsection indicate (Table 1.0) it is important to mention that that both implementations fail to meet the H3 and OpenEAGGR use hierarchy-based complete list of required criteria outlined by and offset coordinate addressing structure OGC and therefore cannot be classified as for hexagonal cell systems respectively. fully functional DGGS (Table 1.0). Hierarchical addressing is based on the lower resolution grid to find next Table 1.0: The following table outlines a core set of criteria to be met by software and classified as DGGS. The table also summarizes technical specifications of H3 and OpenEAGGR libraries and compares them to the OGC standards. Criteria OGC Requirement H3 OpenEAGGR Notes 1 Core Data Model () () This requirement includes definition of conceptual data model of DGGS Partial Partial including reference frame (criteria 2-13) and functional algorithms fulfillment fulfillment (criteria 14-18) elements, which are partially fulfilled by each library. 2 Area () () Guarantees the coverages of the entire globe. Each library fulfills the Fulfilled Fulfilled requirement for covering the entire surface of the earth. 3 Overlap () () Ensures positional uniqueness
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