{"title":"应用开源离散全球网格系统","authors":"A. Kmoch, O. Matsibora, I. Vasilyev, E. Uuemaa","doi":"10.5194/agile-giss-3-41-2022","DOIUrl":null,"url":null,"abstract":"Abstract. Discrete Global Grid Systems (DGGS) are spatial reference systems that use a hierarchical tessellation of cells to partition and address the globe and provide alternative spatial data format and indexing methods as compared to traditional vector and raster spatial data. In order to effectively use DGGS, functional software needs to be available and data needs to be indexed into a DGGS. We compare the software APIs of the 5 main open-source DGGS implementations – Uber H3, Google S2, rHEALPix by Landcare Research New Zealand, RiskAware OpenEAGGR, and DGGRID by Southern Oregon University – and present exemplary workflows for converting spatial and vector and raster datasets into DGGS-indexed format. We summarize, that Uber H3 and Google S2 provide more mature software library functionalities and DGGRID provides excellent functionality to construct grids with desired geometric properties and to load point data but does not provide functions for traversal and navigation within a grid after its construction.\n","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Applied open-source Discrete Global Grid Systems\",\"authors\":\"A. Kmoch, O. Matsibora, I. Vasilyev, E. Uuemaa\",\"doi\":\"10.5194/agile-giss-3-41-2022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. Discrete Global Grid Systems (DGGS) are spatial reference systems that use a hierarchical tessellation of cells to partition and address the globe and provide alternative spatial data format and indexing methods as compared to traditional vector and raster spatial data. In order to effectively use DGGS, functional software needs to be available and data needs to be indexed into a DGGS. We compare the software APIs of the 5 main open-source DGGS implementations – Uber H3, Google S2, rHEALPix by Landcare Research New Zealand, RiskAware OpenEAGGR, and DGGRID by Southern Oregon University – and present exemplary workflows for converting spatial and vector and raster datasets into DGGS-indexed format. We summarize, that Uber H3 and Google S2 provide more mature software library functionalities and DGGRID provides excellent functionality to construct grids with desired geometric properties and to load point data but does not provide functions for traversal and navigation within a grid after its construction.\\n\",\"PeriodicalId\":116168,\"journal\":{\"name\":\"AGILE: GIScience Series\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AGILE: GIScience Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/agile-giss-3-41-2022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AGILE: GIScience Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/agile-giss-3-41-2022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Abstract. Discrete Global Grid Systems (DGGS) are spatial reference systems that use a hierarchical tessellation of cells to partition and address the globe and provide alternative spatial data format and indexing methods as compared to traditional vector and raster spatial data. In order to effectively use DGGS, functional software needs to be available and data needs to be indexed into a DGGS. We compare the software APIs of the 5 main open-source DGGS implementations – Uber H3, Google S2, rHEALPix by Landcare Research New Zealand, RiskAware OpenEAGGR, and DGGRID by Southern Oregon University – and present exemplary workflows for converting spatial and vector and raster datasets into DGGS-indexed format. We summarize, that Uber H3 and Google S2 provide more mature software library functionalities and DGGRID provides excellent functionality to construct grids with desired geometric properties and to load point data but does not provide functions for traversal and navigation within a grid after its construction.