Publishing Geospatial Data as Linked Data: Graph Processing Techniques for Automated Feature Detection and Resolution within Hydrography GIS Products

Interesting, largely unexplored data analysis and information retrieval opportunities exist for GIS data. In their current form, traditional data usage patterns for data persisted in shapefiles or spatially-enabled relational databases are limited. Opportunities exist to achieve ESIP’s Winter 2019 theme of ‘<em>increasing the use and value of Earth science data and information’</em> by transforming geospatial data from their original formats into their Resource Description Framework (RDF) manifestation. This work establishes an innovative workflow enabling the publication for Geospatial data persisted in geospatially enabled databases (PostGIS and MonetDB), ESRI shapefiles and XML, GML, KML, JSON, GeoJSON and CSV documents as graphs of linked open geospatial data. This affords the capability to identify implicit connections between related data that wasn't previously linked e.g. automating the detection of features present within large hydrography datasets as well as smaller regional examples and resolving features in a consistent fashion. This previously unavailable capability is achieved through the use of a semantic technology stack which leverages well matured standards within the Semantic Web space such as RDF as the data model, GeoSPARQL as the data access language and International Resource Identifier’s (IRI) for uniquely identifying and referencing entities such as rivers, streams and other water bodies. In anticipation of NASA’s forthcoming Surface Water Ocean Topography (SWOT – https://swot.jpl.nasa.gov) mission, which once launched in 2021 will make NASA’s first-ever global survey of Earth’s surface water, this work uses Hydrography data products (USGS’s National Hydrography Dataset and other topically relevant examples) as the topic matter. The compelling result is a new, innovative data analysis and information retrieval capability which will increases the use and value of Earth science data (GIS) and information. This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting in January 2019.<br>

地理信息系统（GIS）数据领域存在大量尚未得到充分挖掘的数据分析与信息检索研究机遇。当前，存储于形状文件（shapefile）或支持空间扩展的关系型数据库中的数据，其传统使用模式仍存在较大局限性。将空间数据从原始格式转换为资源描述框架（RDF）形态，可助力达成地球科学信息伙伴（ESIP）2019年冬季会议的主题——"提升地球科学数据与信息的使用价值"。本研究构建了一套创新工作流，可将存储于空间扩展数据库（PostGIS与MonetDB）、ESRI形状文件以及XML、地理标记语言（Geography Markup Language, GML）、关键标记语言（Keyhole Markup Language, KML）、JSON、地理JSON（GeoJSON）与CSV格式文档中的空间数据，发布为关联开放空间数据图谱。该工作流可实现对此前未建立关联的相关数据间隐含关联的识别，例如自动检测大型水文数据集乃至小型区域数据集内的地理要素，并以统一标准对要素进行解析处理。这一此前尚未实现的功能，可通过语义技术栈达成：该技术栈依托语义网领域成熟的标准规范，以资源描述框架（RDF）作为数据模型、以GeoSPARQL作为数据访问语言，并以国际资源标识符（International Resource Identifier, IRI）对河流、溪流及其他水体等实体进行唯一标识与引用。为配合美国国家航空航天局（NASA）即将执行的地表水域海洋地形（Surface Water Ocean Topography, SWOT，https://swot.jpl.nasa.gov）任务——该任务于2021年发射后，将完成NASA史上首次全球地表水域勘测——本研究以水文数据产品（美国地质调查局（United States Geological Survey, USGS）国家水文数据集及其他相关主题数据集）作为研究对象。最终成果实现了一项全新的创新型数据分析与信息检索能力，可有效提升地球科学数据（GIS）与信息的使用价值。本报告于2019年1月在地球科学信息伙伴（ESIP）冬季会议上发表。