It is difficult to share and reuse geospatial data and retrieve geospatial information because of geospatial data heterogeneity problems. Lack of semantic interoperability is one of the major problems facing GIS (Geographic Information Science/System) systems and applications today. To solve geospatial data heterogeneity problems and support geospatial information retrieval and semantic interoperability over the Web, the use of an ontology is proposed because it is a formal explicit description of concepts or meanings of words in a well-defined and unambiguous manner. Geospatial ontologies represent geospatial concepts and properties for use over the Web. OWL (Ontology Web Language) is an emerging language for defining and instantiating ontologies. OWL builds on RDF (Resource Description Framework) but adds more vocabulary for describing properties and classes. The downside of representing structured geospatial data in OWL and RDF languages is that it can result in inefficient data access. SPARQL (Simple Protocol and RDF Query Language) is recommended for general RDF query while the GeoSPARQL (Geographic Simple Protocol and RDF Query Language) protocol is proposed as an extension of SPARQL for querying geospatial data. However, the runtime cost of GeoSPARQL queries can be high due to the fine-grained nature of RDF data models. There are several challenges to using ontologies for geospatial semantic interoperability but these can be overcome through collaboration.

Spatial data infrastructure (SDI) is the infrastructure that facilitates the discovery, access, management, distribution, reuse, and preservation of digital geospatial resources. These resources may include maps, data, geospatial services, and tools. As cyberinfrastructures, SDIs are similar to other infrastructures, such as water supplies and transportation networks, since they play fundamental roles in many aspects of the society. These roles have become even more significant in today’s big data age, when a large volume of geospatial data and Web services are available. From a technological perspective, SDIs mainly consist of data, hardware, and software. However, a truly functional SDI also needs the efforts of people, supports from organizations, government policies, data and software standards, and many others. In this chapter, we will present the concepts and values of SDIs, as well as a brief history of SDI development in the U.S. We will also discuss the components of a typical SDI, and will specifically focus on three key components: geoportals, metadata, and search functions. Examples of the existing SDI implementations will also be discussed.  

Libraries, archives, and museums (LAMs) are an important part of the GIS&T ecosystem and they engage in numerous activities that are critical for students, researchers, and practitioners. Traditionally these organizations have been at the forefront of developing infrastructures and services that connect researchers and others to historical and contemporary GIS data, including print maps. More recently, as a result of greater interest in spatial thinking and research, these organizations and institutions have become a place for instruction, outreach, and practice. This entry will discuss the historical role that LAMs have played in supporting and developing GIS&T as well as focus on current trends.

Spatial data infrastructures may be thought of as socio-technical frameworks for coordinating the development, management, sharing and use of geospatial data across multiple organizational jurisdictions and varying geographic extents. The United States was an early adopter of the SDI concept and the U.S. National Spatial Data Infrastructure (NSDI) is an example of a country-wide SDI implementation facilitated by coordination at the federal-government level. At the time of its establishment in the early 1990s, a unique characteristic of the NSDI was a mandate for federal agencies to establish partnerships with state- and local-level government. This entry summarizes the origins of the NSDI’s establishment, its original core components and how they’ve evolved over the last 25 years, the role of the Federal Geographic Data Committee (FGDC), and the anticipated impact of passage of the Geospatial Data Act of 2018. For broader technical information about SDIs, readers are referred to GIST BoK Entry DM-60: Spatial Data Infrastructures (Hu and Li 2017). For additional details on the history of the NSDI, readers are referred to Rhind (1999). For the latest information on recent and emerging NSDI initiatives, please visit the FGDC web site (www.fgdc.gov).  

It is difficult to share and reuse geospatial data and retrieve geospatial information because of geospatial data heterogeneity problems. Lack of semantic interoperability is one of the major problems facing GIS (Geographic Information Science/System) systems and applications today. To solve geospatial data heterogeneity problems and support geospatial information retrieval and semantic interoperability over the Web, the use of an ontology is proposed because it is a formal explicit description of concepts or meanings of words in a well-defined and unambiguous manner. Geospatial ontologies represent geospatial concepts and properties for use over the Web. OWL (Ontology Web Language) is an emerging language for defining and instantiating ontologies. OWL builds on RDF (Resource Description Framework) but adds more vocabulary for describing properties and classes. The downside of representing structured geospatial data in OWL and RDF languages is that it can result in inefficient data access. SPARQL (Simple Protocol and RDF Query Language) is recommended for general RDF query while the GeoSPARQL (Geographic Simple Protocol and RDF Query Language) protocol is proposed as an extension of SPARQL for querying geospatial data. However, the runtime cost of GeoSPARQL queries can be high due to the fine-grained nature of RDF data models. There are several challenges to using ontologies for geospatial semantic interoperability but these can be overcome through collaboration.