Metadata and data standards are crucial in geospatial engineering. They provide essential information about datasets, enabling efficient discovery, understanding, and quality assessment. These standards ensure consistency and interoperability in documenting and exchanging geospatial data.
Various types of metadata exist, including descriptive, structural, and administrative. Standards like and FGDC CSDGM provide frameworks for documenting datasets. Tools and best practices help create and manage metadata, supporting effective data sharing and integration across platforms and organizations.
Importance of metadata
Metadata plays a crucial role in geospatial engineering by providing essential information about geospatial datasets
Metadata facilitates data discovery, understanding, and quality assessment, enabling efficient use and management of geospatial data
Metadata for data discovery
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The Code4Lib Journal – A Metadata Schema for Geospatial Resource Discovery Use Cases View original
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Metadata includes descriptive information such as title, abstract, keywords, and spatial
Enables users to search for and locate relevant geospatial datasets (land cover, elevation models)
Facilitates data sharing and reuse across different platforms and organizations
Metadata for data understanding
Metadata provides detailed information about the content, structure, and context of geospatial data
Includes , data collection methods, data processing steps, and data limitations
Enables users to assess the suitability of a dataset for their specific application or analysis
Metadata for data quality
Metadata documents data quality information such as , completeness, and consistency
Provides information on data sources, data collection dates, and data update frequency
Allows users to evaluate the reliability and fitness for use of geospatial datasets
Types of metadata
Descriptive metadata
focuses on the identification and discovery of geospatial datasets
Includes elements such as title, abstract, keywords, spatial extent, and temporal coverage
Enables users to search for and locate relevant datasets based on their content and characteristics
Structural metadata
Structural metadata describes the internal structure and organization of geospatial datasets
Includes information on file formats, data models, and spatial reference systems
Enables users to understand how the data is stored and how it can be accessed and processed
Administrative metadata
provides information related to the management and maintenance of geospatial datasets
Includes elements such as data ownership, access rights, data use constraints, and data update schedules
Supports the effective management and long-term preservation of geospatial datasets
Metadata standards
Importance of metadata standards
Metadata standards ensure consistency and interoperability in the documentation and exchange of geospatial metadata
Provide a common structure and terminology for describing geospatial datasets
Enable the integration and sharing of metadata across different systems and organizations
ISO 19115 geographic metadata standard
ISO 19115 is an international standard for describing geographic information and services
Defines a comprehensive set of metadata elements and their relationships
Provides a standardized framework for documenting geospatial datasets and services
FGDC Content Standard for Digital Geospatial Metadata
FGDC CSDGM is a metadata standard developed by the Federal Geographic Data Committee in the United States
Defines a set of metadata elements for documenting geospatial datasets
Widely used in the United States and has influenced the development of other metadata standards
Dublin Core metadata standard
is a general-purpose metadata standard for describing a wide range of resources, including geospatial data
Consists of a simple set of 15 metadata elements (title, creator, subject, description)
Provides a basic level of interoperability for metadata across different domains and disciplines
Metadata creation and management
Metadata creation tools
Metadata creation tools assist in the generation and editing of geospatial metadata
Examples include ArcGIS Metadata Editor, GeoNetwork opensource, and CatMDEdit
Facilitate the creation of metadata that complies with specific metadata standards
Metadata management systems
Metadata management systems provide centralized storage, search, and retrieval of geospatial metadata
Examples include GeoNetwork opensource, CKAN, and Esri Geoportal Server
Enable the efficient management and dissemination of metadata across organizations
Best practices for metadata creation
Follow established metadata standards and guidelines to ensure consistency and interoperability
Provide clear and concise descriptions of geospatial datasets, including their purpose, content, and limitations
Keep metadata up to date as datasets evolve and change over time
Geospatial data standards
Importance of data standards
Geospatial data standards ensure interoperability and consistency in the representation and exchange of geospatial data
Enable the integration and analysis of data from different sources and systems
Facilitate data sharing and reuse across different applications and organizations
Open Geospatial Consortium (OGC) standards
OGC develops and maintains a wide range of open standards for geospatial data and services
Examples include Web Map Service (), Web Feature Service (WFS), and GeoPackage
Promote interoperability and enable the development of interoperable geospatial systems
International Organization for Standardization (ISO) standards
ISO develops international standards for various aspects of geospatial data and information
Examples include ISO 19107 (Spatial Schema), ISO 19111 (Referencing by Coordinates), and ISO 19157 (Data Quality)
Provide a framework for the consistent representation and exchange of geospatial data
National spatial data infrastructure (NSDI) standards
NSDI standards are developed by national mapping agencies to support the development of national spatial data infrastructures
Examples include the United States NSDI Framework Data Standards and the Australian and New Zealand Spatial Information Council (ANZLIC) standards
Ensure the interoperability and consistency of geospatial data at a national level
Data interoperability and integration
Importance of data interoperability
Data interoperability enables the seamless exchange and use of geospatial data across different systems and applications
Allows for the integration and analysis of data from multiple sources
Facilitates data sharing and collaboration among different organizations and stakeholders
Data format standards
Data format standards specify the structure and encoding of geospatial data files
Examples include for raster data, Shapefile for vector data, and GeoPackage for both raster and vector data
Ensure compatibility and interoperability of geospatial data across different software platforms
Coordinate reference system standards
Coordinate reference system standards define the parameters and methods for spatial referencing of geospatial data
Examples include WGS84 (World Geodetic System 1984) and EPSG (European Petroleum Survey Group) codes
Enable the accurate and consistent representation of spatial locations across different datasets and systems
Data exchange protocols
Data exchange protocols define the methods and formats for exchanging geospatial data between systems
Examples include Web Map Service (WMS) for map images, Web Feature Service (WFS) for vector data, and Web Coverage Service (WCS) for raster data
Enable the interoperable sharing and access to geospatial data over the internet
Metadata and data standards in practice
Metadata in geospatial data portals
Geospatial data portals use metadata to facilitate the discovery, access, and use of geospatial datasets
Examples include the US Geospatial Platform, the European Union's INSPIRE Geoportal, and the Australian Geoscience Information Network (AusGIN)
Metadata enables users to search for and evaluate the suitability of datasets for their specific needs
Metadata in spatial data infrastructures
Spatial data infrastructures (SDIs) rely on metadata to support the sharing and integration of geospatial data across organizations
Examples include the US National Spatial Data Infrastructure (NSDI) and the European Union's Infrastructure for Spatial Information in Europe (INSPIRE)
Metadata standards ensure the consistency and interoperability of metadata within SDIs
Data standards in GIS software
GIS software packages support various geospatial data standards to enable data interoperability and integration
Examples include Esri ArcGIS, QGIS, and MapInfo Professional
GIS software tools facilitate the creation, management, and dissemination of standardized geospatial data
Case studies of metadata and data standards implementation
The US Geological Survey (USGS) has implemented the FGDC CSDGM and ISO 19115 metadata standards for their geospatial data holdings
The European Environment Agency (EEA) has adopted the INSPIRE metadata and data standards for their environmental data infrastructure
The World Bank has implemented the Dublin Core metadata standard for their geospatial data catalog
Key Terms to Review (18)
Accuracy: Accuracy refers to how close a measured or calculated value is to the true value or the actual position of an object. In the context of geospatial data, accuracy is crucial as it affects the reliability of measurements and analyses, ensuring that data representation aligns closely with real-world conditions. Various factors influence accuracy, including instrument calibration, environmental conditions, and methodologies used in data collection.
Administrative metadata: Administrative metadata refers to the information that helps manage, maintain, and govern digital resources throughout their lifecycle. This type of metadata is essential for facilitating access, ensuring proper data stewardship, and maintaining compliance with legal and regulatory requirements. It plays a crucial role in organizing data, tracking changes, and providing context, which is vital for both data standards and data lineage.
Data cataloging: Data cataloging is the process of creating an organized inventory of data assets within an organization, enabling users to find, understand, and use data more effectively. This involves not only listing the data sets but also providing contextual information such as metadata, data lineage, and quality metrics, which enhance data governance and compliance. By facilitating easy access to relevant data, data cataloging supports better decision-making and collaboration across various teams.
Data governance: Data governance refers to the overall management of data availability, usability, integrity, and security in an organization. It ensures that data is accurate, consistent, and used responsibly across various platforms and stakeholders. Effective data governance establishes clear policies and procedures that dictate how data is handled, which is crucial for maintaining high-quality attribute data management, adhering to metadata standards, and building a robust spatial data infrastructure.
Data lineage: Data lineage refers to the process of tracking the flow of data through its lifecycle, including its origins, transformations, and movement across systems. This concept is crucial for understanding the quality, context, and traceability of data, as it helps stakeholders identify how data is created, modified, and ultimately utilized. With effective data lineage, organizations can ensure compliance with regulations and improve overall data governance.
Descriptive metadata: Descriptive metadata refers to the information that helps to identify and describe a resource, making it easier to find and understand its content. This type of metadata includes elements like title, author, subject, and keywords, which are essential for cataloging and retrieving data effectively. It plays a crucial role in ensuring that users can locate relevant datasets and understand their context, especially in relation to data standards and lineage.
Dublin Core: Dublin Core is a metadata standard used for describing a wide range of digital resources, including documents, images, and videos. It consists of 15 core elements that help users identify and locate resources, promoting better discovery and interoperability across diverse systems. This standard plays a crucial role in data standards by providing a simple yet effective framework for metadata creation and management.
Extent: Extent refers to the geographic boundaries or area covered by a dataset, describing its spatial scope. Understanding the extent is crucial for interpreting data accurately, as it defines what is included or excluded from analysis and helps assess data quality and relevance in relation to specific applications.
FGDC Metadata Standards: FGDC Metadata Standards are guidelines established by the Federal Geographic Data Committee to provide a consistent framework for describing geospatial data. These standards ensure that data is well-documented, allowing users to easily discover, understand, and use the information effectively. By promoting interoperability and encouraging best practices in data management, FGDC Metadata Standards play a crucial role in enhancing the usability and sharing of geospatial datasets across different platforms and agencies.
Geospatial data sharing: Geospatial data sharing is the practice of distributing and making geographic information accessible to various users and systems. This process enables collaboration and enhances the ability to analyze, visualize, and utilize spatial data across different platforms and organizations. Effective data sharing relies on standardized metadata, which provides essential information about the data, ensuring compatibility and promoting interoperability among diverse datasets.
GeoTIFF: A GeoTIFF is a public domain metadata standard that allows georeferencing information to be embedded within a TIFF (Tagged Image File Format) file. This format not only stores the image data but also includes essential information like coordinate system, projection, and georeferencing details, making it vital for spatial data applications.
Identifier: An identifier is a unique label or reference used to distinguish a specific piece of data within a dataset. In the context of metadata and data standards, identifiers play a crucial role in ensuring that data can be accurately referenced, accessed, and linked to other datasets or resources. By providing a systematic way to locate and manage data, identifiers enhance data interoperability and facilitate effective data sharing among various stakeholders.
ISO 19115: ISO 19115 is an international standard that provides a framework for describing the geographic information and services, focusing on metadata. It aims to ensure that data can be easily understood, shared, and utilized across various systems and applications, enhancing data discoverability and interoperability.
Metadata profiling: Metadata profiling is the process of analyzing and evaluating the characteristics and quality of metadata associated with data sets. This helps organizations understand how well their metadata aligns with data standards, which is essential for effective data management, interoperability, and decision-making. By examining aspects such as completeness, consistency, and accuracy of metadata, organizations can enhance data usability and ensure compliance with relevant standards.
OGC Standards: OGC standards are a set of specifications developed by the Open Geospatial Consortium to ensure interoperability and integration of geospatial data and services across different platforms. These standards facilitate the sharing and use of geospatial information, enabling diverse systems to work together seamlessly, which is essential for effective data management and spatial analysis.
Precision: Precision refers to the consistency and reproducibility of measurements or data points, indicating how close multiple measurements are to each other. It highlights the degree of refinement and exactness in data collection, which is crucial in applications like navigation, data standards, and assessing spatial data quality. In geospatial contexts, precision helps determine the reliability of positioning systems and the integrity of spatial datasets.
SDI: SDI stands for Spatial Data Infrastructure, which refers to the framework of technologies, policies, and standards that facilitate the collection, management, and sharing of geospatial data. It enables organizations to effectively use spatial information for decision-making and planning. The implementation of SDI ensures that geospatial data is accessible, interoperable, and usable across different platforms and applications, enhancing the overall quality and consistency of spatial information.
WMS: WMS, or Web Map Service, is a standard protocol developed by the Open Geospatial Consortium (OGC) for serving georeferenced map images over the internet. It allows users to request map data from a server and receive rendered images that can be displayed on web applications, making it essential for interactive mapping and the sharing of geographic information.