Course Details

Metadata, Standards, and Spatial Data Infrastructure (SDI) Training Course

Introduction

In today's interconnected world, geographic information is a fundamental asset for effective governance, business operations, scientific research, and daily life. However, the true value of spatial data can only be realized when it is discoverable, accessible, understandable, and usable by diverse stakeholders. This necessitates a robust framework of Metadata, Standards, and Spatial Data Infrastructure (SDI).

Metadata – "data about data" – provides the essential descriptive information about spatial datasets, including their source, accuracy, quality, content, lineage, and access restrictions. Without comprehensive and standardized metadata, spatial data remains largely undiscoverable and unusable, akin to a library without a catalog. Standards provide the common language and protocols for how spatial data is collected, processed, stored, shared, and integrated across different systems and organizations. They ensure interoperability, enabling seamless exchange and use of geographic information regardless of its origin or the software used. Finally, a Spatial Data Infrastructure (SDI) is the overarching framework—encompassing policies, technologies, standards, human resources, and spatial data itself—that facilitates the effective discovery, access, sharing, and use of geographic information among all levels of government, the private sector, academia, and the public. An SDI is the glue that holds disparate geospatial initiatives together, promoting efficiency, reducing duplication, and fostering collaboration. Many organizations possess vast amounts of spatial data but struggle with fragmented data management, inconsistent quality, and limited data sharing capabilities due to a lack of understanding or implementation of these critical concepts.

Conversely, mastering metadata creation, adhering to spatial data standards, and understanding the principles of SDI empowers organizations to maximize the value of their geographic information assets, promote seamless data sharing, enhance data quality, and foster a truly integrated and collaborative spatial data ecosystem. This specialized skill set is crucial for building sustainable, interoperable, and impactful geospatial programs and for participating effectively in national and global spatial data initiatives. Our intensive 5-day "Metadata, Standards, and Spatial Data Infrastructure (SDI)" training course is meticulously designed to equip GIS professionals, data managers, IT specialists, policy makers, researchers, and anyone involved in the creation, management, or dissemination of spatial data with the essential knowledge and practical skills required to confidently implement best practices in metadata, apply spatial standards, and contribute to or lead SDI development initiatives.

Duration

5 Days

Target Audience

The "Metadata, Standards, and Spatial Data Infrastructure (SDI)" training course is designed for a wide range of professionals involved in the management, creation, use, and dissemination of geographic information. This includes:

• GIS Managers and Coordinators: Responsible for overseeing geospatial data assets.
• Data Managers and Stewards: Focusing on data quality, discoverability, and accessibility.
• GIS Analysts and Specialists: Involved in data creation, processing, and analysis.
• IT Professionals: Supporting geospatial infrastructure and data management.
• Policy Makers and Planners: Involved in developing and implementing spatial data policies.
• Government Officials: At local, regional, and national levels, working with public sector data.
• Academics and Researchers: Focusing on geospatial data management and interoperability.
• Consultants: Advising organizations on geospatial data strategies.
• Anyone responsible for the long-term value, accessibility, and sharing of spatial data.

Course Objectives

Upon successful completion of the "Metadata, Standards, and Spatial Data Infrastructure (SDI)" training course, participants will be able to:

• Understand the fundamental concepts and importance of metadata in GIS.
• Identify and apply relevant international and national spatial data standards.
• Comprehend the components, benefits, and challenges of building and maintaining a Spatial Data Infrastructure (SDI).
• Create, validate, and manage metadata for spatial datasets using appropriate tools and guidelines.
• Implement best practices for ensuring data quality, consistency, and interoperability through standards.
• Navigate and utilize spatial data portals and discover spatial data resources effectively.
• Understand the legal, policy, and institutional frameworks supporting SDI development.
• Formulate strategies for developing or enhancing an SDI within an organization or jurisdiction.

 Course Modules

Module 1: Introduction to Metadata and its Importance in GIS

• What is Metadata? Definition, purpose, and types (descriptive, administrative, structural).
• Why metadata is crucial for spatial data: Discoverability, usability, trust, accountability.
• Key elements of geospatial metadata: Identification, quality, spatial reference, distribution, lineage.
• The lifecycle of spatial data and the role of metadata at each stage.
• Consequences of poor or missing metadata for organizations and users.

Module 2: Spatial Data Standards: Concepts and Types

• What are spatial data standards? Their role in interoperability and data exchange.
• Categorization of spatial standards: Data content, data quality, data transfer, services.
• Introduction to key international standards organizations: ISO/TC 211, OGC (Open Geospatial Consortium).
• Overview of widely adopted standards: ISO 19115 (Metadata), ISO 19107 (Spatial Schema), OGC Web Services (WMS, WFS).
• The process of developing, adopting, and implementing spatial standards.

Module 3: Understanding and Implementing ISO 19115 (Geographic Information - Metadata)

• Deep dive into the ISO 19115 standard: Structure, core elements, and profiles.
• Practical exercises in interpreting and populating ISO 19115 metadata fields.
• Using metadata authoring tools (e.g., ArcGIS Pro, QGIS, online editors) to create ISO-compliant metadata.
• Validating metadata against the standard and addressing compliance issues.
• Managing metadata within geodatabases and file systems.

Module 4: Overview of Spatial Data Infrastructure (SDI)

• Defining SDI: Components (people, policies, technology, data, standards), vision, and purpose.
• Benefits of SDI: Enhanced data sharing, reduced costs, improved decision-making, economic growth.
• Levels of SDI: Local, regional, national, and global SDIs (e.g., GSDI, INSPIRE).
• Challenges in SDI implementation: Technical, institutional, legal, financial.
• Case studies of successful SDI initiatives and their impacts.

Module 5: Key Components of an SDI: Data, Services, and Portals

• Framework Data: Identifying fundamental datasets for an SDI (e.g., imagery, elevation, transport, addresses).
• Geospatial Services: Web Map Services (WMS), Web Feature Services (WFS), Catalog Services for the Web (CSW).
• Spatial Data Portals/Clearinghouses: Design, functionality, and user experience for data discovery.
• Data licensing, terms of use, and access policies within an SDI context.
• Integrating diverse data sources and services into a cohesive SDI.

Module 6: Policies, Legal Frameworks, and Institutional Arrangements in SDI

• The role of policy in driving SDI development and data sharing.
• Legal frameworks: Data ownership, intellectual property rights, privacy regulations.
• Institutional arrangements: Collaboration models, roles and responsibilities, governance structures.
• Funding models and sustainability for SDI initiatives.
• Capacity building and human resource development for SDI implementation.

Module 7: Data Quality and Provenance through Metadata and Standards

• Dimensions of data quality: Positional accuracy, thematic accuracy, completeness, logical consistency, temporal accuracy.
• Documenting data quality elements in metadata (e.g., lineage, data quality reports).
• Data Provenance: Tracking the history, transformations, and origin of spatial data.
• Using metadata to assess the fitness-for-purpose of spatial data.
• Best practices for integrating data quality assurance into geospatial workflows.

Module 8: Building and Sustaining an SDI: Practical Considerations

• Developing an SDI strategy: Vision, objectives, roadmap, and action plan.
• Stakeholder engagement and communication in SDI development.
• Selecting appropriate technologies and tools for SDI components.
• Monitoring and evaluating SDI performance and impact.
• Future trends in SDI: Cloud-native SDIs, AI/Machine Learning for data discovery, distributed ledger technologies.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com