How can you ensure accessibility of geospatial data in agriculture information systems?

2 Geospatial data quality

Geospatial data quality refers to the degree to which your data meets the requirements and expectations of your users. It can affect the reliability, accuracy, and usability of your data, as well as the outcomes of your decision-making processes. To ensure geospatial data quality, you need to assess and document various aspects of your data, such as completeness, consistency, validity, timeliness, and lineage. You can use tools and methods such as data quality indicators, data quality models, and data quality assurance procedures to measure and improve your data quality.

3 Geospatial data interoperability

Geospatial data interoperability is the ability of your data to be shared and used by different systems, applications, and users. It can enable you to access and combine data from various sources, enhance your data analysis and visualization capabilities, and support collaboration and innovation. To achieve geospatial data interoperability, you need to adopt common standards, vocabularies, and ontologies for your data, as well as use open and web-based platforms and services for data dissemination and exchange.

4 Geospatial data accessibility

Geospatial data accessibility is the extent to which your data is available and usable by your intended users, which can be impacted by legal, ethical, technical, and organizational aspects of your data management and sharing practices. To improve geospatial data accessibility, you need to comply with relevant laws and regulations regarding data protection, privacy, security, and intellectual property rights. Additionally, you should respect the rights and interests of your data providers, users, and stakeholders. Additionally, you need to make sure your data is compatible with the standards and formats that your users expect and prefer. Furthermore, you should provide adequate and user-friendly data documentation, metadata, and services to facilitate data discovery, access, and use. Finally, it's important to establish an effective and efficient data governance and stewardship structure as well as foster a culture of data sharing and reuse.

5 Geospatial data best practices

Geospatial data best practices are guidelines and recommendations that can help you implement and improve your geospatial data standards, quality, interoperability, and accessibility. They can help you optimize your data management and sharing workflows, enhance your data performance and value, and avoid common pitfalls and challenges. Some examples of geospatial data best practices are the FAIR principles, the GEOSS Data Sharing Principles, and the AGROVOC Controlled Vocabulary.

6 Geospatial data resources

Geospatial data resources are sources of information and support that can help you learn more about geospatial data standards and best practices, as well as find and use geospatial data for your agriculture information systems. They can include publications, websites, portals, databases, tools, and networks that provide geospatial data knowledge, guidance, and access. Some examples of geospatial data resources are the World Bank Geospatial Data Portal, the Global Agricultural Concept Scheme, and the CGIAR Platform for Big Data in Agriculture.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?