As we move into the 21st century, more and more importance is placed on data. Automation is starting to become essential, driven by industries such as electric vehicles, the upgrade program and the Net Zero Carbon race.
Data collection has often been done to meet the needs of the organization collecting it. Naturally, utilities, road authorities, telecom operators and ministries have taken the lead in using their own data for their own purposes, with the secondary notion of sharing it with others if necessary. However, what we are targeting now is the idea of a spatialized society.
As much as infrastructure assets themselves have intrinsic value, organizations are realizing that the data associated with those infrastructure assets has its own value. This value is naturally increased because the data follows the principles of the Geospatial Commission Q-FAIR Framework: Quality, Findable, Accessible, Interoperableand Reusable. So the value of data isn’t just about its original intent: it’s about how it can be adapted to larger goals. By applying the Q-FAIR framework, we can extend the value of our data with proportionally less effort than it took to capture it in the first place.
The Open Data Institute (ODI) highlights these key areas of a data infrastructure:
- Data Assets such as identifiers, registers and datasets
- Standards used to enable the sharing or exchange of data assets between multiple parties
- Technologies used to maintain and provide access to data assets
- Guidelines and policies that inform the use and management of data assets and the data infrastructure itself
- Organizations that govern the data infrastructure
- Communities or Suppliers involved in contributing to or maintaining data assets, and those affected by decisions made using data assets
All of this applies to modern and sustainable Spatial Data Infrastructures (SDIs), as we implement them to create sustainable ways to share our data and extend its value.
An SDI is essentially a coordinated set of policies, technical and data standards and interfaces that enable the discovery, use and reuse of geospatial information to provide additional uses. It is a formal means of increasing the value of data beyond its original purpose, with the aim of facilitating greater use, enabling new ways of working, and – ultimately, increasing the size economy.
As such, SDIs are an essential aspect of the “DNA” of any organization or country. In the UK, an SDI can create value from data to help:
Revitalizing networks such as improving our roads, railways and broadband coverage
Leveling up communities and regions that have been left behind by society and investment
Move to net zero greenhouse gas emissions by 2050, switching to cleaner and more sustainable energy
Building one million new homes to provide affordable housing for people who need it, where they need it
Preparing the UK for electric vehicles and autonomous transport
World leader in agricultural technology and land management
Ordnance Survey Ireland’s PRIME2 initiative is a good example of an SDI in action. PRIME2 provides data and APIs for a fully digital and object-based representation of real-world functionality with persistent identifiers and a defined lifecycle of functionality, not just a map snapshot of data. This initiative meets the definition of an IDS perfectly, as it covers policy, data models and the method of accessing them. PRIME2 creates the underlying framework on which to base government, business and individual data structures with a spatial aspect, and integrates them with each other. It’s not just about creating spatial or cartographic relationships as we traditionally would in GIS, but real connectivity – the actual relationships between real-world features to enable interoperability, modeling and scenario building planning and simulation.
The key principles for creating an SDI are:
- The possession – so that responsibilities for data provision are identified and shared
- Statement – so that the original use of the data (the use for which it was originally captured) is identified, as well as its potential future suitability for another use
- Consistency – using automated workflows, data activation stability and consistency over time is no longer a manual effort, making it even more reliable
- Transformation – ensure that data from whatever source adheres to applied policies, standards and descriptors (which may include a level of reshaping)
- Validation – ensure that the quality of the data is maintained, which is particularly important when the data is used outside of its original purpose
- To access – that the data can be easily discovered and accessed; its metadata description is consumable (by humans or other systems)
- Visualization – that the data can be visualized in different ways, especially mixed with other geospatial data depending on the use case
- Usability – outside of GIS, data needs to be enabled for use in other systems in a non-traditional way (often not rendered as a map, but as part of a completely different application)
Datasets exposed and curated under an SDI must anticipate a future level of integration, so it is essential that Q-FAIR principles are maintained – while preserving data quality.
For a more in-depth look, 1Spatial has published a small book on IDS. The book identifies a number of SDI examples already in progress, including OSi.
1Spatial exhibits at the prestigious Cambridge Conference 12-14and April, where leaders of the world’s national mapping organizations meet, usually every 4 years, to explore common challenges facing the global geospatial community. This year’s theme is how location data is at the forefront of the fight against the climate crisis and IDS is fundamentalfor that. At 1Spatial, we believe that when governments have access to consistent, up-to-date data, they are likely to do better and faster. the decisionsin the fight against climate change, while offering a smarter, safer and more sustainable world.
Next time we will see in more detail how we do it create a modern and sustainable IDS, and how Open Data standards are essential to this process.