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Monica_55905 Aug 03

Spatial Analysis: only as good as the data that drives it

Spatial Analysis: only as good as the data that drives it image
Access to high quality, attribute rich spatial data allows complex questions to be answered – and understood. An article written by Matthew Barlow, CEO and co-founder at
A picture – or a map – is worth a thousand words. A robust spatial analysis presented to policymakers, key stakeholders, investors and others such as developers will help make sense of what is, in fact, a sea of spatial data.

But a spatial analysis is only as good as the data that drives it. Garbage in, garbage out. A model output can only be as good as the data input. There are many sources of spatial data freely available online: global wind speed models at 250m resolution; remotely sensed land cover and land use datasets, digital elevation models at 30m resolution. But there are also data gaps – and one of the most glaring gaps is infrastructure information.

A spatial analysis for renewable energy project site finding can be imagined as a complex Venn diagram. Project developers are looking for those overlaps where the data values are in their prime threshold. The best of the best. For example, in offshore wind consider 2 parameters: water depth and wind speed. The prime potential from a foundation perspective is 20m to 50m water depth. Deep enough that a construction vessel can access it, but not too deep to preclude a fixed foundation. Now selecting those shallow areas that have a wind speed > 7.5m/s gives a specific spatial area representing prime seabed locations. From here energy yield calculations can be derived and more detailed analyses may be performed. Adding additional input parameters will also help quantify the data overlaps in the form of spatial levelised cost of energy heat maps.

But that is only two parameters. Finding the right place for a project – or understanding resource potential across an entire country - combines dozens of input parameters: wind speed, water depth, seabed geological conditions, wave heights, tidal ranges and more… and just as importantly, distances to infrastructure. How close is the nearest construction port? And ports for operations and maintenance? What is the substation distance – and therefore the onshore and offshore cable lengths required to connect that location to the grid? The list goes on.

For this, data sharing is key. Facilities and utilities need to share their infrastructure data. If they do not currently have it in an accessible format, then this needs to take priority. Access to high quality, attribute rich spatial data allows complex questions to be answered – and understood.


Take for example, substation and grid or distribution data. As a key driver in project costs, it is a specific area that could benefit from a coordinated approach. - first, where it isn't already, the infrastructure needs to be digitised (made digital) and shared in an accessible format. Where are the substations? What capacity is available? Where are the load centres or areas of high demand? From there the process can be digitalised  - or leveraged to enable decisions – and will allow transformation of renewable energy site finding and planning.

With digitalization and data analysis, any country, company or organization can have the knowledge they need to implement their energy transition policy in an effective and coordinated approach.

To achieve this, international agreement and cooperation are key. A region-wide, country-wide, or indeed a worldwide quality-assured data repository with rich and consistent attribution will ensure governments, policy makers, stakeholders, investors, and developers have robust spatial analyses available to support the cost-effective deployment of wind, solar and other renewable technologies and therefore facilitate a smooth and sensible energy transition.