I read story after story of emerging renewable energy plants, so it was surprising to find out that there had been little mapped information on where all of the wind and solar power infrastructure lived — until now.
The big map picture was absent until lead researcher and PhD student Sebastian Dunnett in Biological Sciences at the University of Southampton changed all that. Many will now welcome the news that solar and wind energy sites are easily accessed online at your fingertips.
The article “Harmonised global datasets of wind and solar farm locations and power” was published in the scientific journal Scientific Data. The work came about with the use of readily accessible OpenStreetMap data. The maps present global, open-access, harmonized spatial datasets of wind and solar installations. There is a user-friendly code to enable users to easily create newer versions of the dataset. The journal article points out that it also includes first order estimates of power capacities of the installations.
“They extracted grouped data records tagged ‘solar’ or ‘wind’ and then cross-referenced these with select national datasets in order to get a best estimate of power capacity and create their own maps of solar and wind energy sites. The data show Europe, North America and East Asia’s dominance of the renewable energy sector, and results correlate extremely well with official independent statistics of the renewable energy capacity of countries.”
Lead researcher Sebastian Dunnett explains: “While global land planners are promising more of the planet’s limited space to wind and solar energy, governments are struggling to maintain geospatial information on the rapid expansion of renewables. Most existing studies use land suitability and socioeconomic data to estimate the geographical spread of such technologies, but we hope our study will provide more robust publicly available data.”
If you want to get technical, here’s more info on how the data is saved: “This dataset is stored in three different formats: shapefiles for use with GIS software, geopackage for open-source usage, and .csv format for ease of use in any statistical software. Two final datasets were produced that represent the best publicly available global, harmonized geospatial data for field-scale solar PV and wind installations (Fig. 5). We provide vector data (point and polygon) for grouped installations (more than two features; Methods), in Eckert IV equal area projection.”
The University of South Hampton relates that by accurately mapping the development of these renewable energy farms, the researchers believe they offer insight into the footprint of renewable energy on vulnerable ecosystems. They hope their work will aid planners to understand such effects.
The full PDF of the researchers’ article is here.
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