Mapping Wind and Population: Using raster algebra and value-by-alpha mapping

I came back to grad school with the plan of studying something that might have a positive impact on the world. When I lived in Southern California, I loved driving out to the desert through the San Gorgonio Wind Farm. It was during one of those many drives when I realized I could make a career out of studying renewable energy. I found it interesting and in studying the geographic/mapping issues around it, I might be able to make a positive impact on the environment. I’m still figuring out how exactly to do that, but in the meantime I have had some time to make cool maps of wind energy.

Wind Farm Picture

Me in the San Gorgonio Wind Farm near Palm Springs, CA

The original goal of this little study was to attempt to find places or a relationship between places where there was high wind and high population. Why? Long story short: wind energy isn’t very useful if it is far from the grid and the people using it. This is where people live:

Population Density

Population Density in the Lower 48 By County

Here is where wind is high:

Wind Speed Map

Wind Speeds in the Lower 48 (Wind data interpolated from 669 METAR weather stations in the lower 48)

Using Raster Algebra to Find Where Wind and Population are High

I converted the population data from polygons to a raster based on my 5-class quantile classification scheme. I then used raster algebra to find where wind and population were both high.

Wind Speeds and Population

Wind Speeds and Population results from Raster Algebra Analysis

The raster algebra map loses a bit of information because it becomes impossible to understand what the middle colors on the legend mean. We can’t tell the difference between places that might have high wind but low population and vice versa.

Using Value-By-Alpha to get a better picture

The raster algebra approach does get me to my end goal of understanding where high winds and high population are collocated, but the technique used on the map below called Value-By-Alpha has been used in other bivariate maps (namely election maps). Basically, the deeper blue, the higher the wind, and the darker, the greater the population. The same general pattern appears, places on the eastern edge of the Great Plains are high in population and also have high winds, but now we can see where high winds and high populations are as well as understand where there might be high wind but low population and vice versa.

Value-By-Alpha Map of Wind Speeds and Population

Value-By-Alpha Map of Wind Speeds and Population

Obviously there are still many limitations to this study, mostly the issue of scale and the lack of understanding of where people actually want a backyard of wind turbines, but those are all issues for another day. In the meantime, I think these maps are beautiful and they offer a better understanding of the geographic relationship between people and renewable energy. Peace, Fish.

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One Response to Mapping Wind and Population: Using raster algebra and value-by-alpha mapping

  1. Dneiz says:

    I love your study and maps !!! it’s fun to read and very informative :) Good Job!!!

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