Mapping Wind Turbines

R
energy
mapping
leaflet
energy
Author

Andy Pickering, PhD

Published

November 6, 2024

Introduction

For day 1 (points) of the 30 Day Map Challenge, I decided to map locations of wind turbines in Iowa using data from the US Wind Turbine Database (USWTDB). I drove through Iowa on a road trip last year and was amazed to see how many turbines there were. It was especially mesmerizing at night when all the red warning lights on top of them were blinking in sync. The USWTDB has a nice online viewer, but I chose to get the data via their API and plot the data in R.

USWTDB Citation:

Hoen, B.D., Diffendorfer, J.E., Rand, J.T., Kramer, L.A., Garrity, C.P., and Hunt, H.E., 2018, United States Wind Turbine Database (ver. 7.1, August 2024): U.S. Geological Survey, American Clean Power Association, and Lawrence Berkeley National Laboratory data release, https://doi.org/10.5066/F7TX3DN0.

Load libraries

Code 
library(httr)
library(dplyr)
library(leaflet)
library(leaflegend)
library(DT)
1
Interactive mapping
2
Map legends
3
Nice data tables

Data

First I will get data for one state using the API:

Code 
wh_state <- "IA"

req_url <- paste0("https://eersc.usgs.gov/api/uswtdb/v1/turbines?t_state=eq.", wh_state)

resp <- httr::GET(req_url)

df <- jsonlite::fromJSON(httr::content(resp, as = "text", encoding = "UTF-8"))

df |> head() |> DT::datatable(options = list(pageLength = 5), rownames = FALSE)
Table 1: Example wind turbine data from USWTDB API

Map of turbine locations

I used {leaflet} Cheng et al. (2024) to make an interactive plot of the individual wind turbine locations.

Code 
leaflet(data = df) |>
  addProviderTiles(provider = providers$CartoDB.Positron) |>
  addCircleMarkers(lng = ~xlong, lat = ~ylat, 
                   stroke = FALSE, 
                   radius = 2)
Figure 1: Map of wind turbine locations in IA. Data from USWTDB

Group by project

There are a lot of individual turbines that are part of a smaller group of projects, so I’ll group by the project and compute the total capacity and mean lat/lon.

Code 
projects <- df |>
  group_by(p_name) |>
  summarise(n_turbines = n(),
            total_cap_MW = round(sum(t_cap, na.rm = TRUE)/1e3),
            lat_mean = mean(ylat, na.rm = TRUE),
            long_mean = mean(xlong,na.rm = TRUE)) |>
  arrange(desc(total_cap_MW))

projects |> select(p_name, n_turbines, total_cap_MW) |> 
  DT::datatable(options = list(pageLength = 5), rownames = FALSE)
Table 2: Table of projects and total capacity for IA

Map projects

I decided to make a second map, where I plot a circle for each project with size proportional to total capacity

  • I used {leaflegend} Roh (2024) to make the legend for circle size.
Code 
# define some plot parameters that are repeated in the code
basesz <- 7
wh_col <- "green"

leaflet(data = projects) |>
  addTiles() |>
    leaflegend::addSymbolsSize(lng = ~long_mean, lat = ~lat_mean, 
                   values = ~total_cap_MW, 
                   shape = 'circle', 
                   color = wh_col,
                   opacity = 0.5,
                   baseSize = basesz, 
                   popup = paste(projects$p_name, "<br>", projects$total_cap_MW, "MW")
                   ) |>
  leaflegend::addLegendSize(values = ~total_cap_MW, 
                color = wh_col, 
                shape  = 'circle', 
                breaks = 4,
                baseSize = basesz,
                title = 'Capacity (MW)')
Figure 2: Map of mean wind project locations and total capacity by project in IA. Data from USWTDB

SessionInfo

R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Sonoma 14.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Denver
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] DT_0.33          leaflegend_1.2.1 leaflet_2.2.2    dplyr_1.1.4     
[5] httr_1.4.7      

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5             cli_3.6.3               knitr_1.48             
 [4] rlang_1.1.4             xfun_0.49               renv_1.0.9             
 [7] generics_0.1.3          jsonlite_1.8.9          glue_1.8.0             
[10] htmltools_0.5.8.1       sass_0.4.9              fansi_1.0.6            
[13] rmarkdown_2.29          jquerylib_0.1.4         crosstalk_1.2.1        
[16] evaluate_1.0.1          tibble_3.2.1            fastmap_1.2.0          
[19] yaml_2.3.10             lifecycle_1.0.4         compiler_4.4.1         
[22] htmlwidgets_1.6.4       pkgconfig_2.0.3         rstudioapi_0.17.1      
[25] leaflet.providers_2.0.0 digest_0.6.37           R6_2.5.1               
[28] tidyselect_1.2.1        utf8_1.2.4              curl_5.2.3             
[31] pillar_1.9.0            magrittr_2.0.3          bslib_0.8.0            
[34] withr_3.0.2             tools_4.4.1             cachem_1.1.0

References

Cheng, Joe, Barret Schloerke, Bhaskar Karambelkar, and Yihui Xie. 2024. “Leaflet: Create Interactive Web Maps with the JavaScript ’Leaflet’ Library.” https://CRAN.R-project.org/package=leaflet.
Roh, Thomas. 2024. “Leaflegend: Create Custom Legends for Leaflet.” https://leaflegend.roh.engineering.