Utility-scale wind and solar project siting prediction surfaces for the United States

These 8 rasters are results stemming from the following publication: Wu, Grace C., Yohan Min, Ranjit Deshmukh, et al. 2026. “Factors Shaping the Siting of Utility-Scale Solar and Wind Projects in the United States.” Environmental Research Letters. https://doi.org/10.1088/1748-9326/ae5faa. The file in this respository are as follows: pred_lasso_s.tif: prediction surface for solar (s) using lasso regression pred_lasso_w.tif: prediction surface for wind (w) using lasso regression pred_logReg_s.tif: prediction surface for solar (s) using logistic regression pred_logReg_w.tif: prediction surface for wind (w) using logistic regression pred_randomForest_s.tif: prediction surface for solar (s) using random forest pred_randomForest_w.tif: prediction surface for wind (w) using random forest pred_xg_s.tif: prediction surface for solar (s) using XGboost pred_xg_w.tif: prediction surface for wind (w) using XGboost Higher values indicate higher probability of siting, lower values indicate lower probability of siting. Values range fro 0 to 1. See manuscript for details on how each raster was generated. See https://spatialclimatesolutions.github.io/siting_dashboard for interactive visualizations of these data.