Analyzing siting trends of utility-scale solar and wind

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Project Workflow

The analysis is structured as a sequential pipeline. The scripts are intended to be run in the following order to reproduce the analysis, from initial data processing to final figure generation.

1. Setup & Data Preparation:

   • Rasterizes the solar project location data (tech.R).

2. Predictor and Model Matrix Generation:

   • Samples pseudo-absence locations for the regression models (absence.R).
   • Combines all predictor layers (e.g., climate risk, land use, policy incentives) into a single raster stack (stack.R).
   • Performs zonal statistics to extract predictor values for all presence and pseudo-absence locations (zonal.R).
   • Generate probability surfaces from modeling (modeling.R).

3. Modeling & Analysis:

   • Loads all necessary libraries and custom functions (function.R).
   • Run intermediat models, and generates the foundational datasets (data.R).

4. Visualization & Output:

   • Generates and saves the main figures for the publication (plot.R).
   • Generates and saves all supplementary figures (SI.R).

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Script Descriptions

Core Scripts

• function.R: The foundational setup script. It loads all required R packages, defines custom functions used throughout the analysis, and sets global environment options.
• data.R: Orchestrates the initial data generation pipeline, creating a complete and clean dataset for the modeling phase.
• modeling.R: This script takes the processed data to prepare regression models.

Data Processing Scripts

• tech.R: Rasterizes the solar project data, transforming vector points/polygons to a raster format for alignment with predictor layers.
• absence.R: Performs pseudo-absence sampling to create a contrastive baseline for the model.
• stack.R: Creates a unified raster "stack" by combining multiple predictor variable files into a single object.
• zonal.R: Extracts the predictor variable values for each presence and pseudo-absence point.

Figure Generation Scripts

• plot.R: Contains all the plotting code to create the final figures for the main body of the manuscript.
• SI.R: Dedicated to producing all visual outputs for the Supplementary Information (SI) section.

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How to Run the Analysis

1. Dependencies: The complete list of required R packages is located at the top of the function.R script.

2. Running the Analysis:

   • Begin by running the data.R script to generate the necessary datasets.
   • Follow the sequence outlined in the Project Workflow section.
   • After running the models, execute plot.R and SI.R to generate the final outputs.

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Repository Structure

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├── syntax/
│   ├── function.R
│   ├── data.R
│   ├── tech.R
│   ├── absence.R
│   ├── stack.R
│   ├── zonal.R
│   ├── modeling.R
│   ├── plot.R
│   └── SI.R
│
├── derive/
│   └── ... (Input data files)
│
└── fig/
    └── ... (Output figures)