r-geo-viz

Reconstructed Prompt History

Based on the structure, logic, and explicit comments embedded within the codebase, it’s clear this project evolved through an iterative, conversational process with an AI.

Here is a reconstructed timeline of the types of prompts that likely led to the current state of the repository:

Phase 1: Foundation & Data Plumbing

The project started with basic data ingestion and geospatial alignment.

• “Write an R script using the terra package to load US county shapefiles, filter for Oregon and California, and plot them.”
• “Create utility functions to easily load historic fire shapefiles and project them to match the Coordinate Reference System (CRS) of a target raster.”
• “Read the georeferenced forest map rasters from the data/forest_maps/ directory and overlay them on the county boundaries.”

Phase 2: Iterative Map Generation & Layout Tuning

As basic maps were generated, the prompts shifted toward formatting, batch processing, and aesthetic layout.

• “Write a loop (01_plot_all.R) to generate individual maps for every raster found in the directory. Add a legend and a bounding box showing the scanned map extent.”
• “Add a text box panel in the lower-left corner of the maps to imprint the exact prompt used to generate them.”
• Explicit Prompt found in 01_plot_all.R: “expand context to the southwest to accommodate the text box panel.” (The model was asked to asymmetrically pad the map limits to make room for the UI element).

Phase 3: Targeted Regional & Temporal Analysis

The focus then narrowed to specific subsets of the data.

• “Create a composite map focusing specifically on Southwest Oregon.” (02_plot_southwest.R)
• “Filter the historic fire dataset to only show fires that occurred in the year 1900.” (03_plot_southwest_1900.R)
• “Take the two westernmost maps (Plates CXXVI and CXXVII), crop their outer borders/collars, and overlay them natively. Output both a standard version and a massive, untouched high-resolution version.” (04_plot_western_1900.R)

Phase 4: Complex Synthesis & Visualizations

The user began asking for large-scale, multi-layered visualizations.

• “Create a massive overall composite (05_california_composite.R) projecting all maps to Web Mercator. Aggregate all fires by decade and plot them with a color gradient.”
• Explicit Prompt found in 05_california_composite.R: “flip the color scheme so that red is oldest and yellow is newest.” (This shows the user iterating on the visual design).
• “Overlay the California FRAP dataset onto the specific Placerville, Big Trees, and Port Orford quadrangles, cropping the vector data precisely to the raster bounds.” (07_frap_overlay.R)

Phase 5: Machine Vision & Image Processing

The project moved beyond simple rendering into actual computer vision and algorithmic analysis.

• “Using terra, write a script (06_detect_burnt.R) to detect burnt forest areas on Plate CXXVI. Use a reference image (burned.jpg) and k-means clustering to isolate reddish and greenish pixels, find where they intersect, and output the result as a GeoJSON.”

Phase 6: Orchestration

Finally, tying it all together.

• “Create a master driver script (run_all.R) that loops through scripts 01 to 07, executing them in sequence with proper error handling and timestamped logging.”

---

Summary: The progression clearly moves from “load this data” -> “make it look good” -> “filter by these specific criteria” -> “synthesize everything into one big map” -> “use algorithms to extract new data from the images.”