This repository contains an archival copy of the FSA_Counties_dd22_NonGeneralized dataset, originally distributed by the U.S. Department of Agriculture (USDA) Farm Service Agency (FSA) as part of the Common Land Unit (CLU) geospatial data series. This dataset was obtained via a Freedom of Information Act (FOIA) request.

📦 Dataset Overview

• Title: FSA_Counties_dd22_NonGeneralized
• Source: USDA Farm Service Agency (FSA)
• Format: ESRI File Geodatabase (.gdb)
• FOIA Request: 2026-FSA-03465-F (R. Kyle Bocinsky, Montana Climate Office)
• Distribution Type: Public archival for research and historical purposes
• Date of Archive: 2026-04-29

📂 Contents

The zipped geodatabase includes polygon features representing U.S. counties, attributed with identifiers used by the FSA for administrative and mapping purposes. Unlike the dd17 schema, this is the non-generalized version, containing full-resolution county boundary geometries.

• FSA_Counties_dd22_NonGeneralized.gdb.zip – Original USDA File Geodatabase
• fsa-counties-dd22.topojson – Simplified TopoJSON version with pre-inset Alaska/Hawaii/Puerto Rico (see below)
• fsa-counties-dd22.R – R script that produces the Simplified TopoJSON versions

📋 FOIA Provenance

The FSA_Counties_dd22_NonGeneralized geodatabase was obtained through FOIA request 2026-FSA-03465-F, submitted by R. Kyle Bocinsky (Montana Climate Office, University of Montana) on March 18, 2026. The complete FOIA correspondence and related materials are archived in foia/2026-FSA-03465-F Bocinsky.zip.

🧾 Field Descriptions

🗂️ Simplified TopoJSON Version

A simplified version of the FSA_Counties_dd22_NonGeneralized dataset is included in this repository as fsa-counties-dd22.topojson. This version was created to reduce geometric complexity and ensure compatibility with common web mapping tools.

🔧 Processing Steps

This workflow processes the USDA Farm Service Agency (FSA) county definitions to produce a clean, simplified, and TopoJSON-compatible version of the dataset, suitable for use in web mapping applications.

1. Load and Preprocess FSA County Data

• Load the original county boundaries from a zipped file geodatabase: FSA_Counties_dd22_NonGeneralized.gdb.zip
• Exclude U.S. territories by filtering out counties with FIPSST codes:
  • American Samoa (60), Guam (66), Northern Mariana Islands (69), Puerto Rico (72), U.S. Virgin Islands (78), etc.
• Retain only the FSA_STCOU field and rename it to id for clarity.

2. Normalize Geometries

• Perform a round-trip to GeoJSON:
  • Write the filtered data to a temporary .geojson file.
  • Read it back into R using sf::read_sf().
• This step removes non-standard geometries (e.g., curved edges or arcs) that may interfere with simplification or projection.

3. Transform and Simplify Geometries

• Project all features to the WGS84 coordinate reference system.
• Use rmapshaper::ms_explode() to break multi-part polygons into single-part features.
• Recombine features by id using ms_dissolve() to ensure unique county geometries.
• Apply ms_simplify(keep = 0.008) to reduce geometric complexity while preserving topology.

4. Clip to Official TIGER/Line Boundaries

• Download generalized U.S. county boundaries via the tigris package:
  • Use cb = TRUE and resolution = "5m" for cartographic accuracy.
• Use ms_explode() and ms_dissolve() to produce a clean national boundary.
• Clip FSA geometries to the national boundary using ms_clip(), removing slivers and enforcing alignment.

5. Further Geometry Cleaning

• Use sf::st_make_valid() to repair any topological errors.
• Repeat explode/dissolve operations as needed to clean up geometry structure.
• Reposition Alaska, Hawaii, and outlying areas using tigris::shift_geometry() for optimal layout in web maps.
• Convert all features to MULTIPOLYGON geometries.
• Sort features by id to ensure consistent ordering.

6. Export Cleaned Data

• Save the cleaned and simplified geometries to fsa-counties-dd22.geojson.

7. Post-process with Mapshaper (CLI)

• Use Mapshaper to:
  • Clean and reorient geometry: -clean rewind
  • Derive a new state field from the first two characters of id: -each 'state=id.slice(0,2)'
  • Merge counties into states: -dissolve field=state
  • Rename layers: counties and states
  • Quantize coordinates for TopoJSON compression: quantization=1e5
• Export the result as fsa-counties-dd22.topojson.

8. Cleanup

• Remove the temporary GeoJSON file to tidy the workspace.

🛠️ How to Use

1. Unzip the FSA_Counties_dd22_NonGeneralized.gdb.zip file.
2. Open the .gdb in a GIS software environment such as QGIS or ArcGIS Pro.
3. Use the layer properties to explore attributes and spatial coverage.

📍 Quick Start: Visualize the FSA_Counties_dd22 topojson data in R

This snippet shows how to load the fsa-counties-dd22.topojson file from the archive and create a simple map using sf and ggplot2.

# Load required libraries
library(sf)
library(ggplot2) # For plotting
library(tigris)  # For state boundaries
library(rmapshaper) # For innerlines function

## Download the FSA_Counties_dd22 archive
counties <-
  sf::read_sf("https://sustainable-fsa.com/fsa-counties-dd22/fsa-counties-dd22.topojson",
              layer = "counties") |>
  sf::st_set_crs("EPSG:4326") |>
  sf::st_transform("EPSG:5070")

# Plot the map
ggplot(counties) +
  geom_sf(data = sf::st_union(counties),
          fill = "grey80",
          color = NA) +
  geom_sf(data = counties,
          aes(fill = state),
          color = NA,
          show.legend = FALSE) +
  geom_sf(data = rmapshaper::ms_innerlines(counties),
          fill = NA,
          color = "white",
          linewidth = 0.1) +
  geom_sf(data = counties |>
            dplyr::group_by(state) |>
            dplyr::summarise() |>
            rmapshaper::ms_innerlines(),
          fill = NA,
          color = "white",
          linewidth = 0.2) +
  labs(title = "FSA County Administrative Boundaries",
       subtitle = "Derived from the FSA_Counties_dd22_NonGeneralized dataset") +
  theme_void()

📌 Background

The dataset originates from the dd22 schema, a geospatial data standard used by the USDA Farm Service Agency (FSA) for structuring county-level datasets. It serves as a spatial index for county-level geospatial products and is used in conjunction with the Common Land Unit (CLU) framework. The “NonGeneralized” designation indicates that this version contains full-resolution boundary geometries, as opposed to simplified versions used for cartographic display.

The dataset remains of historical and analytical interest — particularly for referencing USDA program boundaries, disaster assistance eligibility, and other geospatial analysis across agriculture and conservation.

📜 Citation

If using this data in published work, consider citing it as:

USDA Farm Service Agency. FSA_Counties_dd22_NonGeneralized Geospatial Dataset. Obtained via FOIA request 2026-FSA-03465-F. Accessed via GitHub archive, YYYY.

📄 License

Data in the FSA_Counties_dd22_NonGeneralized.gdb.zip archive were produced by the United States Department of Agriculture (USDA), which are in the public domain under U.S. law (17 USC § 105).

You are free to:

• Use, modify, and distribute the data for any purpose
• Include it in derivative works or applications, with or without attribution

If you modify or build upon the data, you are encouraged (but not required) to clearly mark any changes and cite this repository as the source of the original.

No warranty is provided. Use at your own risk.

The derivative fsa-counties-dd22.topojson file was created by R. Kyle Bocinsky and is released under the Creative Commons CCZero license.

The fsa-counties-dd22.R script is copyright R. Kyle Bocinsky, and is released under the MIT License.

⚠️ Disclaimer

This dataset is archived for reference and educational use. It may not reflect current administrative boundaries and should not be used for official USDA program administration. Always consult the USDA or state FSA office for current data.

👏 Acknowledgment

This work is part of the Enhancing Sustainable Disaster Relief in FSA Programs: Non-stationarity at the Intersection of Normal Grazing Periods and US Drought Assessment project. It is supported by US Department of Agriculture Office of the Chief Economist (OCE), Office of Energy and Environmental Policy (OEEP) funds passed through to Research, Education, and Economics mission area. We also acknowledge and appreciate the assistance of the USDA Climate Hubs in securing these data.

✉️ Contact

Please contact Kyle Bocinsky (kyle.bocinsky@umontana.edu) with any questions.