Misplaced Trust

This repository accompanies Grist's Misplaced Trust investigation. It allows users to build and modify the dataset underlying the project. For more details on our methodology, please view METHODOLOGY.md. A user guide is available at USER-GUIDE.md. Final built datasets are available in the public_data folder.

The investigation was written and reported by Tristan Ahtone, Robert Lee, Amanda Tachine, An Garagiola, Audrianna Goodwin, Maria Parazo Rose, and Clayton Aldern. This repository was authored by Maria Parazo Rose, Clayton Aldern, Marcelle Bonterre, and Parker Ziegler.

Looking for the code behind the interactives in the project? That repo is here.

public_data also includes one additional xlsx file related to the Misplaced Trust investigation but underlying a separate piece published by Grist and High Country News. Grist_STLs-Clipped-to-Reservations.xlsx effectively subsets the main dataset to include only trust lands present within Federal Indian reservation boundaries.

Installation

Python Version Requirement

This project currently requires using Python <= 3.10.4. To set the appropriate Python version locally, consider using a Python version manager like pyenv.

Prerequisities

This project uses Git LFS to store .zip, .dbf, .shp, and .geojson files remotely on GitHub rather than directly in the repository source. In order to access these files and build the datasets locally, you'll need to do the following:

1. Install Git LFS. Follow the installation instructions for your operating system.
2. At the terminal, run the following two commands (without typing the dollar sign):

$ git lfs install
$ git lfs pull

Together, these commands will install the Git LFS configuration, fetch LFS changes from the remote, and replace pointer files locally with the actual data.

Dependencies

After completing the above, install Python dependencies. At the terminal, run the following command (again, omit the dollar sign):

$ pip install -e .

This only needs to be done the first time you run the script.

Building the datasets

All functionality is orchestrated by a single top-level command run.py. To see a listing of all available command options, run the following command:

$ DATA=data python run.py --help

run.py expects a particular directory structure for the datasets, which is already enforced by this repository's directory structure. As such, you should not move any files from their current locations.

State Trust Lands (STL) Dataset

The STL dataset is built in stages, with the output of each stage becoming the input of the next stage. The following assumes your data directory is itself called data and all paths will refer to it as such.

Stage 1

To execute Stage 1, run the following command at the terminal:

$ DATA=data python run.py stl-stage-1

This command:

• Gathers the raw data from both remote state-run servers and the input data directory.
• Collects all fields-of-interest from the raw data and normalizes the naming across datasets.
• Unifies all individual states' data into one large dataset.

Individual state datasets are written to data/stl_dataset/step_1/output/merged/<state-abbreviation>.[csv,geojson]. The unified dataset is written to data/stl_dataset/step_1/output/merged/all-states.[csv,geojson].

Stage 2

To execute Stage 2, run the following command at the terminal:

$ DATA=data PYTHONHASHSEED=42 python run.py stl-stage-2

This command matches activity information to the parcels from the unified multi-state dataset output in Stage 1 (data/stl_dataset/step_1/output/merged/all-states.[csv,geojson]). The data directory for Stage 2 already includes state-specific information about the activities occuring on all parcels.

The output of this stage is written to data/stl_dataset/step_2/output/stl_dataset_extra_activities.[csv, geojson]

Stage 2.5 (Manual)

Stage 2.5 involves manually enriching the unified dataset from Stage 2 (data/stl_dataset/step_2/output/stl_dataset_extra_activities.[csv, geojson]) with land-cession information for each parcel. The new dataset should be named stl_dataset_extra_activities_plus_cessions.csv and located at data/stl_dataset/step_2_5/output/ (though, as evidenced by the Stage 3 input details, the title is not important to the code).

IMPORTANT 💡 Ensure there is only one CSV file in this directory.

Stage 3

To execute Stage 3, run the following command at the terminal:

$ DATA=data python run.py stl-stage-3

This command will take the first CSV found in data/stl_dataset/step_2_5/output/ and augment it with the prices paid for each parcel of land. The data directory for Stage 3 already contains a listing a CSV with the listing of prices paid for land (data/stl_dataset/step_3/input/Cession_Data.csv).

The output of this stage is written to data/stl_dataset/step_3/output/stl_dataset_extra_activities_plus_cessions_plus_prices.[csv, geojson]

Stage 4

To execute Stage 4, run the following command at the terminal:

$ DATA=data python run.py stl-stage-4

This command will calculate summaries connecting universities and cessions to tribes using the output of Stage 3 (data/stl_dataset/step_3/output/stl_dataset_extra_activities_plus_cessions_plus_prices.[csv, geojson]).

The outputs of this stage are three files, written to:

• data/stl_dataset/step_4/output/university-summary.csv
• data/stl_dataset/step_4/output/tribe-summary.csv
• data/stl_dataset/step_4/output/tribe-summary-condensed.csv

Private Holdings Dataset

—Coming soon—