Spatial Fairness Workflow

Research workflow for auditing spatial fairness with the original spatial scan setup and an HDBSCAN-based comparison.

The current pipeline reproduces the authors' experiments with SUL/MeanVar and adds an organic density-based alternative:

Authors: KMeans seeds -> square scan regions -> SUL
This work: HDBSCAN clusters -> organic regions -> SUL

Repository Structure

datasets/
  old/                 # Current CSV inputs: LAR, Crime, Semisynth, Synth_fair, Synth_unfair
src/
  main.py              # CLI entry point
  data_loading.py      # Dataset normalization and default experiment parameters
  experiments.py       # Experiment runner, logging, CSV output
  regions.py           # R-tree, grid, KMeans scan regions, overlap filtering
  visualization.py     # Folium maps with HDBSCAN convex hulls
  clustering/
    hdbscan.py         # HDBSCAN spatial partitioning
  metrics/
    group_fairness.py  # SUL, MeanVar, Monte Carlo thresholding
tests/                 # Unit tests for data loading, metrics, regions, HDBSCAN, maps

Setup

Requires Python 3.12 and uv.

uv sync

Run tests:

uv run python -m unittest discover -s tests

Running Experiments

The CLI has four commands:

uv run python src/main.py unrestricted --dataset lar --out outputs --no-maps
uv run python src/main.py one-partitioning --dataset crime --out outputs --maps
uv run python src/main.py multiple-partitionings --dataset semisynth --out outputs
uv run python src/main.py all --out outputs --no-maps

Use --maps to generate Folium HTML maps and --quiet to suppress progress logs. For faster exploratory runs, reduce Monte Carlo worlds or HDBSCAN settings:

uv run python src/main.py unrestricted \
  --dataset crime \
  --n-alt-worlds 20 \
  --hdbscan-fracs 0.001,0.002,0.005 \
  --out outputs \
  --maps

Experiment Modes

• unrestricted: reproduces the authors' unrestricted scan with 100 KMeans seeds and dataset-specific square radii, then compares against HDBSCAN clusters evaluated with SUL.
• one-partitioning: runs fixed grid partitioning, SUL, MeanVar, Monte Carlo thresholds, and HDBSCAN comparison. Defaults include LAR 100x50 and 25x12, Crime 20x20.
• multiple-partitionings: generates 100 random rectangular grids with dimensions sampled from 10..40 and reports MeanVar stability.
• all: runs the default reproduction suite across the main datasets.

Outputs

CSV outputs are written per dataset to avoid overwriting previous runs:

outputs/unrestricted_lar_regions.csv
outputs/unrestricted_crime_regions.csv
outputs/hdbscan_lar_comparison.csv
outputs/hdbscan_crime_comparison.csv
outputs/one_partitioning_lar.csv
outputs/multiple_partitionings_semisynth.csv

Maps are written under outputs/maps/, for example:

outputs/maps/unrestricted_lar.html
outputs/maps/unrestricted_crime.html

Key output columns:

• max_sul: strongest spatial unfairness likelihood found.
• signif_threshold: Monte Carlo threshold for significance.
• significant_regions: number of regions or clusters above the threshold.
• best_region_n, best_region_rate: size and local positive rate of the strongest region.
• noise_rate: HDBSCAN-only share of points not assigned to any cluster.

Use max_sul >= signif_threshold as the basic significance check.

Datasets

Current inputs live in datasets/old/:

• LAR.csv: outcome column action_taken, with 3 normalized to 0.
• Crime.csv: outcome column pred.
• Semisynth.csv, Synth_fair.csv, Synth_unfair.csv: outcome column label.

See datasets/README.md for dataset organization notes.

Notes

MeanVar is kept as a baseline from prior work and is most meaningful on non-overlapping partitions. The main comparison for the proposed approach is HDBSCAN-generated regions evaluated with SUL and Monte Carlo significance testing.