Datasets:

lukeslp
/

us-inequality-atlas

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
source: string
table: string
description: string
records: int64
columns: list<item: string>
fips_column: string
collection_date: timestamp[s]
license: string
vs
source: string
table: string
variable: string
description: string
geography: string
record_count: int64
collection_date: timestamp[s]
api_url: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 572, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              source: string
              table: string
              description: string
              records: int64
              columns: list<item: string>
              fips_column: string
              collection_date: timestamp[s]
              license: string
              vs
              source: string
              table: string
              variable: string
              description: string
              geography: string
              record_count: int64
              collection_date: timestamp[s]
              api_url: string

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US Inequality Atlas

County-level inequality data for all ~3,200 US counties, keyed on 5-digit FIPS codes. Covers food deserts, healthcare access, housing affordability, hospital infrastructure, veteran demographics, disability prevalence, income inequality (Gini coefficient), education attainment, unemployment, and poverty depth.

I assembled this from Census ACS, CMS, USDA, and HRSA data for the inequality visualization series at dr.eamer.dev/datavis. Every file uses FIPS codes as the merge key, so you can join any combination.

Part of the Data Trove collection.

What's Inside

Food Deserts (`food_deserts/`)

File	Records	Source
`food_desert_merged.csv`	3,222 counties	Census ACS 2021 + USDA Food Access Atlas 2019
`state_rankings.json`	50 states	Aggregated state-level rankings
`worst_counties.json`	Top worst	Counties with highest food desert scores
`children_impact.json`	--	Child food insecurity indicators
`snap_gap_states.json`	50 states	SNAP coverage gaps
`regional_analysis.json`	--	Regional breakdowns
`national_summary.json`	--	National aggregate stats

Healthcare (`healthcare/`)

File	Records	Source
`healthcare_desert_merged.csv`	3,222 counties	Census ACS 2022 + HRSA HPSA
`cms_hospitals_2025.csv`	5,421 hospitals	CMS Hospital Compare

Housing (`housing/`)

File	Records	Source
`housing_crisis_merged.csv`	3,222 counties	Census ACS 2022 (rent burden, income, units)

Veterans (`veterans/`)

File	Records	Source
`military_firearm_merged_analysis.csv`	54 states/territories	Census ACS + CDC + VA
`military_firearm_veterans.csv`	--	Veteran population by state
`military_firearm_ptsd.csv`	--	PTSD and mental health indicators
`military_firearm_suicide.csv`	--	Veteran suicide rates
`military_firearm_va_healthcare.csv`	--	VA healthcare enrollment
`military_firearm_firearms.csv`	--	Firearm ownership rates
+ 4 more CSVs with metadata	--	Active duty, FFL, economic impact, spouse employment

Economic (`economic/`)

File	Records	Source
`gini_by_county.csv`	3,222 counties	Census ACS 2022 (B19083)
`unemployment_by_county.csv`	3,222 counties	Census ACS 2022 (B23025)
`poverty_depth_by_county.csv`	3,222 counties	Census ACS 2022 (C17002)

Education (`education/`)

File	Records	Source
`education_by_county.csv`	3,222 counties	Census ACS 2022 (B15003)

Disability (`disability/`)

File	Records	Source
`census_disability_by_county_2022.csv`	3,222 counties	Census ACS 2022 (S1810)

CMS Hospitals (`cms/`)

File	Records	Source
`cms_hospitals_20260121.csv`	5,421 hospitals	CMS Hospital Compare (Jan 2026 refresh)

Source Data (`source/`)

File	Size	Description
`food_access_atlas_2019.xlsx`	82 MB	Raw USDA Food Access Research Atlas (Git LFS)

FIPS Codes

Every county-level file uses 5-digit FIPS codes as the primary key:

State FIPS (2 digits) + County FIPS (3 digits)
Example: "01001" = Autauga County, Alabama

This means you can merge any combination of datasets:

import pandas as pd

food = pd.read_csv("food_deserts/food_desert_merged.csv", dtype={"fips": str})
health = pd.read_csv("healthcare/healthcare_desert_merged.csv", dtype={"fips": str})
housing = pd.read_csv("housing/housing_crisis_merged.csv", dtype={"fips": str})

merged = food.merge(health, on="fips", suffixes=("_food", "_health"))
merged = merged.merge(housing, on="fips")

Quick Start

Python

import pandas as pd

# Load any county-level dataset
df = pd.read_csv("food_deserts/food_desert_merged.csv", dtype={"fips": str})

# Worst counties for food access
worst = df.nlargest(20, "poverty_rate")
print(worst[["fips", "name", "poverty_rate", "no_vehicle_pct"]])

D3.js

const data = await d3.csv("healthcare/healthcare_desert_merged.csv");
// FIPS codes ready for choropleth mapping

Data Sources

Source	Agency	URL
American Community Survey	Census Bureau	data.census.gov
Food Access Research Atlas	USDA ERS	ers.usda.gov
Hospital Compare	CMS	data.cms.gov
Health Professional Shortage Areas	HRSA	data.hrsa.gov
Veteran Statistics	VA + CDC	Multiple sources

Data Trove -- full dataset catalog
lukesteuber.com -- portfolio
HuggingFace Dataset
Kaggle Dataset

Author

Luke Steuber

License

MIT. See LICENSE.

Source data is from US federal agencies (public domain).

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US Inequality Atlas

What's Inside

Food Deserts (food_deserts/)

Healthcare (healthcare/)

Housing (housing/)

Veterans (veterans/)

Economic (economic/)

Education (education/)

Disability (disability/)

CMS Hospitals (cms/)

Source Data (source/)