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economic poverty depth by county

saturn notebook · generated 2026-05-01 Report Notebook

Overview

Source: /home/coolhand/datasets/us-inequality-atlas/economic/poverty_depth_by_county.csv

Saturn profiled 3,222 rows across 7 columns. The stats below are deterministic and machine-readable; the prose is a language-model interpretation of those stats (opt-in, added after the fact, never sees raw rows).

[2]:
!pip install saturn-dissect
import subprocess
subprocess.run([
    "saturn", "analyze", "/home/coolhand/datasets/us-inequality-atlas/economic/poverty_depth_by_county.csv",
    "--findings", "economic-poverty_depth_by_county.json",
    "--llm", "anthropic:claude-opus-4-7",
])

Summary confidence: high

This dataset contains 3,222 rows of US county-level poverty statistics, with each row identified by a FIPS code, county name, and state abbreviation, plus three poverty rate measures and a population total. The poverty measures are all right-skewed: pct_poverty ranges from 1.6% to 66.32% with a median of 13.55%, while pct_deep_poverty has a median of 5.82% but reaches as high as 34.7%. The total population column is extremely skewed (skew of 13.4, kurtosis ~297) with a median of 25,174 but a max near 9.8 million, so any aggregate analysis should account for this. Texas (254 counties), Georgia (159), and Virginia (133) dominate the state distribution, which matters for any state-level rollups.

citing: row_count · column_count · columns · kinds

Out[4]:

saturn.schema() · 7 columns

column kind n null% unique alerts
fips numeric 3,222 0.0% 3,222
county_name text 3,222 0.0% 1,960 short_text duplicates
state categorical 3,222 0.0% 52
total numeric 3,222 0.0% 3,173 high_skew outliers
pct_deep_poverty numeric 3,222 0.0% 1,131 high_skew outliers
pct_poverty numeric 3,222 0.0% 1,719 high_skew
pct_near_poverty numeric 3,222 0.0% 1,237
Fig 1.
pct_poverty · Look at the long right tail — most counties cluster near 13-15% but some exceed 60%.
Show data table
bincount
1.6 – 3.2187
3.218 – 4.83634
4.836 – 6.454106
6.454 – 8.072246
8.072 – 9.69320
9.69 – 11.31354
11.31 – 12.93393
12.93 – 14.54364
14.54 – 16.16306
16.16 – 17.78262
17.78 – 19.4192
19.4 – 21.02149
21.02 – 22.63123
22.63 – 24.2591
24.25 – 25.8752
25.87 – 27.4944
27.49 – 29.1134
29.11 – 30.7223
30.72 – 32.3418
32.34 – 33.9614
33.96 – 35.586
35.58 – 37.28
37.2 – 38.813
38.81 – 40.438
40.43 – 42.055
42.05 – 43.679
43.67 – 45.294
45.29 – 46.911
46.9 – 48.527
48.52 – 50.148
50.14 – 51.762
51.76 – 53.386
53.38 – 54.995
54.99 – 56.615
56.61 – 58.231
58.23 – 59.850
59.85 – 61.470
61.47 – 63.080
63.08 – 64.71
64.7 – 66.321
Fig 2.
pct_deep_poverty · Check how deep-poverty rates concentrate below 8% with outlier counties stretching past 30%.
Show data table
bincount
0 – 0.867515
0.8675 – 1.73528
1.735 – 2.603128
2.603 – 3.47241
3.47 – 4.338429
4.338 – 5.205446
5.205 – 6.073436
6.073 – 6.94403
6.94 – 7.808261
7.808 – 8.675211
8.675 – 9.543157
9.543 – 10.41113
10.41 – 11.2857
11.28 – 12.1558
12.15 – 13.0150
13.01 – 13.8828
13.88 – 14.7518
14.75 – 15.6222
15.62 – 16.4818
16.48 – 17.358
17.35 – 18.2211
18.22 – 19.099
19.09 – 19.957
19.95 – 20.824
20.82 – 21.697
21.69 – 22.558
22.55 – 23.425
23.42 – 24.292
24.29 – 25.168
25.16 – 26.034
26.03 – 26.896
26.89 – 27.762
27.76 – 28.634
28.63 – 29.57
29.5 – 30.363
30.36 – 31.230
31.23 – 32.12
32.1 – 32.971
32.97 – 33.831
33.83 – 34.74
Fig 3.
total · Note the extreme skew: most counties are small but a few exceed millions, which will dominate any unweighted average.
Show data table
bincount
47 – 2.446e+052942
2.446e+05 – 4.892e+05137
4.892e+05 – 7.337e+0557
7.337e+05 – 9.783e+0539
9.783e+05 – 1.223e+0612
1.223e+06 – 1.467e+069
1.467e+06 – 1.712e+067
1.712e+06 – 1.957e+063
1.957e+06 – 2.201e+063
2.201e+06 – 2.446e+064
2.446e+06 – 2.69e+063
2.69e+06 – 2.935e+060
2.935e+06 – 3.179e+061
3.179e+06 – 3.424e+061
3.424e+06 – 3.669e+060
3.669e+06 – 3.913e+060
3.913e+06 – 4.158e+060
4.158e+06 – 4.402e+061
4.402e+06 – 4.647e+060
4.647e+06 – 4.891e+061
4.891e+06 – 5.136e+060
5.136e+06 – 5.38e+061
5.38e+06 – 5.625e+060
5.625e+06 – 5.87e+060
5.87e+06 – 6.114e+060
6.114e+06 – 6.359e+060
6.359e+06 – 6.603e+060
6.603e+06 – 6.848e+060
6.848e+06 – 7.092e+060
7.092e+06 – 7.337e+060
7.337e+06 – 7.582e+060
7.582e+06 – 7.826e+060
7.826e+06 – 8.071e+060
8.071e+06 – 8.315e+060
8.315e+06 – 8.56e+060
8.56e+06 – 8.804e+060
8.804e+06 – 9.049e+060
9.049e+06 – 9.293e+060
9.293e+06 – 9.538e+060
9.538e+06 – 9.783e+061
Fig 4.
state · See which states contribute the most counties — Texas, Georgia, and Virginia lead.
Show data table
valuecountshare
TX2547.9%
GA1594.9%
VA1334.1%
KY1203.7%
MO1153.6%
KS1053.3%
IL1023.2%
NC1003.1%
IA993.1%
TN952.9%
NE932.9%
IN922.9%
OH882.7%
MN872.7%
MI832.6%
MS822.5%
PR782.4%
OK772.4%
AR752.3%
WI722.2%
Fig 5.
pct_near_poverty · Compare the near-poverty distribution to pct_poverty to gauge how many households sit just above the poverty line.
Show data table
bincount
0.58 – 1.7947
1.794 – 3.00818
3.008 – 4.22282
4.222 – 5.436161
5.436 – 6.65302
6.65 – 7.864419
7.864 – 9.078480
9.078 – 10.29487
10.29 – 11.51392
11.51 – 12.72280
12.72 – 13.93210
13.93 – 15.15138
15.15 – 16.3687
16.36 – 17.5853
17.58 – 18.7937
18.79 – 2035
20 – 21.2215
21.22 – 22.437
22.43 – 23.652
23.65 – 24.865
24.86 – 26.071
26.07 – 27.292
27.29 – 28.50
28.5 – 29.720
29.72 – 30.930
30.93 – 32.140
32.14 – 33.360
33.36 – 34.570
34.57 – 35.790
35.79 – 371
37 – 38.210
38.21 – 39.430
39.43 – 40.640
40.64 – 41.860
41.86 – 43.070
43.07 – 44.280
44.28 – 45.50
45.5 – 46.710
46.71 – 47.930
47.93 – 49.141
Fig 6.
Per-column null rate across the corpus. Columns are ordered by input position.
Show data table
columnkindnull %
fipsnumeric0.0%
county_nametext0.0%
statecategorical0.0%
totalnumeric0.0%
pct_deep_povertynumeric0.0%
pct_povertynumeric0.0%
pct_near_povertynumeric0.0%
Fig 7.
Pearson correlation across numeric columns (sampled, bounded).
Show data table
fipstotalpct_deep_povertypct_povertypct_near_poverty
fips+1.00-0.07+0.18+0.16+0.11
total-0.07+1.00-0.07-0.11-0.16
pct_deep_poverty+0.18-0.07+1.00+0.92+0.49
pct_poverty+0.16-0.11+0.92+1.00+0.59
pct_near_poverty+0.11-0.16+0.49+0.59+1.00

fips numeric identifier

This column is the US county FIPS code: every one of the 3222 rows is unique, null-free, and the value range (1001 to 72153) matches the standard 5-digit state+county encoding. Treating it as numeric is misleading despite the clean distribution (skew 0.16, no outliers) — the digits are categorical identifiers, not measurements.

Treatment: Cast to zero-padded string and use as a join key to county-level data.

anthropic:claude-opus-4-7 · confidence high
Out[13]:

saturn.columns["fips"].stats

statvalue
n3,222
nulls0 (0.0%)
unique3,222
min 1,001
max 72,153
mean 3.138e+04
median 30,022
std 1.63e+04
q1 1.903e+04
q3 4.61e+04
iqr 27,075
skew 0.1574
kurtosis -0.6314
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 8.
Distribution of fips. Vertical dash marks the median.
Show data table
bincount
1001 – 278097
2780 – 455915
4559 – 6337133
6337 – 811659
8116 – 989514
9895 – 1.167e+044
1.167e+04 – 1.345e+04226
1.345e+04 – 1.523e+045
1.523e+04 – 1.701e+0449
1.701e+04 – 1.879e+04189
1.879e+04 – 2.057e+04204
2.057e+04 – 2.235e+04184
2.235e+04 – 2.413e+0439
2.413e+04 – 2.59e+0415
2.59e+04 – 2.768e+04170
2.768e+04 – 2.946e+04196
2.946e+04 – 3.124e+04150
3.124e+04 – 3.302e+0427
3.302e+04 – 3.48e+0421
3.48e+04 – 3.658e+0495
3.658e+04 – 3.836e+04153
3.836e+04 – 4.013e+04155
4.013e+04 – 4.191e+0446
4.191e+04 – 4.369e+0467
4.369e+04 – 4.547e+0451
4.547e+04 – 4.725e+04161
4.725e+04 – 4.903e+04268
4.903e+04 – 5.081e+0429
5.081e+04 – 5.259e+04133
5.259e+04 – 5.436e+0494
5.436e+04 – 5.614e+0495
5.614e+04 – 5.792e+040
5.792e+04 – 5.97e+040
5.97e+04 – 6.148e+040
6.148e+04 – 6.326e+040
6.326e+04 – 6.504e+040
6.504e+04 – 6.682e+040
6.682e+04 – 6.86e+040
6.86e+04 – 7.037e+040
7.037e+04 – 7.215e+0478

county_name text feature

This column holds US county-level place names — virtually every value ends in 'County' (2999 occurrences), with smaller groups of Louisiana 'parish' (64) and Puerto Rican 'municipio' (78) entries. Despite 3222 rows, only 1960 are unique and 39.2% are duplicates, because common names like Washington County (30), Jefferson County (25) and Franklin County (24) recur across states. Values are short and uniform (mean 14.2 chars, ~2 words), so the name alone does not uniquely identify a county.

Treatment: Pair with a state column to form a unique key before joining or grouping.

anthropic:claude-opus-4-7 · confidence high
Out[16]:

saturn.columns["county_name"].stats

statvalue
n3,222
nulls0 (0.0%)
unique1,960
len_min 10
len_max 46
len_mean 14.17
len_median 14
len_p95 18
word_mean 2.083
word_median 2
n_empty 0
n_duplicates 1,262
duplicate_rate 0.3917
vocab_size 1,963
readability_flesch_mean 33.36
emoji_rate 0
url_rate 0
one_word_rate 0
allcaps_rate 0
boilerplate_rate 0
alert: short_text95th-percentile length under 20 chars
alert: duplicates39.2% duplicate strings
Fig 9.
Character-length distribution for county_name.
Show data table
charscount
10 – 1129
11 – 12255
12 – 13465
13 – 14682
14 – 14588
14 – 15493
15 – 16291
16 – 17219
17 – 1867
18 – 190
19 – 2049
20 – 2123
21 – 2216
22 – 2314
23 – 248
24 – 244
24 – 255
25 – 262
26 – 271
27 – 280
28 – 291
29 – 300
30 – 310
31 – 322
32 – 321
32 – 331
33 – 341
34 – 351
35 – 360
36 – 370
37 – 380
38 – 390
39 – 400
40 – 412
41 – 421
42 – 420
42 – 430
43 – 440
44 – 450
45 – 461

state categorical feature

This is a US state code field with 52 distinct values, consistent with the 50 states plus DC and likely one territory. Distribution is broad and near-uniform (entropy ratio 0.93), with TX leading at just 7.88% (254 of 3222 rows), followed by GA, VA, KY, and MO. No nulls, and the row count suggests multiple records per state rather than one-per-state.

Treatment: One-hot or target-encode for modelling; usable as a join key to state-level reference data.

anthropic:claude-opus-4-7 · confidence high
Out[19]:

saturn.columns["state"].stats

statvalue
n3,222
nulls0 (0.0%)
unique52
top_value TX
top_rate 0.07883
cardinality 52
entropy 5.314
entropy_ratio 0.9322
Fig 10.
Top values for state.
Show data table
valuecountshare
TX2547.9%
GA1594.9%
VA1334.1%
KY1203.7%
MO1153.6%
KS1053.3%
IL1023.2%
NC1003.1%
IA993.1%
TN952.9%
NE932.9%
IN922.9%
OH882.7%
MN872.7%
MI832.6%
MS822.5%
PR782.4%
OK772.4%
AR752.3%
WI722.2%

total numeric feature

A heavily right-skewed numeric measure (skew 13.36, kurtosis 297.59) ranging from 47 to 9,782,602 with a median of 25,174 but a mean of 101,340 — the upper tail dwarfs the center. Roughly 13.9% of rows (449) flag as outliers, and the standard deviation (324,628) is over three times the mean, signalling a few very large values dominate. With 3,173 unique values across 3,222 rows and no nulls or zeros, this looks like a per-record aggregate total rather than a category or flag.

Treatment: log-transform before modelling and consider winsorising the extreme tail.

anthropic:claude-opus-4-7 · confidence high
Out[22]:

saturn.columns["total"].stats

statvalue
n3,222
nulls0 (0.0%)
unique3,173
min 47
max 9.783e+06
mean 1.013e+05
median 25,174
std 3.246e+05
q1 1.059e+04
q3 6.501e+04
iqr 5.442e+04
skew 13.36
kurtosis 297.6
n_outliers 449
outlier_rate 0.1394
zero_rate 0
alert: high_skewskew=+13.36
alert: outliers13.9% rows beyond 1.5 IQR
Fig 11.
Distribution of total. Vertical dash marks the median.
Show data table
bincount
47 – 2.446e+052942
2.446e+05 – 4.892e+05137
4.892e+05 – 7.337e+0557
7.337e+05 – 9.783e+0539
9.783e+05 – 1.223e+0612
1.223e+06 – 1.467e+069
1.467e+06 – 1.712e+067
1.712e+06 – 1.957e+063
1.957e+06 – 2.201e+063
2.201e+06 – 2.446e+064
2.446e+06 – 2.69e+063
2.69e+06 – 2.935e+060
2.935e+06 – 3.179e+061
3.179e+06 – 3.424e+061
3.424e+06 – 3.669e+060
3.669e+06 – 3.913e+060
3.913e+06 – 4.158e+060
4.158e+06 – 4.402e+061
4.402e+06 – 4.647e+060
4.647e+06 – 4.891e+061
4.891e+06 – 5.136e+060
5.136e+06 – 5.38e+061
5.38e+06 – 5.625e+060
5.625e+06 – 5.87e+060
5.87e+06 – 6.114e+060
6.114e+06 – 6.359e+060
6.359e+06 – 6.603e+060
6.603e+06 – 6.848e+060
6.848e+06 – 7.092e+060
7.092e+06 – 7.337e+060
7.337e+06 – 7.582e+060
7.582e+06 – 7.826e+060
7.826e+06 – 8.071e+060
8.071e+06 – 8.315e+060
8.315e+06 – 8.56e+060
8.56e+06 – 8.804e+060
8.804e+06 – 9.049e+060
9.049e+06 – 9.293e+060
9.293e+06 – 9.538e+060
9.538e+06 – 9.783e+061

pct_deep_poverty numeric feature

This is a numeric feature representing the percent of population in deep poverty, likely at a county or similar geographic unit (n=3222 with 1131 unique values). The distribution is right-skewed (skew 2.67, kurtosis 10.4) with a median of 5.82 but a max of 34.7, and 176 outliers (5.46%) sit in the upper tail. Min is 0.0 but the zero rate is just 0.09%, so the floor is rarely hit.

Treatment: Apply a log1p or similar transform before regression to tame the right skew.

anthropic:claude-opus-4-7 · confidence high
Out[25]:

saturn.columns["pct_deep_poverty"].stats

statvalue
n3,222
nulls0 (0.0%)
unique1,131
min 0
max 34.7
mean 6.743
median 5.82
std 4.154
q1 4.27
q3 7.918
iqr 3.648
skew 2.665
kurtosis 10.4
n_outliers 176
outlier_rate 0.05462
zero_rate 0.0009311
alert: high_skewskew=+2.67
alert: outliers5.5% rows beyond 1.5 IQR
Fig 12.
Distribution of pct_deep_poverty. Vertical dash marks the median.
Show data table
bincount
0 – 0.867515
0.8675 – 1.73528
1.735 – 2.603128
2.603 – 3.47241
3.47 – 4.338429
4.338 – 5.205446
5.205 – 6.073436
6.073 – 6.94403
6.94 – 7.808261
7.808 – 8.675211
8.675 – 9.543157
9.543 – 10.41113
10.41 – 11.2857
11.28 – 12.1558
12.15 – 13.0150
13.01 – 13.8828
13.88 – 14.7518
14.75 – 15.6222
15.62 – 16.4818
16.48 – 17.358
17.35 – 18.2211
18.22 – 19.099
19.09 – 19.957
19.95 – 20.824
20.82 – 21.697
21.69 – 22.558
22.55 – 23.425
23.42 – 24.292
24.29 – 25.168
25.16 – 26.034
26.03 – 26.896
26.89 – 27.762
27.76 – 28.634
28.63 – 29.57
29.5 – 30.363
30.36 – 31.230
31.23 – 32.12
32.1 – 32.971
32.97 – 33.831
33.83 – 34.74

pct_poverty numeric feature

Likely a county- or tract-level poverty rate expressed as a percentage, ranging from 1.6 to 66.32 with a median of 13.55. The distribution is heavily right-skewed (skew 2.10, kurtosis 6.89) with 137 high-end outliers (~4.3%) pulling the mean (15.10) above the median. No nulls or zeros across 3,222 rows.

Treatment: Consider a log or Yeo-Johnson transform before linear modelling to tame the right tail.

anthropic:claude-opus-4-7 · confidence high
Out[28]:

saturn.columns["pct_poverty"].stats

statvalue
n3,222
nulls0 (0.0%)
unique1,719
min 1.6
max 66.32
mean 15.1
median 13.55
std 7.706
q1 10.16
q3 17.91
iqr 7.75
skew 2.096
kurtosis 6.891
n_outliers 137
outlier_rate 0.04252
zero_rate 0
alert: high_skewskew=+2.10
Fig 13.
Distribution of pct_poverty. Vertical dash marks the median.
Show data table
bincount
1.6 – 3.2187
3.218 – 4.83634
4.836 – 6.454106
6.454 – 8.072246
8.072 – 9.69320
9.69 – 11.31354
11.31 – 12.93393
12.93 – 14.54364
14.54 – 16.16306
16.16 – 17.78262
17.78 – 19.4192
19.4 – 21.02149
21.02 – 22.63123
22.63 – 24.2591
24.25 – 25.8752
25.87 – 27.4944
27.49 – 29.1134
29.11 – 30.7223
30.72 – 32.3418
32.34 – 33.9614
33.96 – 35.586
35.58 – 37.28
37.2 – 38.813
38.81 – 40.438
40.43 – 42.055
42.05 – 43.679
43.67 – 45.294
45.29 – 46.911
46.9 – 48.527
48.52 – 50.148
50.14 – 51.762
51.76 – 53.386
53.38 – 54.995
54.99 – 56.615
56.61 – 58.231
58.23 – 59.850
59.85 – 61.470
61.47 – 63.080
63.08 – 64.71
64.7 – 66.321

pct_near_poverty numeric feature

This column reports a percentage of population near the poverty line, ranging from 0.58 to 49.14 with a mean of 9.81 and median of 9.38. The distribution is right-skewed (skew 1.19, kurtosis 5.73) with 82 outliers (2.55%) on the high tail, but no nulls or zeros. The IQR is tight at 4.43, so most observations cluster between 7.33 and 11.76 with a long upper tail.

Treatment: Consider a log or winsorization before regression to dampen the right tail.

anthropic:claude-opus-4-7 · confidence high
Out[31]:

saturn.columns["pct_near_poverty"].stats

statvalue
n3,222
nulls0 (0.0%)
unique1,237
min 0.58
max 49.14
mean 9.813
median 9.38
std 3.644
q1 7.33
q3 11.76
iqr 4.43
skew 1.19
kurtosis 5.729
n_outliers 82
outlier_rate 0.02545
zero_rate 0
Fig 14.
Distribution of pct_near_poverty. Vertical dash marks the median.
Show data table
bincount
0.58 – 1.7947
1.794 – 3.00818
3.008 – 4.22282
4.222 – 5.436161
5.436 – 6.65302
6.65 – 7.864419
7.864 – 9.078480
9.078 – 10.29487
10.29 – 11.51392
11.51 – 12.72280
12.72 – 13.93210
13.93 – 15.15138
15.15 – 16.3687
16.36 – 17.5853
17.58 – 18.7937
18.79 – 2035
20 – 21.2215
21.22 – 22.437
22.43 – 23.652
23.65 – 24.865
24.86 – 26.071
26.07 – 27.292
27.29 – 28.50
28.5 – 29.720
29.72 – 30.930
30.93 – 32.140
32.14 – 33.360
33.36 – 34.570
34.57 – 35.790
35.79 – 371
37 – 38.210
38.21 – 39.430
39.43 – 40.640
40.64 – 41.860
41.86 – 43.070
43.07 – 44.280
44.28 – 45.50
45.5 – 46.710
46.71 – 47.930
47.93 – 49.141

How to cite

click to copy

BibTeX
@misc{saturn-economic-poverty-depth-by-county-2026,
  author       = {Steuber, Luke},
  title        = {Saturn reading: economic poverty depth by county},
  year         ={2026},
  howpublished = {\url{https://dr.eamer.dev/saturn/view/economic-poverty_depth_by_county}},
  note         = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:claude-opus-4-7},
}
APA
Steuber, L. (2026). Saturn reading: economic poverty depth by county. Source: /home/coolhand/datasets/us-inequality-atlas/economic/poverty_depth_by_county.csv. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:claude-opus-4-7). Retrieved from https://dr.eamer.dev/saturn/view/economic-poverty_depth_by_county