economic poverty depth by county

source /home/coolhand/datasets/us-inequality-atlas/economic/poverty_depth_by_county.csv 3,222 rows 7 columns profiled 2026-05-01 raw JSON static .html .ipynb Report Notebook

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dataset summary · high confidence anthropic:claude-opus-4-7

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

Charts the summary said to look at first

pct_poverty · Look at the long right tail — most counties cluster near 13-15% but some exceed 60%.

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Histogram bins for pct_poverty (median: 13.55).
bin	count
1.6 – 3.218	7
3.218 – 4.836	34
4.836 – 6.454	106
6.454 – 8.072	246
8.072 – 9.69	320
9.69 – 11.31	354
11.31 – 12.93	393
12.93 – 14.54	364
14.54 – 16.16	306
16.16 – 17.78	262
17.78 – 19.4	192
19.4 – 21.02	149
21.02 – 22.63	123
22.63 – 24.25	91
24.25 – 25.87	52
25.87 – 27.49	44
27.49 – 29.11	34
29.11 – 30.72	23
30.72 – 32.34	18
32.34 – 33.96	14
33.96 – 35.58	6
35.58 – 37.2	8
37.2 – 38.81	3
38.81 – 40.43	8
40.43 – 42.05	5
42.05 – 43.67	9
43.67 – 45.29	4
45.29 – 46.9	11
46.9 – 48.52	7
48.52 – 50.14	8
50.14 – 51.76	2
51.76 – 53.38	6
53.38 – 54.99	5
54.99 – 56.61	5
56.61 – 58.23	1
58.23 – 59.85	0
59.85 – 61.47	0
61.47 – 63.08	0
63.08 – 64.7	1
64.7 – 66.32	1

pct_deep_poverty · Check how deep-poverty rates concentrate below 8% with outlier counties stretching past 30%.

Show data table

Histogram bins for pct_deep_poverty (median: 5.82).
bin	count
0 – 0.8675	15
0.8675 – 1.735	28
1.735 – 2.603	128
2.603 – 3.47	241
3.47 – 4.338	429
4.338 – 5.205	446
5.205 – 6.073	436
6.073 – 6.94	403
6.94 – 7.808	261
7.808 – 8.675	211
8.675 – 9.543	157
9.543 – 10.41	113
10.41 – 11.28	57
11.28 – 12.15	58
12.15 – 13.01	50
13.01 – 13.88	28
13.88 – 14.75	18
14.75 – 15.62	22
15.62 – 16.48	18
16.48 – 17.35	8
17.35 – 18.22	11
18.22 – 19.09	9
19.09 – 19.95	7
19.95 – 20.82	4
20.82 – 21.69	7
21.69 – 22.55	8
22.55 – 23.42	5
23.42 – 24.29	2
24.29 – 25.16	8
25.16 – 26.03	4
26.03 – 26.89	6
26.89 – 27.76	2
27.76 – 28.63	4
28.63 – 29.5	7
29.5 – 30.36	3
30.36 – 31.23	0
31.23 – 32.1	2
32.1 – 32.97	1
32.97 – 33.83	1
33.83 – 34.7	4

total · Note the extreme skew: most counties are small but a few exceed millions, which will dominate any unweighted average.

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Histogram bins for total (median: 25174.0).
bin	count
47 – 2.446e+05	2942
2.446e+05 – 4.892e+05	137
4.892e+05 – 7.337e+05	57
7.337e+05 – 9.783e+05	39
9.783e+05 – 1.223e+06	12
1.223e+06 – 1.467e+06	9
1.467e+06 – 1.712e+06	7
1.712e+06 – 1.957e+06	3
1.957e+06 – 2.201e+06	3
2.201e+06 – 2.446e+06	4
2.446e+06 – 2.69e+06	3
2.69e+06 – 2.935e+06	0
2.935e+06 – 3.179e+06	1
3.179e+06 – 3.424e+06	1
3.424e+06 – 3.669e+06	0
3.669e+06 – 3.913e+06	0
3.913e+06 – 4.158e+06	0
4.158e+06 – 4.402e+06	1
4.402e+06 – 4.647e+06	0
4.647e+06 – 4.891e+06	1
4.891e+06 – 5.136e+06	0
5.136e+06 – 5.38e+06	1
5.38e+06 – 5.625e+06	0
5.625e+06 – 5.87e+06	0
5.87e+06 – 6.114e+06	0
6.114e+06 – 6.359e+06	0
6.359e+06 – 6.603e+06	0
6.603e+06 – 6.848e+06	0
6.848e+06 – 7.092e+06	0
7.092e+06 – 7.337e+06	0
7.337e+06 – 7.582e+06	0
7.582e+06 – 7.826e+06	0
7.826e+06 – 8.071e+06	0
8.071e+06 – 8.315e+06	0
8.315e+06 – 8.56e+06	0
8.56e+06 – 8.804e+06	0
8.804e+06 – 9.049e+06	0
9.049e+06 – 9.293e+06	0
9.293e+06 – 9.538e+06	0
9.538e+06 – 9.783e+06	1

state · See which states contribute the most counties — Texas, Georgia, and Virginia lead.

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Top values for state (20 unique shown, of 52 total).
value	count	share
TX	254	7.9%
GA	159	4.9%
VA	133	4.1%
KY	120	3.7%
MO	115	3.6%
KS	105	3.3%
IL	102	3.2%
NC	100	3.1%
IA	99	3.1%
TN	95	2.9%
NE	93	2.9%
IN	92	2.9%
OH	88	2.7%
MN	87	2.7%
MI	83	2.6%
MS	82	2.5%
PR	78	2.4%
OK	77	2.4%
AR	75	2.3%
WI	72	2.2%

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

Histogram bins for pct_near_poverty (median: 9.38).
bin	count
0.58 – 1.794	7
1.794 – 3.008	18
3.008 – 4.222	82
4.222 – 5.436	161
5.436 – 6.65	302
6.65 – 7.864	419
7.864 – 9.078	480
9.078 – 10.29	487
10.29 – 11.51	392
11.51 – 12.72	280
12.72 – 13.93	210
13.93 – 15.15	138
15.15 – 16.36	87
16.36 – 17.58	53
17.58 – 18.79	37
18.79 – 20	35
20 – 21.22	15
21.22 – 22.43	7
22.43 – 23.65	2
23.65 – 24.86	5
24.86 – 26.07	1
26.07 – 27.29	2
27.29 – 28.5	0
28.5 – 29.72	0
29.72 – 30.93	0
30.93 – 32.14	0
32.14 – 33.36	0
33.36 – 34.57	0
34.57 – 35.79	0
35.79 – 37	1
37 – 38.21	0
38.21 – 39.43	0
39.43 – 40.64	0
40.64 – 41.86	0
41.86 – 43.07	0
43.07 – 44.28	0
44.28 – 45.5	0
45.5 – 46.71	0
46.71 – 47.93	0
47.93 – 49.14	1

Schema

7 columns

Per-column summary. Click column name to jump to its detail.
				Alerts
fips	numeric	0.0%	3,222
county_name	text	0.0%	1,960	short_text duplicates
state	categorical	0.0%	52
total	numeric	0.0%	3,173	high_skew outliers
pct_deep_poverty	numeric	0.0%	1,131	high_skew outliers
pct_poverty	numeric	0.0%	1,719	high_skew
pct_near_poverty	numeric	0.0%	1,237

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. high · anthropic:claude-opus-4-7

n: 3,222
nulls: 0 (0.0%)
unique: 3,222
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

county_name

text feature short_text duplicates

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. high · anthropic:claude-opus-4-7

n: 3,222
nulls: 0 (0.0%)
unique: 1,960
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

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. high · anthropic:claude-opus-4-7

n: 3,222
nulls: 0 (0.0%)
unique: 52
top_value: TX
top_rate: 0.07883
cardinality: 52
entropy: 5.314
entropy_ratio: 0.9322

total

numeric feature high_skew outliers

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. high · anthropic:claude-opus-4-7

n: 3,222
nulls: 0 (0.0%)
unique: 3,173
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

pct_deep_poverty

numeric feature high_skew outliers

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. high · anthropic:claude-opus-4-7

n: 3,222
nulls: 0 (0.0%)
unique: 1,131
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

pct_poverty

numeric feature high_skew

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. high · anthropic:claude-opus-4-7

n: 3,222
nulls: 0 (0.0%)
unique: 1,719
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

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. high · anthropic:claude-opus-4-7

n: 3,222
nulls: 0 (0.0%)
unique: 1,237
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