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food deserts food desert merged

saturn notebook · generated 2026-05-01 Report Notebook

Overview

Source: /home/coolhand/datasets/us-inequality-atlas/food_deserts/food_desert_merged.csv

Saturn profiled 3,222 rows across 11 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/food_deserts/food_desert_merged.csv",
    "--findings", "food_deserts-food_desert_merged.json",
    "--llm", "anthropic:claude-opus-4-7",
])

Summary confidence: high

This dataset contains 3,222 rows and 11 columns of US county-level indicators on poverty, SNAP eligibility and participation, vehicle access, and total population, keyed by FIPS and county/state codes. The population and program-count columns (total_pop, poverty_pop, snap_eligible_est, snap_participants_est, no_vehicle_total) are extremely right-skewed, with skew values from 13 to 20 and around 11-14% of rows flagged as outliers — a handful of very large counties dominate the raw totals. Note that snap_eligible_est and poverty_pop have identical statistics, suggesting one is a direct copy of the other and worth verifying before analysis. The rate-based columns are more tractable: poverty_rate has a moderate skew of 2.1 with a median of 13.55%, and no_vehicle_pct has a median of 5.41% but a long tail reaching 85.94%. Start with the rate columns for cross-county comparison and reserve the totals for absolute-magnitude questions.

citing: row_count · column_count · columns.total_pop.stats · columns.poverty_pop.stats · columns.snap_eligible_est.stats · columns.snap_participants_est.stats · columns.no_vehicle_total.stats · columns.poverty_rate.stats · columns.no_vehicle_pct.stats · columns.state.n_unique

Out[4]:

saturn.schema() · 11 columns

column kind n null% unique alerts
name text 3,222 0.0% 3,222 near_unique
total_pop numeric 3,222 0.0% 3,173 high_skew outliers
poverty_pop numeric 3,222 0.0% 2,839 high_skew outliers
state numeric 3,222 0.0% 52
county numeric 3,222 0.0% 330 high_skew outliers
fips numeric 3,222 0.0% 3,222
poverty_rate numeric 3,222 0.0% 1,719 high_skew
snap_eligible_est numeric 3,222 0.0% 2,839 high_skew outliers
snap_participants_est numeric 3,222 0.0% 2,636 high_skew outliers
no_vehicle_total numeric 3,222 0.0% 1,823 high_skew outliers
no_vehicle_pct numeric 3,222 0.0% 1,065 high_skew
Fig 1.
poverty_rate · Distribution of county poverty rates; median around 13.55% with a moderate right tail up to 66%.
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.
no_vehicle_pct · Share of households without a vehicle per county — most cluster below 8% but a long tail reaches near 86%.
Show data table
bincount
0 – 2.148161
2.148 – 4.297823
4.297 – 6.4451091
6.445 – 8.594630
8.594 – 10.74283
10.74 – 12.89111
12.89 – 15.0461
15.04 – 17.1923
17.19 – 19.348
19.34 – 21.483
21.48 – 23.634
23.63 – 25.782
25.78 – 27.933
27.93 – 30.082
30.08 – 32.232
32.23 – 34.382
34.38 – 36.522
36.52 – 38.672
38.67 – 40.820
40.82 – 42.971
42.97 – 45.120
45.12 – 47.271
47.27 – 49.420
49.42 – 51.560
51.56 – 53.710
53.71 – 55.861
55.86 – 58.011
58.01 – 60.160
60.16 – 62.312
62.31 – 64.450
64.45 – 66.61
66.6 – 68.750
68.75 – 70.90
70.9 – 73.050
73.05 – 75.20
75.2 – 77.350
77.35 – 79.491
79.49 – 81.640
81.64 – 83.790
83.79 – 85.941
Fig 3.
total_pop · County population is heavily right-skewed; expect to log-transform before modeling.
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.
snap_participants_est · SNAP participant counts span from 2 to 900k, with ~11% of counties flagged as high-end outliers.
Show data table
bincount
2 – 2.251e+042963
2.251e+04 – 4.503e+04152
4.503e+04 – 6.754e+0449
6.754e+04 – 9.005e+0419
9.005e+04 – 1.126e+0510
1.126e+05 – 1.351e+056
1.351e+05 – 1.576e+053
1.576e+05 – 1.801e+053
1.801e+05 – 2.026e+055
2.026e+05 – 2.251e+051
2.251e+05 – 2.476e+054
2.476e+05 – 2.701e+051
2.701e+05 – 2.927e+051
2.927e+05 – 3.152e+050
3.152e+05 – 3.377e+052
3.377e+05 – 3.602e+050
3.602e+05 – 3.827e+050
3.827e+05 – 4.052e+050
4.052e+05 – 4.277e+050
4.277e+05 – 4.502e+050
4.502e+05 – 4.727e+051
4.727e+05 – 4.953e+051
4.953e+05 – 5.178e+050
5.178e+05 – 5.403e+050
5.403e+05 – 5.628e+050
5.628e+05 – 5.853e+050
5.853e+05 – 6.078e+050
6.078e+05 – 6.303e+050
6.303e+05 – 6.528e+050
6.528e+05 – 6.753e+050
6.753e+05 – 6.979e+050
6.979e+05 – 7.204e+050
7.204e+05 – 7.429e+050
7.429e+05 – 7.654e+050
7.654e+05 – 7.879e+050
7.879e+05 – 8.104e+050
8.104e+05 – 8.329e+050
8.329e+05 – 8.554e+050
8.554e+05 – 8.78e+050
8.78e+05 – 9.005e+051
Fig 5.
state · Counts of counties per state code (52 unique values) to confirm geographic coverage.
Show data table
bincount
1 – 2.77597
2.775 – 4.5515
4.55 – 6.325133
6.325 – 8.164
8.1 – 9.8759
9.875 – 11.654
11.65 – 13.42226
13.42 – 15.25
15.2 – 16.9844
16.98 – 18.75194
18.75 – 20.52204
20.52 – 22.3184
22.3 – 24.0740
24.07 – 25.8514
25.85 – 27.62170
27.62 – 29.4197
29.4 – 31.17149
31.17 – 32.9517
32.95 – 34.7331
34.73 – 36.595
36.5 – 38.27153
38.27 – 40.05165
40.05 – 41.8236
41.82 – 43.667
43.6 – 45.3851
45.38 – 47.15161
47.15 – 48.92254
48.92 – 50.743
50.7 – 52.47133
52.47 – 54.2594
54.25 – 56.0295
56.02 – 57.80
57.8 – 59.570
59.57 – 61.350
61.35 – 63.120
63.12 – 64.90
64.9 – 66.670
66.67 – 68.450
68.45 – 70.220
70.22 – 7278
Fig 6.
Per-column null rate across the corpus. Columns are ordered by input position.
Show data table
columnkindnull %
nametext0.0%
total_popnumeric0.0%
poverty_popnumeric0.0%
statenumeric0.0%
countynumeric0.0%
fipsnumeric0.0%
poverty_ratenumeric0.0%
snap_eligible_estnumeric0.0%
snap_participants_estnumeric0.0%
no_vehicle_totalnumeric0.0%
no_vehicle_pctnumeric0.0%
Fig 7.
Pearson correlation across numeric columns (sampled, bounded).
Show data table
total_poppoverty_popstatecountyfipspoverty_ratesnap_eligible_estsnap_participants_estno_vehicle_totalno_vehicle_pct
total_pop+1.00+0.96-0.07-0.02-0.07-0.11+0.96+0.96+0.90+0.09
poverty_pop+0.96+1.00-0.07-0.01-0.07-0.03+1.00+1.00+0.92+0.14
state-0.07-0.07+1.00+0.14+1.00+0.16-0.07-0.07-0.05+0.04
county-0.02-0.01+0.14+1.00+0.15+0.05-0.01-0.01-0.02+0.11
fips-0.07-0.07+1.00+0.15+1.00+0.16-0.07-0.07-0.05+0.04
poverty_rate-0.11-0.03+0.16+0.05+0.16+1.00-0.03-0.03-0.04+0.45
snap_eligible_est+0.96+1.00-0.07-0.01-0.07-0.03+1.00+1.00+0.92+0.14
snap_participants_est+0.96+1.00-0.07-0.01-0.07-0.03+1.00+1.00+0.92+0.14
no_vehicle_total+0.90+0.92-0.05-0.02-0.05-0.04+0.92+0.92+1.00+0.15
no_vehicle_pct+0.09+0.14+0.04+0.11+0.04+0.45+0.14+0.14+0.15+1.00

name text identifier

This column holds full county names paired with state (e.g., "... County, Texas"), as evidenced by "county," appearing 2999 times out of 3222 rows alongside top state tokens like Texas (256), Virginia (189), and Georgia (159). Every value is unique (n_unique=3222, null_rate=0) and lengths are tightly clustered (mean 24.3, min 16, max 59, ~3 words), consistent with a canonical place-name label. The near_unique alert confirms it functions as a row identifier rather than a categorical feature.

Treatment: Use as a join key on county-state; do not feed into models as a categorical feature.

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

saturn.columns["name"].stats

statvalue
n3,222
nulls0 (0.0%)
unique3,222
len_min 16
len_max 59
len_mean 24.32
len_median 24
len_p95 31
word_mean 3.248
word_median 3
n_empty 0
n_duplicates 0
duplicate_rate 0
vocab_size 1,990
readability_flesch_mean 10.28
emoji_rate 0
url_rate 0
one_word_rate 0
allcaps_rate 0
boilerplate_rate 0
alert: near_unique100.0% of rows are unique strings
Fig 8.
Character-length distribution for name.
Show data table
charscount
16 – 1726
17 – 1872
18 – 19121
19 – 20190
20 – 21264
21 – 22407
22 – 24420
24 – 25363
25 – 26320
26 – 27240
27 – 28231
28 – 29152
29 – 30139
30 – 31165
31 – 3241
32 – 3328
33 – 3416
34 – 3510
35 – 365
36 – 380
38 – 391
39 – 401
40 – 410
41 – 421
42 – 431
43 – 440
44 – 452
45 – 460
46 – 471
47 – 481
48 – 490
49 – 500
50 – 510
51 – 530
53 – 542
54 – 551
55 – 560
56 – 570
57 – 580
58 – 591

total_pop numeric feature

Population counts per record, ranging from 47 to 9,782,602 with a median of 25,174 — consistent with US county-level totals. The distribution is extremely right-skewed (skew 13.36, kurtosis 297.59) and 13.9% of rows (449) flag as outliers, driven by a handful of mega-population entities pulling the mean (101,340) far above the median.

Treatment: log-transform before regression or distance-based modelling.

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

saturn.columns["total_pop"].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 9.
Distribution of total_pop. 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

poverty_pop numeric feature

This is a count of population in poverty per record (likely a county or similar geographic unit), ranging from 3 to 1,343,978 with a median of 3,799.5. The distribution is extremely right-skewed (skew 14.73, kurtosis 342.21) and 362 values (11.2%) are flagged as outliers, consistent with a few very large jurisdictions dwarfing the rest. No nulls or zeros, and 2,839 of 3,222 values are unique.

Treatment: log-transform before regression to tame the heavy right tail.

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

saturn.columns["poverty_pop"].stats

statvalue
n3,222
nulls0 (0.0%)
unique2,839
min 3
max 1.344e+06
mean 1.3e+04
median 3800
std 4.326e+04
q1 1526
q3 9768
iqr 8242
skew 14.73
kurtosis 342.2
n_outliers 362
outlier_rate 0.1124
zero_rate 0
alert: high_skewskew=+14.73
alert: outliers11.2% rows beyond 1.5 IQR
Fig 10.
Distribution of poverty_pop. Vertical dash marks the median.
Show data table
bincount
3 – 3.36e+042963
3.36e+04 – 6.72e+04152
6.72e+04 – 1.008e+0549
1.008e+05 – 1.344e+0519
1.344e+05 – 1.68e+0510
1.68e+05 – 2.016e+056
2.016e+05 – 2.352e+053
2.352e+05 – 2.688e+053
2.688e+05 – 3.024e+055
3.024e+05 – 3.36e+051
3.36e+05 – 3.696e+054
3.696e+05 – 4.032e+051
4.032e+05 – 4.368e+051
4.368e+05 – 4.704e+050
4.704e+05 – 5.04e+052
5.04e+05 – 5.376e+050
5.376e+05 – 5.712e+050
5.712e+05 – 6.048e+050
6.048e+05 – 6.384e+050
6.384e+05 – 6.72e+050
6.72e+05 – 7.056e+051
7.056e+05 – 7.392e+051
7.392e+05 – 7.728e+050
7.728e+05 – 8.064e+050
8.064e+05 – 8.4e+050
8.4e+05 – 8.736e+050
8.736e+05 – 9.072e+050
9.072e+05 – 9.408e+050
9.408e+05 – 9.744e+050
9.744e+05 – 1.008e+060
1.008e+06 – 1.042e+060
1.042e+06 – 1.075e+060
1.075e+06 – 1.109e+060
1.109e+06 – 1.142e+060
1.142e+06 – 1.176e+060
1.176e+06 – 1.21e+060
1.21e+06 – 1.243e+060
1.243e+06 – 1.277e+060
1.277e+06 – 1.31e+060
1.31e+06 – 1.344e+061

state numeric identifier

Numeric codes ranging from 1 to 72 with 52 unique values across 3222 rows and no nulls strongly suggest US state/territory FIPS codes rather than a true measurement. The near-uniform spread (mean 31.27, median 30, std 16.29, skew 0.16) and absence of outliers are consistent with a categorical identifier encoded as integers. Treating these as a continuous feature would be misleading.

Treatment: Cast to categorical and map FIPS codes to state names before modelling.

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

saturn.columns["state"].stats

statvalue
n3,222
nulls0 (0.0%)
unique52
min 1
max 72
mean 31.27
median 30
std 16.29
q1 19
q3 46
iqr 27
skew 0.1574
kurtosis -0.6267
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 11.
Distribution of state. Vertical dash marks the median.
Show data table
bincount
1 – 2.77597
2.775 – 4.5515
4.55 – 6.325133
6.325 – 8.164
8.1 – 9.8759
9.875 – 11.654
11.65 – 13.42226
13.42 – 15.25
15.2 – 16.9844
16.98 – 18.75194
18.75 – 20.52204
20.52 – 22.3184
22.3 – 24.0740
24.07 – 25.8514
25.85 – 27.62170
27.62 – 29.4197
29.4 – 31.17149
31.17 – 32.9517
32.95 – 34.7331
34.73 – 36.595
36.5 – 38.27153
38.27 – 40.05165
40.05 – 41.8236
41.82 – 43.667
43.6 – 45.3851
45.38 – 47.15161
47.15 – 48.92254
48.92 – 50.743
50.7 – 52.47133
52.47 – 54.2594
54.25 – 56.0295
56.02 – 57.80
57.8 – 59.570
59.57 – 61.350
61.35 – 63.120
63.12 – 64.90
64.9 – 66.670
66.67 – 68.450
68.45 – 70.220
70.22 – 7278

county numeric foreign_key

Despite the name 'county', this column is stored as numeric with 330 unique integer values from 1 to 840 across 3,222 rows — consistent with a county FIPS or lookup code rather than a measured quantity. The distribution is heavily right-skewed (skew 2.87, kurtosis 11.6) with 178 outliers (5.5%), which is expected behavior for an ID-like code, not a meaningful statistical signal. No nulls or zeros are present.

Treatment: Cast to categorical/string and treat as a county code; do not use as a continuous numeric feature.

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

saturn.columns["county"].stats

statvalue
n3,222
nulls0 (0.0%)
unique330
min 1
max 840
mean 103.2
median 79
std 106.6
q1 35
q3 133
iqr 98
skew 2.866
kurtosis 11.64
n_outliers 178
outlier_rate 0.05525
zero_rate 0
alert: high_skewskew=+2.87
alert: outliers5.5% rows beyond 1.5 IQR
Fig 12.
Distribution of county. Vertical dash marks the median.
Show data table
bincount
1 – 21.98523
21.98 – 42.95418
42.95 – 63.93411
63.93 – 84.9345
84.9 – 105.9352
105.9 – 126.9281
126.9 – 147.8236
147.8 – 168.8168
168.8 – 189.8140
189.8 – 210.871
210.8 – 231.745
231.7 – 252.725
252.7 – 273.722
273.7 – 294.723
294.7 – 315.622
315.6 – 336.613
336.6 – 357.611
357.6 – 378.610
378.6 – 399.511
399.5 – 420.510
420.5 – 441.511
441.5 – 462.510
462.5 – 483.411
483.4 – 504.410
504.4 – 525.47
525.4 – 546.42
546.4 – 567.31
567.3 – 588.32
588.3 – 609.33
609.3 – 630.23
630.2 – 651.22
651.2 – 672.22
672.2 – 693.25
693.2 – 714.22
714.2 – 735.13
735.1 – 756.12
756.1 – 777.13
777.1 – 798.11
798.1 – 8192
819 – 8403

fips numeric identifier

This is the U.S. county FIPS code: every one of the 3222 rows is unique, with values spanning 1001 to 72153, consistent with state-prefixed county identifiers. The distribution is near-symmetric (skew 0.16, kurtosis -0.63) and has no outliers or nulls, as expected for a structured code rather than a measurement. Despite being numeric, the values are categorical labels and arithmetic on them is meaningless.

Treatment: treat as a categorical key and left-join county-level attributes on it rather than using it as a numeric feature.

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

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 13.
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

poverty_rate numeric feature

This column appears to be a county- or area-level poverty rate expressed as a percentage, with 3222 rows, 1719 unique values, and no nulls. The distribution is right-skewed (skew 2.10, kurtosis 6.89) with a median of 13.55 and mean 15.10, but a long tail stretching to a max of 66.32 versus a min of 1.6. About 4.25% of rows (137) are flagged as outliers, consistent with a small set of severely impoverished areas.

Treatment: Consider a log or sqrt transform before regression to tame the right skew.

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

saturn.columns["poverty_rate"].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 14.
Distribution of poverty_rate. 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

snap_eligible_est numeric feature

A numeric estimate of SNAP-eligible counts per record, with 3222 non-null rows and 2839 unique values. The distribution is severely right-skewed (skew 14.73, kurtosis 342.21): the median is 3799.5 but the max reaches 1,343,978, and 11.2% of rows flag as outliers. No nulls or zeros are present, so the spread is real, not missingness artefact.

Treatment: log-transform (or winsorize) before any distance- or variance-sensitive modelling.

anthropic:claude-opus-4-7 · confidence high
Out[34]:

saturn.columns["snap_eligible_est"].stats

statvalue
n3,222
nulls0 (0.0%)
unique2,839
min 3
max 1.344e+06
mean 1.3e+04
median 3800
std 4.326e+04
q1 1526
q3 9768
iqr 8242
skew 14.73
kurtosis 342.2
n_outliers 362
outlier_rate 0.1124
zero_rate 0
alert: high_skewskew=+14.73
alert: outliers11.2% rows beyond 1.5 IQR
Fig 15.
Distribution of snap_eligible_est. Vertical dash marks the median.
Show data table
bincount
3 – 3.36e+042963
3.36e+04 – 6.72e+04152
6.72e+04 – 1.008e+0549
1.008e+05 – 1.344e+0519
1.344e+05 – 1.68e+0510
1.68e+05 – 2.016e+056
2.016e+05 – 2.352e+053
2.352e+05 – 2.688e+053
2.688e+05 – 3.024e+055
3.024e+05 – 3.36e+051
3.36e+05 – 3.696e+054
3.696e+05 – 4.032e+051
4.032e+05 – 4.368e+051
4.368e+05 – 4.704e+050
4.704e+05 – 5.04e+052
5.04e+05 – 5.376e+050
5.376e+05 – 5.712e+050
5.712e+05 – 6.048e+050
6.048e+05 – 6.384e+050
6.384e+05 – 6.72e+050
6.72e+05 – 7.056e+051
7.056e+05 – 7.392e+051
7.392e+05 – 7.728e+050
7.728e+05 – 8.064e+050
8.064e+05 – 8.4e+050
8.4e+05 – 8.736e+050
8.736e+05 – 9.072e+050
9.072e+05 – 9.408e+050
9.408e+05 – 9.744e+050
9.744e+05 – 1.008e+060
1.008e+06 – 1.042e+060
1.042e+06 – 1.075e+060
1.075e+06 – 1.109e+060
1.109e+06 – 1.142e+060
1.142e+06 – 1.176e+060
1.176e+06 – 1.21e+060
1.21e+06 – 1.243e+060
1.243e+06 – 1.277e+060
1.277e+06 – 1.31e+060
1.31e+06 – 1.344e+061

snap_participants_est numeric feature

Estimated SNAP participant counts per record, ranging from 2 to 900,465 with a median of 2,546 and mean of 8,711. The distribution is severely right-skewed (skew 14.73, kurtosis 342.21) with 362 outliers (11.2%) and a standard deviation (28,987) more than three times the mean, suggesting a few very large jurisdictions dominate. No nulls or zeros are present across 3,222 rows.

Treatment: log-transform before regression to tame the heavy right tail.

anthropic:claude-opus-4-7 · confidence high
Out[37]:

saturn.columns["snap_participants_est"].stats

statvalue
n3,222
nulls0 (0.0%)
unique2,636
min 2
max 900,465
mean 8711
median 2,546
std 2.899e+04
q1 1022
q3 6544
iqr 5,522
skew 14.73
kurtosis 342.2
n_outliers 362
outlier_rate 0.1124
zero_rate 0
alert: high_skewskew=+14.73
alert: outliers11.2% rows beyond 1.5 IQR
Fig 16.
Distribution of snap_participants_est. Vertical dash marks the median.
Show data table
bincount
2 – 2.251e+042963
2.251e+04 – 4.503e+04152
4.503e+04 – 6.754e+0449
6.754e+04 – 9.005e+0419
9.005e+04 – 1.126e+0510
1.126e+05 – 1.351e+056
1.351e+05 – 1.576e+053
1.576e+05 – 1.801e+053
1.801e+05 – 2.026e+055
2.026e+05 – 2.251e+051
2.251e+05 – 2.476e+054
2.476e+05 – 2.701e+051
2.701e+05 – 2.927e+051
2.927e+05 – 3.152e+050
3.152e+05 – 3.377e+052
3.377e+05 – 3.602e+050
3.602e+05 – 3.827e+050
3.827e+05 – 4.052e+050
4.052e+05 – 4.277e+050
4.277e+05 – 4.502e+050
4.502e+05 – 4.727e+051
4.727e+05 – 4.953e+051
4.953e+05 – 5.178e+050
5.178e+05 – 5.403e+050
5.403e+05 – 5.628e+050
5.628e+05 – 5.853e+050
5.853e+05 – 6.078e+050
6.078e+05 – 6.303e+050
6.303e+05 – 6.528e+050
6.528e+05 – 6.753e+050
6.753e+05 – 6.979e+050
6.979e+05 – 7.204e+050
7.204e+05 – 7.429e+050
7.429e+05 – 7.654e+050
7.654e+05 – 7.879e+050
7.879e+05 – 8.104e+050
8.104e+05 – 8.329e+050
8.329e+05 – 8.554e+050
8.554e+05 – 8.78e+050
8.78e+05 – 9.005e+051

no_vehicle_total numeric feature

This column appears to be an aggregate vehicle count (likely total number of vehicles per record/area). The distribution is extremely heavy-tailed: median is 580 but the mean is 3304 and the maximum reaches 601,621, with skew of 20.26 and kurtosis of 501.27. About 12.6% of rows (407) flag as outliers, while only 0.37% are zeros and there are no nulls.

Treatment: Log-transform (or winsorize) before any distance- or variance-based modelling.

anthropic:claude-opus-4-7 · confidence high
Out[40]:

saturn.columns["no_vehicle_total"].stats

statvalue
n3,222
nulls0 (0.0%)
unique1,823
min 0
max 601,621
mean 3304
median 580
std 2.005e+04
q1 223
q3 1555
iqr 1332
skew 20.26
kurtosis 501.3
n_outliers 407
outlier_rate 0.1263
zero_rate 0.003724
alert: high_skewskew=+20.26
alert: outliers12.6% rows beyond 1.5 IQR
Fig 17.
Distribution of no_vehicle_total. Vertical dash marks the median.
Show data table
bincount
0 – 1.504e+043108
1.504e+04 – 3.008e+0462
3.008e+04 – 4.512e+0419
4.512e+04 – 6.016e+049
6.016e+04 – 7.52e+048
7.52e+04 – 9.024e+042
9.024e+04 – 1.053e+054
1.053e+05 – 1.203e+053
1.203e+05 – 1.354e+050
1.354e+05 – 1.504e+050
1.504e+05 – 1.654e+050
1.654e+05 – 1.805e+050
1.805e+05 – 1.955e+051
1.955e+05 – 2.106e+050
2.106e+05 – 2.256e+050
2.256e+05 – 2.406e+050
2.406e+05 – 2.557e+050
2.557e+05 – 2.707e+050
2.707e+05 – 2.858e+050
2.858e+05 – 3.008e+052
3.008e+05 – 3.159e+050
3.159e+05 – 3.309e+051
3.309e+05 – 3.459e+050
3.459e+05 – 3.61e+050
3.61e+05 – 3.76e+051
3.76e+05 – 3.911e+050
3.911e+05 – 4.061e+050
4.061e+05 – 4.211e+050
4.211e+05 – 4.362e+050
4.362e+05 – 4.512e+050
4.512e+05 – 4.663e+050
4.663e+05 – 4.813e+050
4.813e+05 – 4.963e+050
4.963e+05 – 5.114e+050
5.114e+05 – 5.264e+050
5.264e+05 – 5.415e+050
5.415e+05 – 5.565e+051
5.565e+05 – 5.715e+050
5.715e+05 – 5.866e+050
5.866e+05 – 6.016e+051

no_vehicle_pct numeric feature

Likely a per-area percentage of households without a vehicle, given values bounded between 0.0 and 85.94 with a median of 5.41 and Q1-Q3 of 3.98-7.36. The distribution is severely right-skewed (skew 6.98, kurtosis 86.23) with 140 outliers (4.35%) stretching far above the typical range, while only 0.37% of rows are exactly zero. No nulls across 3,222 rows.

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

anthropic:claude-opus-4-7 · confidence high
Out[43]:

saturn.columns["no_vehicle_pct"].stats

statvalue
n3,222
nulls0 (0.0%)
unique1,065
min 0
max 85.94
mean 6.197
median 5.41
std 4.538
q1 3.98
q3 7.36
iqr 3.38
skew 6.976
kurtosis 86.23
n_outliers 140
outlier_rate 0.04345
zero_rate 0.003724
alert: high_skewskew=+6.98
Fig 18.
Distribution of no_vehicle_pct. Vertical dash marks the median.
Show data table
bincount
0 – 2.148161
2.148 – 4.297823
4.297 – 6.4451091
6.445 – 8.594630
8.594 – 10.74283
10.74 – 12.89111
12.89 – 15.0461
15.04 – 17.1923
17.19 – 19.348
19.34 – 21.483
21.48 – 23.634
23.63 – 25.782
25.78 – 27.933
27.93 – 30.082
30.08 – 32.232
32.23 – 34.382
34.38 – 36.522
36.52 – 38.672
38.67 – 40.820
40.82 – 42.971
42.97 – 45.120
45.12 – 47.271
47.27 – 49.420
49.42 – 51.560
51.56 – 53.710
53.71 – 55.861
55.86 – 58.011
58.01 – 60.160
60.16 – 62.312
62.31 – 64.450
64.45 – 66.61
66.6 – 68.750
68.75 – 70.90
70.9 – 73.050
73.05 – 75.20
75.2 – 77.350
77.35 – 79.491
79.49 – 81.640
81.64 – 83.790
83.79 – 85.941

How to cite

click to copy

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