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food deserts snap participation

source /home/coolhand/html/datavis/data_trove/data/urban/food_deserts/snap_participation.csv 3,222 rows 9 columns profiled 2026-05-01 raw JSON static .html .ipynb Report Notebook

Reading

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

This dataset covers 3,222 U.S. counties with population, poverty, and SNAP participation estimates alongside FIPS and state identifiers. Population and SNAP-related counts are extremely right-skewed — total_pop has a skew of 13.4 and a max of 9.78M against a median of just 25,174, with similar long tails in poverty_pop and snap_participants_est. The poverty_rate column is more behaved (median 13.55%, max 66.32%) and is probably the most useful field for cross-county comparison without log-scaling. Note that snap_eligible_est appears to be an exact duplicate of poverty_pop (identical stats), which is worth verifying before using either as an independent variable. State coverage spans 52 distinct values, so DC and territories are included.

citing: row_count · column_count · columns.total_pop.stats · columns.poverty_rate.stats · columns.poverty_pop.stats · columns.snap_eligible_est.stats · columns.snap_participants_est.stats · columns.state.n_unique · columns.name.top_words

poverty_rate · Most counties cluster between 10–18% poverty, with a tail extending past 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
total_pop · Heavy right skew — a handful of large-population counties dwarf the rest; consider a log scale.
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
snap_participants_est · Distribution of SNAP participant estimates is similarly long-tailed and tracks population size.
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
state · Counts of counties per state show which states contribute most rows (Texas, Virginia, Georgia lead).
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
poverty_pop · Compare against snap_eligible_est — the two columns appear identical and may be redundant.
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

Schema

9 columns
Alerts
name text 0.0% 3,222
near_unique
total_pop numeric 0.0% 3,173
high_skew outliers
poverty_pop numeric 0.0% 2,839
high_skew outliers
state numeric 0.0% 52
county numeric 0.0% 330
high_skew outliers
fips numeric 0.0% 3,222
poverty_rate numeric 0.0% 1,719
high_skew
snap_eligible_est numeric 0.0% 2,839
high_skew outliers
snap_participants_est numeric 0.0% 2,636
high_skew outliers

name

text identifier near_unique
This column holds U.S. county names with state qualifiers — 2999 of 3222 values contain the token 'county,' followed by state names like Texas (256), Virginia (189), and Georgia (159). Every one of the 3222 rows is unique with zero nulls or duplicates, and lengths cluster tightly between 16 and 31 characters (mean 24.3, 3.2 words). The near_unique alert plus the 'County, State' pattern strongly suggests this is a geographic identifier rather than free text. Treatment: Parse into separate county and state fields, then use as a join key to geographic reference data. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
3,222
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

total_pop

numeric feature high_skew outliers
This is a population count by geographic unit, with 3,222 rows, no nulls, and 3,173 unique values, suggesting one row per area (likely U.S. counties given the count). The distribution is extremely heavy-tailed: median is 25,174 but the mean is 101,340 and the max reaches 9,782,602, producing a skew of 13.36 and kurtosis of 297.59. About 13.9% of rows (449) flag as outliers, reflecting a small number of very large jurisdictions dominating the tail. Treatment: log-transform before regression or any distance-based modelling. 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

poverty_pop

numeric feature high_skew outliers
This is a count of the population in poverty per record (likely county or similar geography), 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) with 362 outliers (11.2%) reflecting a few very populous areas dwarfing the rest. No nulls or zeros, and 2,839 unique values across 3,222 rows suggest near-record-level granularity. Treatment: Log-transform or convert to a poverty rate before modelling. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
2,839
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

state

numeric identifier
Stored as numeric but the values look like FIPS-style state codes: 52 unique integers spanning 1 to 72 across 3222 rows with no nulls and no zeros. The roughly uniform spread (mean 31.27, median 30, std 16.29, skew 0.16, kurtosis -0.63) and the count of 52 distinct codes are consistent with US states/territories rather than a continuous measurement. Treatment: Treat as a categorical state code and map to labels before modelling, not as a numeric feature. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
52
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

county

numeric identifier high_skew outliers
Despite being labeled 'county', this column holds small integers ranging from 1 to 840 with 330 unique values across 3,222 rows, suggesting it's a numeric county code (likely a FIPS-style identifier) rather than a true measurement. The distribution is heavily right-skewed (skew 2.87, kurtosis 11.6) with 178 outliers (5.5%), which is expected for ID codes but meaningless as a numeric signal. The median (79) sits well below the mean (103), and there are no nulls or zeros. Treatment: Treat as a categorical code; do not use as a numeric feature — encode or join to a county lookup. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
330
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

fips

numeric identifier
This is the FIPS county/state code identifier — values span 1001 to 72153 and every one of the 3222 rows is unique with no nulls. The distribution is near-uniform across the code range (skew 0.16, kurtosis -0.63), consistent with a geographic key rather than a measured quantity. Treatment: left-join on this id; do not use as a numeric feature. 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

poverty_rate

numeric feature high_skew
This is a numeric poverty rate, almost certainly a percentage given the range from 1.6 to 66.32 and a median of 13.55, plausibly at a county or tract level given n=3222. The distribution is right-skewed (skew 2.10, kurtosis 6.89) with 137 outliers (4.25%) sitting well above the Q3 of 17.91, indicating a long tail of high-poverty areas. No nulls and no zeros, and 1719 unique values suggest some rounding to one decimal. Treatment: Apply a log or Yeo-Johnson transform before regression to tame the right skew. 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

snap_eligible_est

numeric feature high_skew outliers
Numeric estimate of SNAP-eligible population per record, with all 3222 rows populated and 2839 unique values. The distribution is extremely right-skewed (skew 14.73, kurtosis 342.21): median is 3799.5 but the mean is 13001.22 and the max reaches 1,343,978, roughly 31x the standard deviation above the mean. About 11.2% of rows (362) flag as outliers, so a small number of very large geographies dominate the tail. Treatment: Log-transform (no zeros, min=3) before any modelling or distance-based comparison. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
2,839
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

snap_participants_est

numeric feature high_skew outliers
Estimated SNAP participant counts per row, ranging from 2 to 900,465 with a median of 2,546 and mean of 8,710.83. The distribution is severely right-skewed (skew 14.73, kurtosis 342.21) with std 28,987.13 dwarfing the IQR of 5,522, and 362 rows (11.24%) flagged as outliers — likely a few very large geographies pulling the tail. No nulls or zeros, and 2,636 of 3,222 values are unique. Treatment: log-transform before regression to tame the heavy right tail. high · anthropic:claude-opus-4-7
n
3,222
nulls
0 (0.0%)
unique
2,636
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