saturn

/home/coolhand/html/datavis/data_trove/data/urban/food_deserts/vehicle_access.csv 3,222 rows sample n=3,222 seed 42 2026-05-01T17:15:43+00:00

Overview

Source/home/coolhand/html/datavis/data_trove/data/urban/food_deserts/vehicle_access.csv
Total rows3,222
Profiled sample3,222
Columns9
Generated2026-05-01T17:15:43+00:00

Insights opt-in

Model-generated narrative. These are opinions, not facts — the stats below are what saturn measured. Generated by: anthropic:claude-opus-4-7.

Dataset high anthropic:claude-opus-4-7

This dataset covers vehicle access for 3,222 US counties (one row per county, identified by FIPS code and name) across 9 columns, with no missing values. The household and no-vehicle counts are extremely right-skewed — `no_vehicle_total` has a median of 580 but a max of 601,621, and `total_households` ranges from 32 up to roughly 3.36 million — so a handful of large urban counties dominate the absolute totals. The more comparable signal is `no_vehicle_pct`, which has a median of 5.41% but stretches up to 85.94%, flagging a small set of counties with extreme transit dependence worth investigating first. State coverage looks complete (52 distinct state codes), so geographic breakdowns should be straightforward.

name high anthropic:claude-opus-4-7

This column appears to hold US county names with state suffixes — 2,999 of 3,222 rows contain the token 'county,' and the remaining top words are state names (texas, virginia, georgia, north carolina, dakota, kentucky, missouri). Every value is unique (n_unique=3222, duplicate_rate=0.0) with no nulls, and lengths are tightly clustered (mean 24.3, min 16, max 59, p95 31), consistent with 'X County, State' formatting. The near_unique alert confirms this behaves as a row identifier rather than a categorical feature.

total_households high anthropic:claude-opus-4-7

Likely a count of households per geographic unit (e.g., county or tract), with 3222 rows, 3074 unique values, and no nulls or zeros. The distribution is extremely right-skewed (skew 12.05, kurtosis 240.5): the median is 10021 while the mean is 39402.86 and the max reaches 3363093, roughly 28x the standard deviation above the mean. Saturn flags 443 outliers (13.7%), consistent with a few very large jurisdictions dominating the tail.

no_vehicle_owner high anthropic:claude-opus-4-7

Likely a count of non-vehicle-owners aggregated per row (e.g., per geography or unit), ranging from 0 to 113,473 with a median of just 214. The distribution is severely right-skewed (skew 18.55, kurtosis 433.5) with 360 outliers (11.2%) and a std (3777.8) nearly 5x the mean (820.8), signalling a heavy tail dominated by a few extreme rows. Only 1.2% of rows are zero and there are no nulls, so the column is densely populated but dispersed across 1,176 unique values.

no_vehicle_renter medium anthropic:claude-opus-4-7

A heavily right-skewed numeric count, plausibly the number of renters without a vehicle per record/area. The median is 351 but the mean is 2483 and the max reaches 488148, with skew 20.7 and kurtosis 517.5 driving 436 outliers (13.5%). About 1.5% of rows are zero and there are no nulls, so the long tail — not missingness — dominates the distribution.

state high anthropic:claude-opus-4-7

Despite being typed as numeric, this column holds 52 distinct integer values between 1 and 72 with no nulls or zeros, which matches a FIPS-style state code encoding (50 states plus DC and territories) rather than a true measurement. The near-uniform spread (mean 31.27, median 30, std 16.29, skew 0.16) and absence of outliers reinforce that these are categorical identifiers, not quantities.

county high anthropic:claude-opus-4-7

Despite the name 'county', the column is stored numerically with 330 unique values across 3222 rows and no nulls, suggesting it holds county FIPS codes or similar integer identifiers rather than a measured quantity. The distribution is heavily right-skewed (skew 2.87, kurtosis 11.6) with a median of 79 but a max of 840 and 178 outliers (5.5%), which is expected for code-like values but misleading if treated as a continuous feature.

fips high anthropic:claude-opus-4-7

This is the FIPS county code: every one of the 3222 rows is unique with no nulls, and the value range (1001 to 72153) matches the standard 5-digit state+county FIPS encoding. Distribution stats (mean 31377, median 30022, near-zero skew) are essentially meaningless here since the codes are categorical identifiers, not quantities. No outliers flagged, consistent with a clean geographic key.

no_vehicle_total high anthropic:claude-opus-4-7

Counts of vehicles (totals) per record, ranging from 0 to 601,621 with a median of 580 but a mean of 3,304. The distribution is extremely right-skewed (skew 20.26, kurtosis 501.27) with 407 outliers (12.6%) and a std (20,050) far exceeding the IQR (1,331.75), indicating a few enormous records dominate.

no_vehicle_pct high anthropic:claude-opus-4-7

Likely the percentage of households (or similar units) with no vehicle, recorded for 3,222 rows with no nulls and values bounded between 0.0 and 85.94. The distribution is tightly clustered (median 5.41, IQR 3.38) but extremely right-skewed (skew 6.98, kurtosis 86.23), with 140 outliers (4.35%) pulling the mean to 6.20 well above the median. Only 0.37% are exact zeros, so true absence is rare; the long tail is the surprise here.

Numeric correlation

name text

100.0% of rows are unique strings
rows3,222
null0 (0.0%)
unique3,222
len_min16
len_max59
len_mean24.324
len_median24.000
len_p9531.000
word_mean3.248
word_median3.000
n_empty0
n_duplicates0
duplicate_rate0.000
vocab_size1,990
readability_flesch_mean10.284
emoji_rate0.000
url_rate0.000
one_word_rate0.000
allcaps_rate0.000
boilerplate_rate0.000
Sample values (first 10)
  1. Bibb County, Alabama
  2. Cheatham County, Tennessee
  3. Piute County, Utah
  4. Lamb County, Texas
  5. Martin County, Minnesota
  6. Sheridan County, Wyoming
  7. Chickasaw County, Mississippi
  8. Rockingham County, Virginia
  9. Liberty County, Texas
  10. Clark County, Arkansas

total_households numeric

skew=+12.05 13.7% rows beyond 1.5 IQR
rows3,222
null0 (0.0%)
unique3,074
min32.000
max3,363,093
mean39,403
median10,021
std120,103
q14,211
q325,939
iqr21,728
skew12.048
kurtosis240.507
n_outliers443
outlier_rate0.137
zero_rate0.000

no_vehicle_owner numeric

skew=+18.55 11.2% rows beyond 1.5 IQR
rows3,222
null0 (0.0%)
unique1,176
min0.000
max113,473
mean820.841
median214.000
std3,778
q181.000
q3548.750
iqr467.750
skew18.553
kurtosis433.507
n_outliers360
outlier_rate0.112
zero_rate0.012

no_vehicle_renter numeric

skew=+20.69 13.5% rows beyond 1.5 IQR
rows3,222
null0 (0.0%)
unique1,517
min0.000
max488,148
mean2,483
median351.000
std16,457
q1125.250
q3987.750
iqr862.500
skew20.692
kurtosis517.492
n_outliers436
outlier_rate0.135
zero_rate0.015

state numeric

rows3,222
null0 (0.0%)
unique52
min1.000
max72.000
mean31.275
median30.000
std16.285
q119.000
q346.000
iqr27.000
skew0.157
kurtosis-0.627
n_outliers0
outlier_rate0.000
zero_rate0.000

county numeric

skew=+2.87 5.5% rows beyond 1.5 IQR
rows3,222
null0 (0.0%)
unique330
min1.000
max840.000
mean103.216
median79.000
std106.561
q135.000
q3133.000
iqr98.000
skew2.866
kurtosis11.640
n_outliers178
outlier_rate0.055
zero_rate0.000

fips numeric

rows3,222
null0 (0.0%)
unique3,222
min1,001
max72,153
mean31,378
median30,022
std16,300
q119,030
q346,104
iqr27,075
skew0.157
kurtosis-0.631
n_outliers0
outlier_rate0.000
zero_rate0.000

no_vehicle_total numeric

skew=+20.26 12.6% rows beyond 1.5 IQR
rows3,222
null0 (0.0%)
unique1,823
min0.000
max601,621
mean3,304
median580.000
std20,050
q1223.000
q31,555
iqr1,332
skew20.257
kurtosis501.273
n_outliers407
outlier_rate0.126
zero_rate3.72e-03

no_vehicle_pct numeric

skew=+6.98
rows3,222
null0 (0.0%)
unique1,065
min0.000
max85.940
mean6.197
median5.410
std4.538
q13.980
q37.360
iqr3.380
skew6.976
kurtosis86.230
n_outliers140
outlier_rate0.043
zero_rate3.72e-03