rural urban

saturn notebook · generated 2026-05-01 Report Notebook

Overview

Source: /home/coolhand/html/datavis/data_trove/cache/rural_urban.parquet

Saturn profiled 3,222 rows across 4 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/html/datavis/data_trove/cache/rural_urban.parquet",
    "--findings", "rural_urban.json",
    "--llm", "anthropic:claude-opus-4-7",
])

Summary confidence: high

This dataset is a county-level reference table covering 3,222 U.S. counties, with each row uniquely identified by a county name and FIPS code and labeled as either rural or urban/suburban. The headline finding is the rural skew: 2,212 counties (about 68.7%) are flagged Rural versus 1,010 Urban/Suburban, and the `rural` and `rural_category` columns are perfectly redundant duplicates of each other. County names are dominated by Texas (256), Virginia (189), and Georgia (159), reflecting how many counties those states contain rather than any data quality issue.

citing: row_count · column_count · columns.rural.top_values · columns.rural.stats.top_rate · columns.rural_category.top_values · columns.county_name.top_words · columns.fips.stats.min · columns.fips.stats.max

Out[4]:

saturn.schema() · 4 columns

column	kind	n	null%	unique	alerts
fips	numeric	3,222	0.0%	3,222
county_name	text	3,222	0.0%	3,222	near_unique
rural	categorical	3,222	0.0%	2
rural_category	categorical	3,222	0.0%	2

Fig 1.

rural_category · Shows the roughly 69/31 split between Rural and Urban/Suburban counties.

Show data table

Top values for rural_category (2 unique shown, of 2 total).
value	count	share
Rural	2212	68.7%
Urban/Suburban	1010	31.3%

Fig 2.

fips · Distribution of FIPS codes spans 1001 to 72153, indicating coverage from Alabama through Puerto Rico.

Show data table

Histogram bins for fips (median: 30022.0).
bin	count
1001 – 2780	97
2780 – 4559	15
4559 – 6337	133
6337 – 8116	59
8116 – 9895	14
9895 – 1.167e+04	4
1.167e+04 – 1.345e+04	226
1.345e+04 – 1.523e+04	5
1.523e+04 – 1.701e+04	49
1.701e+04 – 1.879e+04	189
1.879e+04 – 2.057e+04	204
2.057e+04 – 2.235e+04	184
2.235e+04 – 2.413e+04	39
2.413e+04 – 2.59e+04	15
2.59e+04 – 2.768e+04	170
2.768e+04 – 2.946e+04	196
2.946e+04 – 3.124e+04	150
3.124e+04 – 3.302e+04	27
3.302e+04 – 3.48e+04	21
3.48e+04 – 3.658e+04	95
3.658e+04 – 3.836e+04	153
3.836e+04 – 4.013e+04	155
4.013e+04 – 4.191e+04	46
4.191e+04 – 4.369e+04	67
4.369e+04 – 4.547e+04	51
4.547e+04 – 4.725e+04	161
4.725e+04 – 4.903e+04	268
4.903e+04 – 5.081e+04	29
5.081e+04 – 5.259e+04	133
5.259e+04 – 5.436e+04	94
5.436e+04 – 5.614e+04	95
5.614e+04 – 5.792e+04	0
5.792e+04 – 5.97e+04	0
5.97e+04 – 6.148e+04	0
6.148e+04 – 6.326e+04	0
6.326e+04 – 6.504e+04	0
6.504e+04 – 6.682e+04	0
6.682e+04 – 6.86e+04	0
6.86e+04 – 7.037e+04	0
7.037e+04 – 7.215e+04	78

Fig 3.

rural · Confirms `rural` mirrors `rural_category` exactly — one of the two columns is redundant.

Show data table

Top values for rural (2 unique shown, of 2 total).
value	count	share
True	2212	68.7%
False	1010	31.3%

Fig 4.

county_name · County name lengths cluster tightly around 24 characters, useful for sanity-checking display widths.

Show data table

Character-length distribution for county_name (mean: 24.324022346368714).
chars	count
16 – 17	26
17 – 18	72
18 – 19	121
19 – 20	190
20 – 21	264
21 – 22	407
22 – 24	420
24 – 25	363
25 – 26	320
26 – 27	240
27 – 28	231
28 – 29	152
29 – 30	139
30 – 31	165
31 – 32	41
32 – 33	28
33 – 34	16
34 – 35	10
35 – 36	5
36 – 38	0
38 – 39	1
39 – 40	1
40 – 41	0
41 – 42	1
42 – 43	1
43 – 44	0
44 – 45	2
45 – 46	0
46 – 47	1
47 – 48	1
48 – 49	0
49 – 50	0
50 – 51	0
51 – 53	0
53 – 54	2
54 – 55	1
55 – 56	0
56 – 57	0
57 – 58	0
58 – 59	1

Fig 5.

Per-column null rate across the corpus. Columns are ordered by input position.

Show data table

Per-column null rate across the corpus.
column	kind	null %
fips	numeric	0.0%
county_name	text	0.0%
rural	categorical	0.0%
rural_category	categorical	0.0%

fips numeric identifier

This is the FIPS county/state code, with all 3222 rows unique and no nulls. Values span 1001 to 72153 with a near-symmetric distribution (skew 0.16, kurtosis -0.63), consistent with the standard 5-digit US county FIPS encoding rather than a measured quantity. Treat it as a categorical key, not a number.

Treatment: Cast to zero-padded string and use as a join key to geographic reference tables.

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

Out[11]:

saturn.columns["fips"].stats

stat	value
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

Fig 6.

Distribution of fips. Vertical dash marks the median.

Show data table

Histogram bins for fips (median: 30022.0).
bin	count
1001 – 2780	97
2780 – 4559	15
4559 – 6337	133
6337 – 8116	59
8116 – 9895	14
9895 – 1.167e+04	4
1.167e+04 – 1.345e+04	226
1.345e+04 – 1.523e+04	5
1.523e+04 – 1.701e+04	49
1.701e+04 – 1.879e+04	189
1.879e+04 – 2.057e+04	204
2.057e+04 – 2.235e+04	184
2.235e+04 – 2.413e+04	39
2.413e+04 – 2.59e+04	15
2.59e+04 – 2.768e+04	170
2.768e+04 – 2.946e+04	196
2.946e+04 – 3.124e+04	150
3.124e+04 – 3.302e+04	27
3.302e+04 – 3.48e+04	21
3.48e+04 – 3.658e+04	95
3.658e+04 – 3.836e+04	153
3.836e+04 – 4.013e+04	155
4.013e+04 – 4.191e+04	46
4.191e+04 – 4.369e+04	67
4.369e+04 – 4.547e+04	51
4.547e+04 – 4.725e+04	161
4.725e+04 – 4.903e+04	268
4.903e+04 – 5.081e+04	29
5.081e+04 – 5.259e+04	133
5.259e+04 – 5.436e+04	94
5.436e+04 – 5.614e+04	95
5.614e+04 – 5.792e+04	0
5.792e+04 – 5.97e+04	0
5.97e+04 – 6.148e+04	0
6.148e+04 – 6.326e+04	0
6.326e+04 – 6.504e+04	0
6.504e+04 – 6.682e+04	0
6.682e+04 – 6.86e+04	0
6.86e+04 – 7.037e+04	0
7.037e+04 – 7.215e+04	78

county_name text identifier

Each of the 3,222 rows holds a unique county-plus-state string (e.g., 'X County, Texas'), with 'county,' appearing 2,999 times and state names like Texas (256), Virginia (189), and Georgia (159) dominating the top words. Lengths are tight (16-59 chars, median 24) and there are zero nulls or duplicates, consistent with a complete US county roster. The near_unique alert is expected here rather than a data-quality issue.

Treatment: Use as a join key to county-level reference tables; do not feed raw into a model.

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

Out[14]:

saturn.columns["county_name"].stats

stat	value
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
alert: near_unique	100.0% of rows are unique strings

Fig 7.

Character-length distribution for county_name.

Show data table

Character-length distribution for county_name (mean: 24.324022346368714).
chars	count
16 – 17	26
17 – 18	72
18 – 19	121
19 – 20	190
20 – 21	264
21 – 22	407
22 – 24	420
24 – 25	363
25 – 26	320
26 – 27	240
27 – 28	231
28 – 29	152
29 – 30	139
30 – 31	165
31 – 32	41
32 – 33	28
33 – 34	16
34 – 35	10
35 – 36	5
36 – 38	0
38 – 39	1
39 – 40	1
40 – 41	0
41 – 42	1
42 – 43	1
43 – 44	0
44 – 45	2
45 – 46	0
46 – 47	1
47 – 48	1
48 – 49	0
49 – 50	0
50 – 51	0
51 – 53	0
53 – 54	2
54 – 55	1
55 – 56	0
56 – 57	0
57 – 58	0
58 – 59	1

rural categorical feature

Binary boolean flag indicating whether a record is rural, fully populated across all 3222 rows. The split is roughly 69/31 in favour of True (2212 vs 1010), giving a high entropy ratio of 0.90 — imbalanced but far from degenerate.

Treatment: Cast to 0/1 and use directly as a binary feature.

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

Out[17]:

saturn.columns["rural"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	2
top_value	True
top_rate	0.6865
cardinality	2
entropy	0.8971
entropy_ratio	0.8971

Fig 8.

Top values for rural.

Show data table

Top values for rural (2 unique shown, of 2 total).
value	count	share
True	2212	68.7%
False	1010	31.3%

rural_category categorical feature

Binary geographic classifier splitting records into 'Rural' (2212) and 'Urban/Suburban' (1010) across all 3222 rows with no nulls. The split is roughly 69/31 toward Rural, giving an entropy ratio of 0.897, so both classes are well represented despite the imbalance.

Treatment: Encode as a binary indicator (e.g., is_rural) for modelling.

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

Out[20]:

saturn.columns["rural_category"].stats

stat	value
n	3,222
nulls	0 (0.0%)
unique	2
top_value	Rural
top_rate	0.6865
cardinality	2
entropy	0.8971
entropy_ratio	0.8971

Fig 9.

Top values for rural_category.

Show data table

Top values for rural_category (2 unique shown, of 2 total).
value	count	share
Rural	2212	68.7%
Urban/Suburban	1010	31.3%

How to cite

click to copy

BibTeX

@misc{saturn-rural-urban-2026,
  author       = {Steuber, Luke},
  title        = {Saturn reading: rural urban},
  year         ={2026},
  howpublished = {\url{https://dr.eamer.dev/saturn/view/rural_urban}},
  note         = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:claude-opus-4-7},
}

APA

Steuber, L. (2026). Saturn reading: rural urban. Source: /home/coolhand/html/datavis/data_trove/cache/rural_urban.parquet. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:claude-opus-4-7). Retrieved from https://dr.eamer.dev/saturn/view/rural_urban