saturn·

data trove global volcanism program

saturn notebook · generated 2026-06-22 Report Notebook

Overview

Source: /home/coolhand/html/datavis/data_trove/data/quirky/volcanoes.json

Saturn profiled 200 rows across 9 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/data/quirky/volcanoes.json",
    "--findings", "data-trove-global-volcanism-program.json",
    "--llm", "anthropic:default",
])

Summary confidence: high

This dataset captures 200 volcanic eruption records across 33 countries, covering events from 1900 to 1999, with 9 attributes including eruption intensity, volcano type, elevation, and geographic coordinates. The most striking feature is the heavy geographic concentration — Indonesia alone accounts for 28.5% of all records (57 out of 200), with Semeru appearing 13 times as the single most frequent volcano. Volcano type is strongly skewed toward stratovolcanoes, which make up 69.5% of all records, so the 'type' breakdown is worth examining to understand how rare other forms like calderas or shield volcanoes are by comparison. The Volcanic Explosivity Index (VEI) flags 15 outliers at the high end, with a maximum of 6.0 against a mean of 2.6, suggesting a small number of exceptionally powerful eruptions that deserve individual attention.

citing: row_count · column_count · country.top_value · country.top_rate · name.top_value · name.top_values · type.top_value · type.top_rate · vei.n_outliers · vei.max · vei.mean · year.min · year.max

Out[4]:

saturn.schema() · 9 columns

column kind n null% unique alerts
name categorical 200 0.0% 111 long_tail
country categorical 200 0.0% 33
lat numeric 200 0.0% 111
lon numeric 200 0.0% 111
elevation numeric 200 0.0% 109
type categorical 200 0.0% 13
vei numeric 200 0.0% 7 outliers
year numeric 200 0.0% 84
last_eruption numeric 200 0.0% 84
Fig 1.
country · Look for Indonesia's outsized dominance over all other countries — it dwarfs even second-place Japan (29 records).
Show data table
Top values for country (20 unique shown, of 33 total).
valuecountshare
Indonesia5728.5%
Japan2914.5%
Italy136.5%
Philippines126.0%
Guatemala84.0%
Papua New Guinea84.0%
United States84.0%
Chile84.0%
Russia73.5%
Ecuador42.0%
Mexico42.0%
Tanzania42.0%
Cameroon31.5%
Iceland31.5%
Congo, DRC31.5%
Solomon Is.31.5%
New Zealand31.5%
Nicaragua21.0%
Vanuatu21.0%
Colombia21.0%
Fig 2.
type · Stratovolcanoes account for nearly 70% of records; check how thin the slices are for all other volcano types combined.
Show data table
Top values for type (13 unique shown, of 13 total).
valuecountshare
Stratovolcano13969.5%
Complex volcano2211.0%
Shield volcano126.0%
Caldera94.5%
Submarine volcano42.0%
Pyroclastic shield31.5%
Lava dome31.5%
Maar21.0%
Tuff cone21.0%
Cinder cone10.5%
Compound volcano10.5%
Pyroclastic cone10.5%
Subglacial volcano10.5%
Fig 3.
vei · Most eruptions cluster between VEI 2 and 3, but watch for the long right tail where 15 outliers reach up to VEI 6.
Show data table
Histogram bins for vei (median: 3.0).
bincount
0 – 0.42867
0.4286 – 0.85710
0.8571 – 1.28615
1.286 – 1.7140
1.714 – 2.14377
2.143 – 2.5710
2.571 – 30
3 – 3.42970
3.429 – 3.8570
3.857 – 4.28623
4.286 – 4.7140
4.714 – 5.1436
5.143 – 5.5710
5.571 – 62
Fig 4.
elevation · Elevation spans from below sea level to over 5,000 m — look for the broad spread and any clustering around mid-range peaks.
Show data table
Histogram bins for elevation (median: 1848.5).
bincount
-185 – 213.44
213.4 – 611.917
611.9 – 101027
1010 – 140924
1409 – 180727
1807 – 220612
2206 – 260422
2604 – 300223
3002 – 340111
3401 – 379922
3799 – 41983
4198 – 45960
4596 – 49954
4995 – 53934
Fig 5.
year · Eruption records span the entire 20th century; check whether the distribution is roughly uniform or shows reporting gaps in early decades.
Show data table
Histogram bins for year (median: 1955.0).
bincount
1900 – 190716
1907 – 191414
1914 – 192112
1921 – 19289
1928 – 193512
1935 – 194210
1942 – 195014
1950 – 195715
1957 – 196415
1964 – 197117
1971 – 197816
1978 – 198521
1985 – 199213
1992 – 199916
Fig 6.
Per-column null rate across the corpus. Columns are ordered by input position.
Show data table
Per-column null rate across the corpus.
columnkindnull %
namecategorical0.0%
countrycategorical0.0%
latnumeric0.0%
lonnumeric0.0%
elevationnumeric0.0%
typecategorical0.0%
veinumeric0.0%
yearnumeric0.0%
last_eruptionnumeric0.0%
Fig 7.
Pearson correlation across numeric columns (sampled, bounded).
Show data table
Pearson correlation across 6 numeric columns (values clipped to 2 decimals).
latlonelevationveiyearlast_eruption
lat+1.00-0.04-0.07+0.13+0.03+0.03
lon-0.04+1.00-0.26-0.16-0.00-0.00
elevation-0.07-0.26+1.00+0.06+0.14+0.14
vei+0.13-0.16+0.06+1.00+0.03+0.03
year+0.03-0.00+0.14+0.03+1.00+1.00
last_eruption+0.03-0.00+0.14+0.03+1.00+1.00

name categorical label

This column contains volcano names, functioning as a label for individual volcanic entities in the dataset. With 111 unique values across 200 rows, many volcanoes appear multiple times — 'Semeru' leads with 13 occurrences (6.5% of rows), suggesting repeated eruption or activity events per volcano rather than one row per volcano. The high entropy ratio of 0.946 combined with the long-tail alert indicates the distribution is broad but uneven, with a handful of well-known volcanoes (Semeru, Merapi, Etna, Stromboli) dominating while most names appear only once or twice.

Treatment: Group by this column to aggregate per-volcano statistics, or encode as a categorical feature with frequency-based or target encoding given the long-tail distribution.

anthropic:default · confidence high
Out[13]:

saturn.columns["name"].stats

statvalue
n200
nulls0 (0.0%)
unique111
top_value Semeru
top_rate 0.065
cardinality 111
entropy 6.427
entropy_ratio 0.9459
alert: long_tail69 singleton categories
Fig 8.
Top values for name.
Show data table
Top values for name (20 unique shown, of 111 total).
valuecountshare
Semeru136.5%
Merapi84.0%
Fuego52.5%
Kelud52.5%
Etna52.5%
Mayon52.5%
Asamayama52.5%
Stromboli42.0%
Paluweh42.0%
Dieng Volcanic Complex42.0%
Lengai, Ol Doinyo42.0%
Izu-Oshima31.5%
Iliwerung31.5%
Toya31.5%
Karangetang31.5%
Villarrica31.5%
Aira31.5%
Vesuvius31.5%
Tungurahua21.0%
Rabaul21.0%

country categorical label

This column records the country associated with each record — likely the location of a seismic, volcanic, or natural-disaster event given the top countries (Indonesia, Japan, Philippines, Papua New Guinea, Chile). Indonesia dominates heavily at 28.5% of all 200 rows (57 occurrences), followed by Japan at 14.5%, which is a pronounced geographic skew toward the Pacific Ring of Fire. With only 33 unique values and zero nulls, coverage is clean, but the top-heavy distribution (entropy ratio 0.78) means most records cluster around a handful of high-activity nations.

Treatment: One-hot encode or target-encode for modelling; be aware of class imbalance with Indonesia representing 28.5% of rows.

anthropic:default · confidence high
Out[16]:

saturn.columns["country"].stats

statvalue
n200
nulls0 (0.0%)
unique33
top_value Indonesia
top_rate 0.285
cardinality 33
entropy 3.934
entropy_ratio 0.7799
Fig 9.
Top values for country.
Show data table
Top values for country (20 unique shown, of 33 total).
valuecountshare
Indonesia5728.5%
Japan2914.5%
Italy136.5%
Philippines126.0%
Guatemala84.0%
Papua New Guinea84.0%
United States84.0%
Chile84.0%
Russia73.5%
Ecuador42.0%
Mexico42.0%
Tanzania42.0%
Cameroon31.5%
Iceland31.5%
Congo, DRC31.5%
Solomon Is.31.5%
New Zealand31.5%
Nicaragua21.0%
Vanuatu21.0%
Colombia21.0%

lat numeric feature

This column contains geographic latitude values, spanning from -41.33° (southern hemisphere, e.g., southern South America or New Zealand) to 63.983° (northern hemisphere, e.g., Scandinavia or Canada), consistent with a globally distributed dataset. With only 111 unique values across 200 rows, many locations are repeated, suggesting the dataset references a limited set of geographic points rather than unique coordinates per record. The distribution is nearly symmetric (skew 0.20, kurtosis -0.48) and spans a wide IQR of 40.73°, indicating broad global coverage rather than clustering in one region. No nulls, outliers, or zeros are present.

Treatment: Pair with a longitude column for geospatial analysis; consider binning into regions or using as-is in spatial models.

anthropic:default · confidence high
Out[19]:

saturn.columns["lat"].stats

statvalue
n200
nulls0 (0.0%)
unique111
min -41.33
max 63.98
mean 10.08
median 4.548
std 24.29
q1 -7.935
q3 32.79
iqr 40.73
skew 0.1985
kurtosis -0.4762
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 10.
Distribution of lat. Vertical dash marks the median.
Show data table
Histogram bins for lat (median: 4.5475).
bincount
-41.33 – -33.8111
-33.81 – -26.290
-26.29 – -18.763
-18.76 – -11.244
-11.24 – -3.71857
-3.718 – 3.80424
3.804 – 11.338
11.33 – 18.8529
18.85 – 26.376
26.37 – 33.8910
33.89 – 41.4227
41.42 – 48.947
48.94 – 56.468
56.46 – 63.986

lon numeric feature

This column is a geographic longitude coordinate, spanning the full valid range of −175.65 to 177.18 degrees, indicating global coverage. Surprisingly, with only 111 unique values across 200 rows (~55% uniqueness), there is notable coordinate repetition, suggesting many records share the same location or coordinates have been rounded/binned. The mean (59.16) is substantially pulled away from the median (112.31) by a left skew (−0.86), implying a cluster of observations in Eastern hemisphere longitudes with a tail of negative (Western hemisphere) values dragging the mean down.

Treatment: Use as-is or pair with latitude for geospatial modelling; investigate duplicate coordinates (111 unique / 200 rows) to determine if binning or data quality issue.

anthropic:default · confidence high
Out[22]:

saturn.columns["lon"].stats

statvalue
n200
nulls0 (0.0%)
unique111
min -175.7
max 177.2
mean 59.16
median 112.3
std 97.97
q1 2.107
q3 130.4
iqr 128.3
skew -0.8561
kurtosis -0.6788
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 11.
Distribution of lon. Vertical dash marks the median.
Show data table
Histogram bins for lon (median: 112.314).
bincount
-175.7 – -150.49
-150.4 – -125.20
-125.2 – -1002
-100 – -74.8423
-74.84 – -49.6413
-49.64 – -24.440
-24.44 – 0.7653
0.765 – 25.9717
25.97 – 51.1710
51.17 – 76.371
76.37 – 101.62
101.6 – 126.866
126.8 – 15237
152 – 177.217

elevation numeric feature

This column represents geographic elevation in metres (or feet) for 200 location records, spanning from -185.0 (below sea level, consistent with places like the Dead Sea or Death Valley) to 5393.0 (alpine/high-altitude terrain). The distribution is broad and fairly flat — IQR of 1809.75 against a mean of 2074.21 — with a slight positive skew (0.39) and near-platykurtic shape (kurtosis -0.57), suggesting a deliberately diverse geographic sample rather than a natural population draw. With only 109 unique values across 200 rows, roughly 45% of values are repeated, which may indicate rounding to nearest metre or binned elevation bands.

Treatment: Use as-is or apply mild normalisation (e.g. standard scaling); the negative minimum requires care if log-transforming — shift first.

anthropic:default · confidence high
Out[25]:

saturn.columns["elevation"].stats

statvalue
n200
nulls0 (0.0%)
unique109
min -185
max 5,393
mean 2074
median 1848
std 1235
q1 1113
q3 2,923
iqr 1810
skew 0.388
kurtosis -0.5738
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 12.
Distribution of elevation. Vertical dash marks the median.
Show data table
Histogram bins for elevation (median: 1848.5).
bincount
-185 – 213.44
213.4 – 611.917
611.9 – 101027
1010 – 140924
1409 – 180727
1807 – 220612
2206 – 260422
2604 – 300223
3002 – 340111
3401 – 379922
3799 – 41983
4198 – 45960
4596 – 49954
4995 – 53934

type categorical label

This column classifies volcanic structures into 13 morphological types, making it a geological label for each record. 'Stratovolcano' dominates heavily at 69.5% of 200 records (139 occurrences), while the remaining 12 types share the rest — an extreme concentration that yields an entropy ratio of only 0.47. The long tail of rare categories (e.g., 'Cinder cone' and 'Maar' each appearing ≤2 times) may cause class-imbalance problems in any supervised modelling task.

Treatment: One-hot encode or target-encode with caution due to severe class imbalance; consider grouping rare types (frequency < 3) into an 'Other' category before modelling.

anthropic:default · confidence high
Out[28]:

saturn.columns["type"].stats

statvalue
n200
nulls0 (0.0%)
unique13
top_value Stratovolcano
top_rate 0.695
cardinality 13
entropy 1.74
entropy_ratio 0.4703
Fig 13.
Top values for type.
Show data table
Top values for type (13 unique shown, of 13 total).
valuecountshare
Stratovolcano13969.5%
Complex volcano2211.0%
Shield volcano126.0%
Caldera94.5%
Submarine volcano42.0%
Pyroclastic shield31.5%
Lava dome31.5%
Maar21.0%
Tuff cone21.0%
Cinder cone10.5%
Compound volcano10.5%
Pyroclastic cone10.5%
Subglacial volcano10.5%

vei numeric feature

This column is almost certainly the Volcanic Explosivity Index (VEI), a logarithmic scale rating volcanic eruption intensity. With only 7 unique integer values ranging from 0 to 6 and a median of 3, it behaves more like an ordinal category than a continuous numeric. Notably, 15 outliers (7.5% of rows) sit at the upper end of the scale — VEI 5–6 events are rare in real-world volcanology, so their presence is worth verifying. The IQR of 1.0 and tight Q1–Q3 band of 2–3 confirm most eruptions cluster at moderate intensity.

Treatment: Treat as ordinal; consider one-hot or ordinal encoding rather than raw numeric use in models.

anthropic:default · confidence high
Out[31]:

saturn.columns["vei"].stats

statvalue
n200
nulls0 (0.0%)
unique7
min 0
max 6
mean 2.565
median 3
std 1.068
q1 2
q3 3
iqr 1
skew 0.2144
kurtosis 0.8382
n_outliers 15
outlier_rate 0.075
zero_rate 0.035
alert: outliers7.5% rows beyond 1.5 IQR
Fig 14.
Distribution of vei. Vertical dash marks the median.
Show data table
Histogram bins for vei (median: 3.0).
bincount
0 – 0.42867
0.4286 – 0.85710
0.8571 – 1.28615
1.286 – 1.7140
1.714 – 2.14377
2.143 – 2.5710
2.571 – 30
3 – 3.42970
3.429 – 3.8570
3.857 – 4.28623
4.286 – 4.7140
4.714 – 5.1436
5.143 – 5.5710
5.571 – 62

year numeric feature

This column represents a calendar year, spanning 1900 to 1999 — exactly one century of data with no nulls. With 84 unique values across 200 rows, some years appear multiple times, suggesting records grouped by year rather than unique annual entries. The distribution is notably flat (kurtosis ≈ −1.17) and nearly symmetric (skew ≈ −0.21), with the bulk of records concentrated between 1928 and 1977 (IQR = 49.25 years), which is surprisingly wide and uniform for a year field.

Treatment: Use as an ordinal or numeric feature; consider binning into decades if cardinality reduction is needed.

anthropic:default · confidence high
Out[34]:

saturn.columns["year"].stats

statvalue
n200
nulls0 (0.0%)
unique84
min 1,900
max 1,999
mean 1952
median 1,955
std 28.79
q1 1,928
q3 1977
iqr 49.25
skew -0.2134
kurtosis -1.169
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 15.
Distribution of year. Vertical dash marks the median.
Show data table
Histogram bins for year (median: 1955.0).
bincount
1900 – 190716
1907 – 191414
1914 – 192112
1921 – 19289
1928 – 193512
1935 – 194210
1942 – 195014
1950 – 195715
1957 – 196415
1964 – 197117
1971 – 197816
1978 – 198521
1985 – 199213
1992 – 199916

last_eruption numeric feature

This column almost certainly records the year of a volcano's last known eruption, ranging from 1900 to 1999 with a mean of 1952.3 and median of 1955 — consistent with a dataset scoped to the 20th century. The distribution is notably platykurtic (kurtosis ≈ −1.17), meaning eruption years are spread fairly uniformly across the century rather than clustering tightly around any single period. With only 84 unique values across 200 rows, many volcanoes share the same recorded eruption year, which is unsurprising given that annual granularity naturally produces ties. No nulls, no outliers, and near-zero skew make this a clean numeric feature.

Treatment: Use as-is or engineer recency features (e.g., years since eruption relative to a reference year); no transformation needed given near-uniform distribution.

anthropic:default · confidence high
Out[37]:

saturn.columns["last_eruption"].stats

statvalue
n200
nulls0 (0.0%)
unique84
min 1,900
max 1,999
mean 1952
median 1,955
std 28.79
q1 1,928
q3 1977
iqr 49.25
skew -0.2134
kurtosis -1.169
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 16.
Distribution of last_eruption. Vertical dash marks the median.
Show data table
Histogram bins for last_eruption (median: 1955.0).
bincount
1900 – 190716
1907 – 191414
1914 – 192112
1921 – 19289
1928 – 193512
1935 – 194210
1942 – 195014
1950 – 195715
1957 – 196415
1964 – 197117
1971 – 197816
1978 – 198521
1985 – 199213
1992 – 199916

How to cite

click to copy

BibTeX
@misc{saturn-data-trove-global-volcanism-program-2026,
  author       = {Steuber, Luke},
  title        = {Saturn reading: data trove global volcanism program},
  year         ={2026},
  howpublished = {\url{https://dr.eamer.dev/saturn/view/data-trove-global-volcanism-program}},
  note         = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:default},
}
APA
Steuber, L. (2026). Saturn reading: data trove global volcanism program. Source: /home/coolhand/html/datavis/data_trove/data/quirky/volcanoes.json. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:default). Retrieved from https://dr.eamer.dev/saturn/view/data-trove-global-volcanism-program