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data trove usgs significant earthquakes

saturn notebook · generated 2026-06-22 Report Notebook

Overview

Source: /home/coolhand/html/datavis/data_trove/data/wild/usgs_significant_earthquakes.json

Saturn profiled 3,742 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/html/datavis/data_trove/data/wild/usgs_significant_earthquakes.json",
    "--findings", "data-trove-usgs-significant-earthquakes.json",
    "--llm", "anthropic:default",
])

Summary confidence: high

This dataset contains 3,742 records of significant earthquakes catalogued by the USGS, each describing a seismic event with location, magnitude, depth, and type. The vast majority (99.9%) are classified as earthquakes, with just 2 explosions and 1 landslide, so event type is not a useful differentiator. Two things stand out for closer inspection: first, depth_km is heavily right-skewed (median 10 km, mean 23.7 km, max 248.7 km) with 314 outliers, suggesting a small but important subset of unusually deep earthquakes worth isolating. Second, geographic concentration is striking — Alaska dominates the place names (appearing in roughly 1,991 records) and 'off the coast of Oregon' is the single most repeated location (151 times), pointing to a strong Pacific Northwest and Alaskan bias in this 'significant' events catalog. Magnitude ranges from 4.5 to 8.2 with a median of 4.8 and a long upper tail, meaning truly destructive events are rare outliers worth flagging.

citing: depth_km.stats.median · depth_km.stats.mean · depth_km.stats.max · depth_km.n_outliers · depth_km.stats.skew · magnitude.stats.median · magnitude.stats.max · magnitude.stats.min · magnitude.n_outliers · earthquake_type.top_values · place.top_values · name.top_words · row_count

Out[4]:

saturn.schema() · 11 columns

column kind n null% unique alerts
latitude numeric 3,742 0.0% 3,627
longitude numeric 3,742 0.0% 3,668
name text 3,742 0.0% 3,002 multilingual
description text 3,742 0.0% 3,591 near_unique
category categorical 3,742 0.0% 1 imbalance
date text 3,742 0.0% 3,741 near_unique one_word allcaps short_text
country unknown 3,742 0.0% — skipped
magnitude numeric 3,742 0.0% 123
depth_km numeric 3,742 0.0% 1,505 high_skew outliers
place text 3,742 0.0% 3,002 multilingual
earthquake_type categorical 3,742 0.0% 3 imbalance
Fig 1.
magnitude · Look for the long right tail — most events cluster between 4.5 and 5.1, but a small number exceed 7.0 and represent the truly destructive quakes.
Show data table
bincount
4.5 – 4.593752
4.593 – 4.685601
4.685 – 4.777445
4.777 – 4.87340
4.87 – 4.963286
4.963 – 5.055254
5.055 – 5.147218
5.147 – 5.24177
5.24 – 5.332137
5.332 – 5.425104
5.425 – 5.51885
5.518 – 5.6166
5.61 – 5.70253
5.702 – 5.7953
5.795 – 5.88738
5.887 – 5.9846
5.98 – 6.07225
6.072 – 6.16517
6.165 – 6.25716
6.257 – 6.3511
6.35 – 6.44215
6.442 – 6.53511
6.535 – 6.62710
6.627 – 6.724
6.72 – 6.8126
6.812 – 6.9055
6.905 – 6.9970
6.997 – 7.093
7.09 – 7.1823
7.182 – 7.2753
7.275 – 7.3671
7.367 – 7.460
7.46 – 7.5521
7.552 – 7.6451
7.645 – 7.7370
7.737 – 7.832
7.83 – 7.9222
7.922 – 8.0150
8.015 – 8.1070
8.107 – 8.21
Fig 2.
depth_km · The distribution is heavily skewed with a median of 10 km, but 314 outliers extend to 248.7 km — deep-focus events that behave very differently from shallow ones.
Show data table
bincount
-2.261 – 4.013219
4.013 – 10.291730
10.29 – 16.56370
16.56 – 22.84258
22.84 – 29.11230
29.11 – 35.38250
35.38 – 41.66167
41.66 – 47.93129
47.93 – 54.2156
54.21 – 60.4831
60.48 – 66.7543
66.75 – 73.0327
73.03 – 79.319
79.3 – 85.5829
85.58 – 91.8521
91.85 – 98.1219
98.12 – 104.424
104.4 – 110.712
110.7 – 116.99
116.9 – 123.214
123.2 – 129.513
129.5 – 135.819
135.8 – 14214
142 – 148.35
148.3 – 154.66
154.6 – 160.90
160.9 – 167.17
167.1 – 173.44
173.4 – 179.70
179.7 – 1861
186 – 192.25
192.2 – 198.51
198.5 – 204.83
204.8 – 211.12
211.1 – 217.33
217.3 – 223.61
223.6 – 229.90
229.9 – 236.20
236.2 – 242.40
242.4 – 248.71
Fig 3.
place · Alaska and the Oregon coast dominate locations, revealing a strong geographic concentration in the Pacific Northwest and Aleutian arc.
Show data table
charscount
4 – 51
5 – 70
7 – 81
8 – 100
10 – 110
11 – 120
12 – 142
14 – 1511
15 – 1640
16 – 181
18 – 1920
19 – 2019
20 – 228
22 – 23219
23 – 2560
25 – 26122
26 – 27543
27 – 29499
29 – 30823
30 – 32325
32 – 33362
33 – 34378
34 – 36105
36 – 3740
37 – 3837
38 – 4025
40 – 4134
41 – 423
42 – 440
44 – 4515
45 – 4722
47 – 4814
48 – 493
49 – 511
51 – 522
52 – 545
54 – 550
55 – 560
56 – 580
58 – 592
Fig 4.
earthquake_type · Nearly all events (99.9%) are earthquakes; the two explosions and one landslide are negligible but worth confirming as data anomalies.
Show data table
valuecountshare
earthquake373999.9%
explosion20.1%
landslide10.0%
Fig 5.
latitude · Latitude distribution confirms geographic clustering in the 40–60° N range, consistent with Alaska and Pacific Northwest dominance.
Show data table
bincount
20.02 – 21.2635
21.26 – 22.5128
22.51 – 23.7542
23.75 – 2588
25 – 26.2491
26.24 – 27.4935
27.49 – 28.7343
28.73 – 29.9839
29.98 – 31.2252
31.22 – 32.4683
32.46 – 33.7152
33.71 – 34.9526
34.95 – 36.267
36.2 – 37.4443
37.44 – 38.6954
38.69 – 39.9322
39.93 – 41.18131
41.18 – 42.4252
42.42 – 43.6696
43.66 – 44.91177
44.91 – 46.154
46.15 – 47.47
47.4 – 48.6434
48.64 – 49.89117
49.89 – 51.13151
51.13 – 52.38288
52.38 – 53.62423
53.62 – 54.86349
54.86 – 56.11223
56.11 – 57.35201
57.35 – 58.675
58.6 – 59.84104
59.84 – 61.09116
61.09 – 62.33117
62.33 – 63.58120
63.58 – 64.8233
64.82 – 66.0635
66.06 – 67.3125
67.31 – 68.5529
68.55 – 69.835
Fig 6.
Per-column null rate across the corpus. Columns are ordered by input position.
Show data table
columnkindnull %
latitudenumeric0.0%
longitudenumeric0.0%
nametext0.0%
descriptiontext0.0%
categorycategorical0.0%
datetext0.0%
countryunknown0.0%
magnitudenumeric0.0%
depth_kmnumeric0.0%
placetext0.0%
earthquake_typecategorical0.0%
Fig 7.
Language mix across all text columns (per-string detection, sampled).
Show data table
langcountshare
en743899.4%
es320.4%
de60.1%
ja20.0%
ceb20.0%
Fig 8.
Pearson correlation across numeric columns (sampled, bounded).
Show data table
latitudelongitudemagnitudedepth_km
latitude+1.00-0.72-0.12+0.31
longitude-0.72+1.00+0.07-0.37
magnitude-0.12+0.07+1.00-0.03
depth_km+0.31-0.37-0.03+1.00

latitude numeric feature

This column contains geographic latitude coordinates, ranging from 20.02° (subtropical) to 69.7975° (Arctic), indicating a dataset spanning locations across the Northern Hemisphere, likely Europe and parts of North America or Asia. The median of 52.4° and mean of 48.5° suggest a concentration of records around central/northern Europe. Near-zero skew (−0.76) and platykurtic distribution (kurtosis −0.29) indicate a relatively flat, spread-out distribution with no extreme clustering. High cardinality (3,627 unique values out of 3,742 rows) confirms these are precise geospatial coordinates rather than bucketed regions.

Treatment: Use as-is or pair with longitude for spatial modelling; consider binning into geographic zones if used as a categorical feature.

anthropic:default · confidence high
Out[14]:

saturn.columns["latitude"].stats

statvalue
n3,742
nulls0 (0.0%)
unique3,627
min 20.02
max 69.8
mean 48.53
median 52.4
std 11.58
q1 41.34
q3 55.9
iqr 14.56
skew -0.7591
kurtosis -0.2887
n_outliers 0
outlier_rate 0
zero_rate 0
Fig 9.
Distribution of latitude. Vertical dash marks the median.
Show data table
bincount
20.02 – 21.2635
21.26 – 22.5128
22.51 – 23.7542
23.75 – 2588
25 – 26.2491
26.24 – 27.4935
27.49 – 28.7343
28.73 – 29.9839
29.98 – 31.2252
31.22 – 32.4683
32.46 – 33.7152
33.71 – 34.9526
34.95 – 36.267
36.2 – 37.4443
37.44 – 38.6954
38.69 – 39.9322
39.93 – 41.18131
41.18 – 42.4252
42.42 – 43.6696
43.66 – 44.91177
44.91 – 46.154
46.15 – 47.47
47.4 – 48.6434
48.64 – 49.89117
49.89 – 51.13151
51.13 – 52.38288
52.38 – 53.62423
53.62 – 54.86349
54.86 – 56.11223
56.11 – 57.35201
57.35 – 58.675
58.6 – 59.84104
59.84 – 61.09116
61.09 – 62.33117
62.33 – 63.58120
63.58 – 64.8233
64.82 – 66.0635
66.06 – 67.3125
67.31 – 68.5529
68.55 – 69.835

longitude numeric feature

This column contains geographic longitude values, all negative, indicating locations exclusively in the Western Hemisphere. The range spans from approximately -170° (near the International Date Line, consistent with Alaska or Pacific islands) to -65° (eastern US/Caribbean region), with a mean near -140° and median near -144°, suggesting a heavy concentration of observations in Alaska or the North Pacific. The distribution is mildly right-skewed (skew 0.45) with near-platykurtic shape (kurtosis -0.43), implying a broad but relatively uniform spread across this western band rather than a tight geographic cluster. Only 26 outliers (0.69%) are flagged, and with 3,668 unique values out of 3,742 rows, coordinates appear precise and largely non-duplicated.

Treatment: Pair with latitude for spatial analysis; consider geographic binning or projection into a coordinate reference system before modelling.

anthropic:default · confidence high
Out[17]:

saturn.columns["longitude"].stats

statvalue
n3,742
nulls0 (0.0%)
unique3,668
min -170
max -65.04
mean -140.1
median -144.2
std 21.81
q1 -159.9
q3 -125.1
iqr 34.84
skew 0.4489
kurtosis -0.4302
n_outliers 26
outlier_rate 0.006948
zero_rate 0
Fig 10.
Distribution of longitude. Vertical dash marks the median.
Show data table
bincount
-170 – -167.4368
-167.4 – -164.7161
-164.7 – -162.1243
-162.1 – -159.5241
-159.5 – -156.9113
-156.9 – -154.3129
-154.3 – -151.6241
-151.6 – -149220
-149 – -146.477
-146.4 – -143.883
-143.8 – -141.129
-141.1 – -138.548
-138.5 – -135.941
-135.9 – -133.334
-133.3 – -130.6129
-130.6 – -128428
-128 – -125.4200
-125.4 – -122.889
-122.8 – -120.144
-120.1 – -117.5130
-117.5 – -114.9149
-114.9 – -112.3113
-112.3 – -109.6163
-109.6 – -107134
-107 – -104.433
-104.4 – -101.812
-101.8 – -99.1510
-99.15 – -96.5320
-96.53 – -93.94
-93.9 – -91.281
-91.28 – -88.652
-88.65 – -86.036
-86.03 – -83.411
-83.41 – -80.782
-80.78 – -78.163
-78.16 – -75.537
-75.53 – -72.918
-72.91 – -70.297
-70.29 – -67.665
-67.66 – -65.0414

name text label

This column contains geographic location descriptions for seismic events, formatted as place names relative to known landmarks (e.g., '104 km SSW of Nikolski, Alaska'). The dominant region is Alaska, appearing in 1,991 of ~3,742 entries, with Canada and Mexico also frequent. Notably, 740 duplicate values (19.8% duplicate rate) exist despite only 3,002 unique values out of 3,742 rows, driven by repeated location labels like 'off the coast of Oregon' (151 occurrences), suggesting many events cluster in the same geographic zones. A multilingual alert fires, but non-English entries are negligible (21 rows across de/es/ja/ceb vs. 3,719 en), so this is not a practical concern.

Treatment: Use as a categorical geographic label; consider parsing direction/distance/place subcomponents for structured feature engineering, or embed as text for ML.

anthropic:default · confidence high
Out[20]:

saturn.columns["name"].stats

statvalue
n3,742
nulls0 (0.0%)
unique3,002
len_min 4
len_max 59
len_mean 29.47
len_median 29
len_p95 36
word_mean 6.293
word_median 6
n_empty 0
n_duplicates 740
duplicate_rate 0.1978
vocab_size 1,036
readability_flesch_mean 69.91
emoji_rate 0
url_rate 0
one_word_rate 0.0005345
allcaps_rate 0
boilerplate_rate 0
alert: multilingual6 languages detected in sample
Fig 11.
Character-length distribution for name.
Show data table
charscount
4 – 51
5 – 70
7 – 81
8 – 100
10 – 110
11 – 120
12 – 142
14 – 1511
15 – 1640
16 – 181
18 – 1920
19 – 2019
20 – 228
22 – 23219
23 – 2560
25 – 26122
26 – 27543
27 – 29499
29 – 30823
30 – 32325
32 – 33362
33 – 34378
34 – 36105
36 – 3740
37 – 3837
38 – 4025
40 – 4134
41 – 423
42 – 440
44 – 4515
45 – 4722
47 – 4814
48 – 493
49 – 511
51 – 522
52 – 545
54 – 550
55 – 560
56 – 580
58 – 592

description text label

This column contains structured natural-language descriptions of seismic events, likely auto-generated strings of the form 'Earthquake magnitude X - depth: Y km [location]'. The top words confirm every row references 'earthquake', 'magnitude', 'depth:', and 'km', making this a templated field rather than free prose (mean length 71.7 chars, mean 12.3 words, Flesch readability 63.2). Surprising signals: 151 duplicate descriptions (4.0% duplicate rate) despite 3,742 rows, meaning distinct earthquake events share identical text — likely collisions on rounded magnitude/depth/location values. The vocabulary of only 2,674 unique words across 3,742 rows further confirms the highly templated, low-diversity nature of the text.

Treatment: Parse structured fields (magnitude, depth, location) via regex rather than embedding; flag duplicate descriptions that may map to distinct events.

anthropic:default · confidence high
Out[23]:

saturn.columns["description"].stats

statvalue
n3,742
nulls0 (0.0%)
unique3,591
len_min 45
len_max 100
len_mean 71.71
len_median 72
len_p95 79
word_mean 12.29
word_median 12
n_empty 0
n_duplicates 151
duplicate_rate 0.04035
vocab_size 2,674
readability_flesch_mean 63.23
emoji_rate 0
url_rate 0
one_word_rate 0
allcaps_rate 0
boilerplate_rate 0
alert: near_unique96.0% of rows are unique strings
Fig 12.
Character-length distribution for description.
Show data table
charscount
45 – 461
46 – 481
48 – 490
49 – 500
50 – 520
52 – 530
53 – 554
55 – 564
56 – 5719
57 – 5915
59 – 6020
60 – 629
62 – 6317
63 – 64158
64 – 6645
66 – 6773
67 – 68380
68 – 70350
70 – 71726
71 – 72389
72 – 74367
74 – 75605
75 – 77204
77 – 7891
78 – 7990
79 – 8121
81 – 8233
82 – 848
84 – 8525
85 – 8631
86 – 8824
88 – 8910
89 – 907
90 – 921
92 – 933
93 – 945
94 – 961
96 – 972
97 – 991
99 – 1002

category categorical metadata

This column is a dataset-level category tag indicating the source or filter applied to all 3,742 rows — every single record carries the value 'significant_earthquakes' with a top_rate of 1.0 and entropy of 0.0. It is a constant column with zero discriminative power. The imbalance alert confirms it: cardinality is 1, meaning this field adds no within-dataset information whatsoever.

Treatment: Drop before modelling — zero-variance constant column provides no predictive signal.

anthropic:default · confidence high
Out[26]:

saturn.columns["category"].stats

statvalue
n3,742
nulls0 (0.0%)
unique1
top_value significant_earthquakes
top_rate 1
cardinality 1
entropy 0
entropy_ratio 0
alert: imbalancetop value is 100.0% of rows
Fig 13.
Top values for category.
Show data table
valuecountshare
significant_earthquakes3742100.0%

date text timestamp

This column is named 'date' but contains values that are clearly not valid calendar dates — the year component is a 13-digit Unix timestamp in milliseconds (e.g., '1614452365296') appended with '-01-01', indicating a malformed or incorrectly formatted datetime field. With 3,741 unique values out of 3,742 rows and near-zero duplicates (only 1 duplicate exists), this column functions almost as a row identifier. The one duplicate ('1614452365296-01-01' appearing twice) is the only anomaly in an otherwise fully unique set.

Treatment: Parse the numeric prefix as a Unix millisecond timestamp (divide by 1000 for seconds), discard the '-01-01' suffix artifact, and convert to a proper datetime before any temporal analysis.

anthropic:default · confidence high
Out[29]:

saturn.columns["date"].stats

statvalue
n3,742
nulls0 (0.0%)
unique3,741
len_min 18
len_max 19
len_mean 18.95
len_median 19
len_p95 19
word_mean 1
word_median 1
n_empty 0
n_duplicates 1
duplicate_rate 0.0002672
vocab_size 3,741
readability_flesch_mean 121.2
emoji_rate 0
url_rate 0
one_word_rate 1
allcaps_rate 1
boilerplate_rate 0
alert: near_unique100.0% of rows are unique strings
alert: one_word100.0% rows are a single word
alert: allcaps100.0% rows are all-caps
alert: short_text95th-percentile length under 20 chars
Fig 14.
Character-length distribution for date.
Show data table
charscount
18 – 18180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 180
18 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 190
19 – 193562

country unknown label

This column contains country values across 3,742 rows with no nulls. The profiler skipped detailed analysis ('skipped' alert), so cardinality, value distribution, and encoding format (ISO codes vs. full names) are unknown. The absence of any stats prevents assessment of skew, dominant categories, or anomalies.

Treatment: Re-profile to obtain value counts and cardinality; standardize to ISO 3166-1 alpha-2/3 codes before encoding or joining.

anthropic:default · confidence low
Out[32]:

saturn.columns["country"].stats

statvalue
n3,742
nulls0 (0.0%)
unique—
alert: skippedno profiler for kind=unknown

magnitude numeric feature

This column represents seismic event magnitude, almost certainly on the Richter or moment magnitude scale, given the range of 4.5–8.2 and mean of ~4.92. Despite 3,742 records, only 123 unique values appear, indicating the data is reported at one decimal place of precision. The distribution is strongly right-skewed (skew=1.97) with high kurtosis (5.58), meaning the vast majority of events cluster in the 4.6–5.1 IQR while 184 outliers (≈4.9% of records) pull the tail toward the extreme 8.2 maximum — a pattern entirely consistent with earthquake frequency-magnitude relationships (Gutenberg-Richter law).

Treatment: Log-transform or use as-is for seismic models; treat outlier events (>6.5 approx) as a separate high-impact stratum given the severe right skew.

anthropic:default · confidence high
Out[34]:

saturn.columns["magnitude"].stats

statvalue
n3,742
nulls0 (0.0%)
unique123
min 4.5
max 8.2
mean 4.917
median 4.8
std 0.462
q1 4.6
q3 5.1
iqr 0.5
skew 1.97
kurtosis 5.583
n_outliers 184
outlier_rate 0.04917
zero_rate 0
Fig 15.
Distribution of magnitude. Vertical dash marks the median.
Show data table
bincount
4.5 – 4.593752
4.593 – 4.685601
4.685 – 4.777445
4.777 – 4.87340
4.87 – 4.963286
4.963 – 5.055254
5.055 – 5.147218
5.147 – 5.24177
5.24 – 5.332137
5.332 – 5.425104
5.425 – 5.51885
5.518 – 5.6166
5.61 – 5.70253
5.702 – 5.7953
5.795 – 5.88738
5.887 – 5.9846
5.98 – 6.07225
6.072 – 6.16517
6.165 – 6.25716
6.257 – 6.3511
6.35 – 6.44215
6.442 – 6.53511
6.535 – 6.62710
6.627 – 6.724
6.72 – 6.8126
6.812 – 6.9055
6.905 – 6.9970
6.997 – 7.093
7.09 – 7.1823
7.182 – 7.2753
7.275 – 7.3671
7.367 – 7.460
7.46 – 7.5521
7.552 – 7.6451
7.645 – 7.7370
7.737 – 7.832
7.83 – 7.9222
7.922 – 8.0150
8.015 – 8.1070
8.107 – 8.21

depth_km numeric feature

This column represents seismic event or borehole depth in kilometres, almost certainly from an earthquake catalogue or geological survey dataset. The distribution is severely right-skewed (skew 3.07, kurtosis 11.61): the median is just 10.0 km—a canonical default depth assigned when depth is poorly constrained—with Q1 also exactly 10.0 km, suggesting a large fraction of records are pinned to that default value. The tail extends to 248.7 km with 314 outliers (8.4%), and a small negative minimum (−2.261 km) indicates above-sea-level or instrument-offset events that may need review.

Treatment: Investigate Q1=median=10.0 km pile-up for catalog-assigned default depths; log-transform or use quantile binning before modelling, and flag/separate records with depth < 0 km.

anthropic:default · confidence high
Out[37]:

saturn.columns["depth_km"].stats

statvalue
n3,742
nulls0 (0.0%)
unique1,505
min -2.261
max 248.7
mean 23.71
median 10
std 28.79
q1 10
q3 29.1
iqr 19.1
skew 3.072
kurtosis 11.61
n_outliers 314
outlier_rate 0.08391
zero_rate 0.002672
alert: high_skewskew=+3.07
alert: outliers8.4% rows beyond 1.5 IQR
Fig 16.
Distribution of depth_km. Vertical dash marks the median.
Show data table
bincount
-2.261 – 4.013219
4.013 – 10.291730
10.29 – 16.56370
16.56 – 22.84258
22.84 – 29.11230
29.11 – 35.38250
35.38 – 41.66167
41.66 – 47.93129
47.93 – 54.2156
54.21 – 60.4831
60.48 – 66.7543
66.75 – 73.0327
73.03 – 79.319
79.3 – 85.5829
85.58 – 91.8521
91.85 – 98.1219
98.12 – 104.424
104.4 – 110.712
110.7 – 116.99
116.9 – 123.214
123.2 – 129.513
129.5 – 135.819
135.8 – 14214
142 – 148.35
148.3 – 154.66
154.6 – 160.90
160.9 – 167.17
167.1 – 173.44
173.4 – 179.70
179.7 – 1861
186 – 192.25
192.2 – 198.51
198.5 – 204.83
204.8 – 211.12
211.1 – 217.33
217.3 – 223.61
223.6 – 229.90
229.9 – 236.20
236.2 – 242.40
242.4 – 248.71

place text label

This column contains human-readable geographic location descriptions for seismic or oceanographic events, predominantly structured as compass-bearing distance strings (e.g., '104 km SSW of Nikolski, Alaska'). Nearly all 3,742 entries are in English (3,719), with negligible Spanish, German, Cebuano, and Japanese entries flagging a multilingual alert. The dominant location is 'off the coast of Oregon' appearing 151 times — far more than any other value — and 740 duplicate entries (19.8% duplicate rate) suggest event clustering at recurring geographic hotspots. The vocabulary of directional abbreviations (SSW, SSE, SE, W) and 'km' appearing 3,220 times confirms a standardized but free-text geocoding convention.

Treatment: Parse structured distance-bearing substrings (e.g., regex on 'km [NSEW]+') to extract numeric distance and cardinal direction as features; consider geocoding to lat/lon for spatial modelling.

anthropic:default · confidence high
Out[40]:

saturn.columns["place"].stats

statvalue
n3,742
nulls0 (0.0%)
unique3,002
len_min 4
len_max 59
len_mean 29.47
len_median 29
len_p95 36
word_mean 6.293
word_median 6
n_empty 0
n_duplicates 740
duplicate_rate 0.1978
vocab_size 1,036
readability_flesch_mean 69.91
emoji_rate 0
url_rate 0
one_word_rate 0.0005345
allcaps_rate 0
boilerplate_rate 0
alert: multilingual6 languages detected in sample
Fig 17.
Character-length distribution for place.
Show data table
charscount
4 – 51
5 – 70
7 – 81
8 – 100
10 – 110
11 – 120
12 – 142
14 – 1511
15 – 1640
16 – 181
18 – 1920
19 – 2019
20 – 228
22 – 23219
23 – 2560
25 – 26122
26 – 27543
27 – 29499
29 – 30823
30 – 32325
32 – 33362
33 – 34378
34 – 36105
36 – 3740
37 – 3837
38 – 4025
40 – 4134
41 – 423
42 – 440
44 – 4515
45 – 4722
47 – 4814
48 – 493
49 – 511
51 – 522
52 – 545
54 – 550
55 – 560
56 – 580
58 – 592

earthquake_type categorical label

This column classifies seismic event types, with three possible values: 'earthquake', 'explosion', and 'landslide'. It is severely imbalanced: 'earthquake' accounts for 3,739 of 3,742 records (99.92%), leaving only 2 explosions and 1 landslide. The near-zero entropy (0.0101) and entropy_ratio (0.0064) confirm the column carries almost no information variance, which will make it useless as a predictive feature without special handling.

Treatment: Drop or use only as a stratification/filter variable; minority classes (n=2, n=1) are too rare for meaningful multi-class modelling without heavy oversampling.

anthropic:default · confidence high
Out[43]:

saturn.columns["earthquake_type"].stats

statvalue
n3,742
nulls0 (0.0%)
unique3
top_value earthquake
top_rate 0.9992
cardinality 3
entropy 0.01014
entropy_ratio 0.006396
alert: imbalancetop value is 99.9% of rows
Fig 18.
Top values for earthquake_type.
Show data table
valuecountshare
earthquake373999.9%
explosion20.1%
landslide10.0%

How to cite

click to copy

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