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Use this Skill for linguistic typology with WALS data: cross-linguistic feature analysis, Greenbergian universals, and typological maps in Python.
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| name | typology-wals |
| description | Use this Skill for linguistic typology with WALS data: cross-linguistic feature analysis, Greenbergian universals, and typological maps in Python. |
| tags | ["linguistics","typology","wals","cross-linguistic","universals"] |
| version | 1.0.0 |
| authors | [{"name":"Rosetta Skills Contributors","github":"@xjtulyc"}] |
| license | MIT |
| platforms | ["claude-code","codex","gemini-cli","cursor"] |
| dependencies | {"python":["pandas>=2.0","numpy>=1.24","scipy>=1.11","matplotlib>=3.7","geopandas>=0.14"]} |
| last_updated | 2026-03-17 |
| status | stable |
One-line summary: Analyze cross-linguistic patterns using WALS (World Atlas of Language Structures): feature co-occurrence, typological implicational universals, and geographic distribution maps.
Trigger keywords: WALS, typology, Greenberg universals, implicational universal, word order, SOV SVO, cross-linguistic, linguistic geography, language features, feature co-occurrence, typological database, language families
WALS contains 192 typological features for ~2,600 languages, organized in chapters covering phonology, morphology, syntax, and lexicon. Available at wals.info and as downloadable CSV.
If a language has feature A, then it has feature B (Greenberg 1963). Statistical test: $P(B | A) \gg P(B | \neg A)$.
Quantified by:
Greenberg's Universal 1: "In declarative sentences with nominal subject and object, the dominant order is almost always one in which the subject precedes the object." (SOV ≈ 41%, SVO ≈ 35% of languages).
pip install pandas>=2.0 numpy>=1.24 scipy>=1.11 matplotlib>=3.7 geopandas>=0.14
# Download WALS data:
# wget https://github.com/cldf-datasets/wals/archive/refs/heads/main.zip
import pandas as pd
import numpy as np
# Download from: https://wals.info/download
()
()
()
()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import chi2_contingency, fisher_exact
# ------------------------------------------------------------------ #
# Simulate WALS data (substitute with real WALS CSV)
# Key features used: word order, morphology, phonology
# ------------------------------------------------------------------ #
np.random.seed(42)
n_langs = 400
# Language families (approximate real distribution)
families = np.random.choice(
['Indo-European', 'Niger-Congo', 'Austronesian', 'Sino-Tibetan',
'Afro-Asiatic', 'Nilo-Saharan', 'Dravidian', 'Uralic', 'Turkic', 'Other'],
n_langs,
p=[0.15, 0.20, 0.20, 0.10, 0.08, 0.05, 0.04, 0.03, 0.05, 0.10],
)
# Simulate WALS Feature 81A: Order of Subject, Object and Verb
# Real distribution: SOV 41%, SVO 35%, VSO 9%, VOS 2%, OVS 1%, free/other 12%
word_orders = np.random.choice(
['SOV', 'SVO', 'VSO', 'VOS', 'OVS', 'No dominant order'],
n_langs,
p=[0.41, 0.35, 0.09, 0.02, 0.01, 0.12],
)
# Feature 85A: Order of Adposition and NP (preposition vs. postposition)
# Correlated with word order (SOV → postpositional)
adposition = []
for wo in word_orders:
if wo == 'SOV':
adposition.append(np.random.choice(['Postpositions', 'Prepositions', 'Inpositions'], p=[0.75, 0.20, 0.05]))
elif wo == 'SVO':
adposition.append(np.random.choice(['Postpositions', 'Prepositions', 'Inpositions'], p=[0.25, 0.70, 0.05]))
else:
adposition.append(np.random.choice(['Postpositions', 'Prepositions', 'Inpositions'], p=[0.40, 0.50, 0.10]))
# Feature 13A: Tone (tonal vs. non-tonal)
tone = np.random.choice(['Simple tone system', 'Complex tone system', 'No tones'],
n_langs, p=[0.25, 0.20, 0.55])
# Geographic coordinates (rough approximation)
lat = np.random.uniform(-40, 65, n_langs)
lon = np.random.uniform(-150, 160, n_langs)
df_wals = pd.DataFrame({
'language_id': [f"L{i:04d}" for i in range(n_langs)],
'family': families,
'word_order': word_orders,
'adposition': adposition,
'tone': tone,
'latitude': lat,
'longitude': lon,
})
print("Simulated WALS dataset:")
print(f" {len(df_wals)} languages, {df_wals['family'].nunique()} families")
# ---- Feature frequencies --------------------------------------- #
fig, axes = plt.subplots(1, 3, figsize=(16, 5))
# Word order
wo_counts = df_wals['word_order'].value_counts()
colors = ['#e74c3c','#3498db','#2ecc71','#9b59b6','#f39c12','#95a5a6']
axes[0].bar(wo_counts.index, wo_counts.values, color=colors[:len(wo_counts)], edgecolor='black', linewidth=0.7)
axes[0].set_title("Word Order Distribution\n(WALS Feature 81A)")
axes[0].set_xlabel("Word Order"); axes[0].set_ylabel("Number of Languages")
axes[0].tick_params(axis='x', rotation=20); axes[0].grid(axis='y', alpha=0.3)
# Adposition
adp_counts = df_wals['adposition'].value_counts()
axes[1].pie(adp_counts.values, labels=adp_counts.index,
autopct='%1.1f%%', startangle=90, colors=colors[:3])
axes[1].set_title("Adposition Type\n(WALS Feature 85A)")
# Tone
tone_counts = df_wals['tone'].value_counts()
axes[2].bar(tone_counts.index, tone_counts.values, color=['#3498db','#e74c3c','#2ecc71'],
edgecolor='black', linewidth=0.7)
axes[2].set_title("Tone System\n(WALS Feature 13A)")
axes[2].set_xlabel("Tone"); axes[2].set_ylabel("Number of Languages")
axes[2].grid(axis='y', alpha=0.3)
plt.suptitle("Typological Feature Distributions (Simulated WALS)")
plt.tight_layout()
plt.savefig("wals_features.png", dpi=150)
plt.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import fisher_exact, chi2_contingency
# ------------------------------------------------------------------ #
# Test Greenberg's Universal: SOV predicts Postpositions
# (and vice versa)
# ------------------------------------------------------------------ #
def test_implicational_universal(df, feature_a, value_a, feature_b, value_b):
"""
Test: if language has feature A = value_a, does it tend to have feature_b = value_b?
Returns
-------
dict with contingency table, conditional probabilities, Yule's Q, Fisher p-value
"""
has_a = df[feature_a] == value_a
has_b = df[feature_b] == value_b
# 2×2 contingency table
a = (has_a & has_b).sum() # Both A and B
b = (has_a & ~has_b).sum() # A but not B
c = (~has_a & has_b).sum() # Not A but B
d = (~has_a & ~has_b).sum() # Neither
table = np.array([[a, b], [c, d]])
# Statistical test
odds_ratio, p_fisher = fisher_exact(table, alternative='greater')
chi2, p_chi2, _, _ = chi2_contingency(table)
# Yule's Q
Q = (a*d - b*c) / (a*d + b*c) if (a*d + b*c) > 0 else np.nan
# Conditional probability
P_b_given_a = a / (a + b) if (a + b) > 0 else np.nan
P_b_given_na = c / (c + d) if (c + d) > 0 else np.nan
return {
'A': f"{feature_a}={value_a}",
'B': f"{feature_b}={value_b}",
'n_AB': a, 'n_A_notB': b, 'n_notA_B': c, 'n_notA_notB': d,
'P(B|A)': round(P_b_given_a, 4),
'P(B|notA)': round(P_b_given_na, 4),
"Yule's Q": round(Q, 4),
'Fisher OR': round(odds_ratio, 4),
'Fisher p': round(p_fisher, 6),
'Significant': p_fisher < 0.05,
}
# ---- Test key universals --------------------------------------- #
universals_to_test = [
('word_order', 'SOV', 'adposition', 'Postpositions'),
('word_order', 'SVO', 'adposition', 'Prepositions'),
('word_order', 'VSO', 'adposition', 'Prepositions'),
]
results = [test_implicational_universal(df_wals, *u) for u in universals_to_test]
df_results = pd.DataFrame(results)
print("=== Implicational Universal Tests ===\n")
for _, row in df_results.iterrows():
sig = "✓ SIGNIFICANT" if row['Significant'] else "✗ not significant"
print(f"Universal: If {row['A']} → {row['B']}")
print(f" Contingency: n(A∩B)={row['n_AB']}, n(A∩¬B)={row['n_A_notB']}, "
f"n(¬A∩B)={row['n_notA_B']}, n(¬A∩¬B)={row['n_notA_notB']}")
print(f" P(B|A) = {row['P(B|A)']:.3f}, P(B|¬A) = {row['P(B|notA)']:.3f}")
print(f" Yule's Q = {row[\"Yule's Q\"]:.3f}, Fisher p = {row['Fisher p']:.4f} {sig}\n")
# ---- Visualization: conditional probability comparison --------- #
fig, ax = plt.subplots(figsize=(9, 5))
x = np.arange(len(df_results))
width = 0.35
bars1 = ax.bar(x - width/2, df_results["P(B|A)"], width, label='P(B|A)',
color='#3498db', edgecolor='black', linewidth=0.7)
bars2 = ax.bar(x + width/2, df_results["P(B|notA)"], width, label='P(B|¬A)',
color='#e74c3c', edgecolor='black', linewidth=0.7)
labels = [f"{r['A']}\n→ {r['B'].split('=')[1]}" for _, r in df_results.iterrows()]
ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=8)
ax.set_ylabel("Conditional probability")
ax.set_title("Greenbergian Implicational Universals\n(Simulated WALS data)")
ax.legend(); ax.grid(axis='y', alpha=0.3)
for bar, row in zip(bars1, df_results.itertuples()):
if row.Significant:
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, '**',
ha='center', va='bottom', fontsize=14, color='green')
plt.tight_layout()
plt.savefig("implicational_universals.png", dpi=150)
plt.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
# ------------------------------------------------------------------ #
# Map geographic distribution of word order types
# ------------------------------------------------------------------ #
# Try geopandas for proper world map; fallback to scatter plot
try:
import geopandas as gpd
from shapely.geometry import Point
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
fig, ax = plt.subplots(figsize=(15, 8))
world.plot(ax=ax, color='lightgray', edgecolor='white', linewidth=0.5)
order_colors = {
'SOV': '#e74c3c', 'SVO': '#3498db', 'VSO': '#2ecc71',
'VOS': '#9b59b6', 'OVS': '#f39c12', 'No dominant order': '#95a5a6',
}
for wo, color in order_colors.items():
subset = df_wals[df_wals['word_order'] == wo]
ax.scatter(subset['longitude'], subset['latitude'],
c=color, s=25, alpha=0.7, label=f"{wo} (n={len(subset)})",
edgecolors='black', linewidths=0.2, zorder=5)
ax.set_xlim(-170, 175); ax.set_ylim(-55, 80)
ax.set_xlabel("Longitude"); ax.set_ylabel("Latitude")
ax.set_title("Geographic Distribution of Word Order Types (Simulated WALS)")
ax.legend(loc='lower left', fontsize=9, ncol=2, framealpha=0.9)
plt.tight_layout()
plt.savefig("wals_map_word_order.png", dpi=150)
plt.show()
except Exception as e:
print(f"geopandas unavailable ({e}); using scatter plot fallback")
fig, ax = plt.subplots(figsize=(13, 7))
order_colors = {
'SOV': '#e74c3c', 'SVO': '#3498db', 'VSO': '#2ecc71',
'VOS': '#9b59b6', 'OVS': '#f39c12', 'No dominant order': '#95a5a6',
}
for wo, color in order_colors.items():
subset = df_wals[df_wals['word_order'] == wo]
ax.scatter(subset['longitude'], subset['latitude'],
c=color, s=20, alpha=0.8,
label=f"{wo} (n={len(subset)})",
edgecolors='black', linewidths=0.2)
ax.set_xlabel("Longitude"); ax.set_ylabel("Latitude")
ax.set_title("Word Order Geographic Distribution (Simulated WALS)")
ax.legend(fontsize=9); ax.grid(alpha=0.3)
plt.tight_layout()
plt.savefig("wals_scatter_word_order.png", dpi=150)
plt.show()
# ---- Cross-tabulation by family -------------------------------- #
cross_tab = pd.crosstab(df_wals['family'], df_wals['word_order'], normalize='index') * 100
print("\nWord order distribution by language family (%):")
print(cross_tab.round(1).to_string())
import numpy as np
from scipy.spatial.distance import cdist
# ------------------------------------------------------------------ #
# Test whether typological feature clusters geographically
# (geographic signal = Moran's I)
# ------------------------------------------------------------------ #
def morans_i(values, coords, k_neighbors=10):
"""
Compute Moran's I spatial autocorrelation.
Parameters
----------
values : binary array — feature presence (1/0)
coords : (n,2) array — lat/lon
k_neighbors : int — nearest neighbors for spatial weights
"""
n = len(values)
y = values.astype(float) - values.mean()
# Build k-NN spatial weights matrix
dist = cdist(coords, coords)
W = np.zeros((n, n))
for i in range(n):
neighbors = np.argsort(dist[i])[1:k_neighbors+1]
W[i, neighbors] = 1
# Normalize weights
W = W / W.sum(axis=1, keepdims=True)
# Moran's I
num = n * np.sum(W * np.outer(y, y))
denom = W.sum() * np.sum(y**2)
return num / denom if denom > 0 else 0.0
coords = df_wals[['latitude', 'longitude']].values
is_sov = (df_wals['word_order'] == 'SOV').astype(int).values
is_tonal = (df_wals['tone'] != 'No tones').astype(int).values
I_sov = morans_i(is_sov, coords)
I_tonal = morans_i(is_tonal, coords)
print(f"Moran's I — SOV: {I_sov:.4f} (>0 = geographic clustering)")
print(f"Moran's I — Tonal: {I_tonal:.4f}")
import pandas as pd
# After downloading WALS CLDF data:
values = pd.read_csv("wals/cldf/values.csv")
languages = pd.read_csv("wals/cldf/languages.csv")
parameters = pd.read_csv("wals/cldf/parameters.csv")
# Pivot: one row per language, columns = features
wals_wide = values.pivot_table(
index='Language_ID', columns='Parameter_ID', values='Value', aggfunc='first'
)
# Join with language metadata
wals_full = languages.set_index('ID').join(wals_wide)
print(f"WALS full matrix: {wals_full.shape}") # (~2600 languages × 192+ features)
Cause: Most WALS features are only coded for a subset of languages.
Fix: Filter to languages with sufficient coverage:
# Keep only languages with ≥20 features coded
coverage = (~wals_wide.isna()).sum(axis=1)
wals_filtered = wals_wide[coverage >= 20]
print(f"Languages with ≥20 features: {len(wals_filtered)}")
| Package | Tested versions | Notes |
|---|---|---|
| geopandas | 0.14 | Required for world map; fallback to scatter |
| pandas | 2.0, 2.1 | pivot_table for CLDF format |
| scipy | 1.11, 1.12 | fisher_exact stable |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
features = ['word_order', 'adposition', 'tone']
pivot = pd.get_dummies(df_wals[features])
corr = pivot.corr()
fig, ax = plt.subplots(figsize=(10, 8))
im = ax.imshow(corr, cmap='RdBu_r', vmin=-1, vmax=1)
plt.colorbar(im, ax=ax)
ax.set_xticks(range(len(corr.columns))); ax.set_xticklabels(corr.columns, rotation=45, ha='right', fontsize=7)
ax.set_yticks(range(len(corr.columns))); ax.set_yticklabels(corr.columns, fontsize=7)
ax.set_title("Feature Co-occurrence Correlation Matrix (WALS)")
plt.tight_layout(); plt.savefig("feature_correlation.png", dpi=150); plt.show()
import pandas as pd
import numpy as np
family_summary = (df_wals.groupby('family')
.agg(
n_langs=('language_id','count'),
pct_SOV=('word_order', lambda x: (x=='SOV').mean()*100),
pct_SVO=('word_order', lambda x: (x=='SVO').mean()*100),
pct_tonal=('tone', lambda x: (x!='No tones').mean()*100),
pct_postpos=('adposition', lambda x: (x=='Postpositions').mean()*100),
)
.query("n_langs >= 10")
.sort_values('pct_SOV', ascending=False)
)
print("Typological profile by language family (n≥10):")
print(family_summary.round(1).to_string())
Last updated: 2026-03-17 | Maintainer: @xjtulyc Issues: GitHub Issues