typology-wals

To test implicational universals with WALS data, load structured typological tables into Python and compute contingency-based metrics like conditional probability, Fisher's exact test, and Yule's Q to quantify feature co-occurrence across languages.

Quantifying linguistic feature co-occurrence in typology involves computing contingency-based metrics such as conditional probability, Fisher's exact test, and Yule's Q to evaluate the strength and significance of Greenbergian-style implicational universals.

You can map the geographic distribution of word order typology features using Python by generating maps with GeoPandas, or utilizing Matplotlib scatter fallbacks, to visualize where specific feature values like SOV clusters appear on a world map.

This approach works with WALS data to determine if SOV predicts postpositions by constructing contingency tables from the typological dataset and applying Fisher's exact test to evaluate the statistical significance of the feature association.

The best way to visualize cross-linguistic typological patterns from WALS is using Python with Pandas for data handling, GeoPandas for geographic distribution maps, and Matplotlib to generate scatter plots that illustrate feature co-occurrence clusters.

You do not need GeoPandas to generate typological feature distribution maps, as the analysis supports a scatter plot fallback using Matplotlib when GeoPandas is unavailable, ensuring geographic visualization of feature clusters remains possible.