Temperature and Language Sonority

Results

• sonorities.csv: Mean sonority index (MSI) of each filtered doculect. We adapted 5 methods to calculate MSI from ASJP codes:
  • index0: Parker’s scale, from Sonority in The Blackwell Companion to Phonology
  • index1: Fought’s scale, from Sonority and climate in a world sample of languages: Findings and prospects
  • index2: Clements’ scale, from The role of the sonority cycle in core syllabification in Papers in Laboratory Phonology
  • index3: Sonorant index (here obstruent = 1; sonorant = 2)
  • index4: Vowel index (here consonant = 1; semivowel = 2; vowel = 3)
• phones.csv: Extracted phones from filtered doculects (This was not used further in the research)
• temperatures.csv: Monthly temperature (1982–2022) for each filtered doculect
• temperature_global.csv (contained in temperature_global.zip): Global 40-year mean monthly temperature.
• data.csv: MSIs and temperature data for each filtered doculect:
  • Index0 to Index4: MSIs in 5 methods
  • T: Mean annual temperature
  • T_max: Max of 40-year mean monthly temperatures
  • T_min: Min of 40-year mean monthly temperatures
  • T_sd: Standard deviation of monthly temperatures over 40 years
  • T_diff: Mean annual range of temperature
  • Index0_trans, etc.: Transformated above data
• data_family.csv: MSIs and temperature data for each language family classified by WALS
• data_macroarea.csv: MSIs and temperature data for each macroarea (North America, South America, Eurasia, Africa, Greater New Guinea, and Australia)
• global.png (also converted into global.pdf): Global distribution of MATs and MSIs
• distribution.pdf: Distribution of MATs and MSIs grouped by macroarea
• correlation.pdf: Relationship between MAT and MSI

Usage

The following 5 steps can be run separately as the output of each step is already provided in this repository. temperature_global.csv is provided in zip file because its size is too large. Steps 1 and 2 require a local storage of the ASJP dataset and the FLDAS dataset, but you can skip these two steps so you do not need to download full datasets.

1. Extract geometry and sonority data from ASJP

Run py get_sonority.py [raw_path], where [raw_path] is the path to raw folder in the local ASJP dataset (e.g. C:\ASJP\raw\). Result will be saved as sonorities.csv and phones.csv.

2. Extract temperature data from FLDAS

Run py get_temperature.py [FLDAS_path] to extract monthy temperature data of all doculects in sonorities.csv, where [FLDAS_path] is the path to FLDAS_NOAH01_C_GL_M.001 folder of the local FLDAS dataset (e.g. C:\FLDAS\FLDAS_NOAH01_C_GL_M.001\). Result will be saved as temperatures.csv.

Run py get_temperature_global.py [FLDAS_path] to extract global monthy mean temperature data. Result will be saved as temperature_global.csv.

3. Plot global distribution of temperature and sonority

(Unzip temperature_global.zip.) Run py plot_global.py. Plot will be saved as global.png.

4. Combine and process temperature and sonority data

Run py process.py. Results will be saved as data.csv, data_family.csv, and data_macroarea.csv.

5. Generate distribution and correlation plot

Run corresponding code blocks in process.r in R to:

• Read 3 csv files above and analysis the correlation
• Plot distribution of temperature and sonority
• Plot correlation of temperature and sonority

Plots were saved as distribution.pdf and correlation.pdf in this repository.