A new local indicator of spatial autocorrelation identifies clusters of high rendaku frequency in Japanese place names

Abstract

The methods of spatial statistics have been successfully applied to the study of linguistic variation, especially for detecting the existence of spatial patterns in the geographical distribution of linguistic features. However, the use of local indicators of spatial autocorrelation for detecting spatial clusters have been limited to continuous variables, and we propose to apply the new method of Anselin and Li (2019) for categorical variables to linguistic data. We illustrate this method with the case of Japanese rendaku, or sequential voicing, whose dialectal variation is still poorly documented. Focusing on regional differences in the frequency of rendaku, we examined the occurrence of rendaku for four lexemes in 4,921 place names from all Japan. A statistical analysis of local spatial association and an unsupervised density-based cluster analysis revealed the existence of two cluster areas of high rendaku frequency centered around Wakayama and Fukushima-Yamagata prefectures. This suggests that rendaku is more frequent in those dialects, and we recommend that further studies in the dialectal variation of rendaku start by looking at those areas.