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

A new local indicator of spatial autocorrelation identifies clusters of high <i>rendaku</i> frequency in Japanese place names
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空间自相关的新局部指标可识别日本地名中的高 <i>rendaku</i> 频率集群

DOI:
10.1017/jlg.2022.11
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发表时间:
2023
期刊:
Journal of Linguistic Geography
影响因子:
--
通讯作者:
Timothy J. Vance
Timothy J. Vance
中科院分区:
--
文献类型:
--
作者:
Thomas Pellard ;Akiko Takemura;Hyun Kyung Hwang ;Timothy J. Vance

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空间统计方法已经成功地应用于语言变异的研究,特别是在检测语言特征的地理分布中是否存在空间模式方面。然而,使用局部空间自相关指标来检测空间聚类一直局限于连续变量,我们建议将Anselin和Li(2019)的新方法应用于语言数据中的范畴变量。我们用日语连续发音的例子来说明这种方法,这种发音的方言变异仍然没有得到很好的记录。着眼于连词出现频率的地区差异,我们考察了来自全日本的4921个地名中四个词位的连词出现情况。对当地空间关联性的统计分析和基于非监督密度的聚类分析表明,存在以和歌山县和福岛-山形县为中心的两个伦达库频率较高的聚集区。这表明伦达库在这些方言中出现的频率更高,我们建议进一步研究伦达库的方言变异,从这些地区开始。
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.
DOI: 10.1080/02693799308901981
发表时间: 2011
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