Travel time as a predictor of linguistic distance

Abstract

The aim of the present investigation 1 was to get an impression of geographic influences on dialectal variation in a country. In previous investigations, the correlations between linguistic distances and geographic distances using dialect data from the Netherlands and Norway were calculated (Gooskens and Heeringa 2004, Nerbonne et al. 1996). The results showed a high correlation in the case of the Dutch data while the correlation was considerably lower in the case of the Norwegian data. This seems to reflect the fact that for Norway, in particular, the direct distance between two settlements does not reflect the difficulty of travel and therefore social contact, which is expected to play a role in keeping linguistic distance within limits. Holland is a country with a flat, regularly populated landscape with few natural obstacles such as mountains and rivers. This is in great contrast with Norway with its high mountains and many J/ords which especially in the past made it quite difficult to travel between places. These differences in geographical situations are clearly reflected in the correlations between the linguistic and geographical distances between the dialects of the two countries.The present investigation aims to find more successful ways of predicting linguistic distances by means of geographic distances in Norway. To this end, old and new traveling data are used, providing information about travel times by road, train, and boat between the places where the different dialects are spoken. The results show that a large part of the linguistic variation can be accounted for by geography in Norway, just as in the Netherlands. However, in the case of a geographically more complicated This article is based on a paper read at the Second International Conference on Language Variation in Europe, Uppsala, 12-14 June 2003. The author wishes to thank the following people for their help at different stages in the investigation: Dag Bj0rnland (BI Norwegian School of Management) for providing the data on old travel times, Femke Jongerius (University of Groningen) for data entering, Wilbert Heeringa (University of Groningen) for help with the Levenshtein distances and statistics and for making the maps, Peter Kleiweg (University of Groningen) for his software for creating the maps and John Nerbonne (University of Groningen) for comments on an earlier version of this paper.Brought to you by | Nanyang Technological University Authenticated Download Date | 6/7/15 4:58 PM Travel time as a predictor of linguistic distance 39 country like Norway, travel times reflect the influence of geography on linguistic variation better than straight-line distances, particularly when the historical aspect is also taken into consideration since the former circumstances of travel are reflected in the modern dialects. Travel times can vary independently from straight-line distances, and this variation was more pronounced in the past, when the present dialect situation crystallized.

Language Boundaries Driven by Surface Tension

“…The definition of linguistic distance and its relation to geographic distance was made by Séguy [5,6], and the relationship therefore goes by the name Séguy's curve [39]. It has been substantially refined and tested since [2,39,79], and also generalized to involve travel time [80]. Séguy's curve is not universal, however.…”

Mutual intelligibility among the sign languages of Belgium and the Netherlands

“…It seems likely that geo graphically closer variants would show a higher degree of mutual intelligibility. Gooskens (2005) has shown that travelling times from 1900 (before the creation of the modern road system) between different regions of Norway still has an effect on the dialects spoken in those areas and that more easily accessible areas show greater linguistic similarities. This phenomenon could be even more pronounced in the case of sign languages, since until very recently deaf people had little ac cess to communication over distances using sign language or be exposed to other variants of the language in mass media (for example television broadcasts).…”

Using fuzzy clustering to reveal recurring spatial patterns in corpora of dialect maps

“…Other types of distance (like linguistic distance or travel time, cf. Gooskens 2005, Szmrecsanyi 2011) obviously are applicable as well.…”