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2 results

  • Spatial analysis of sub-regional variation in Southern US English

  • Jonathan A. Jones, Margaret E. L. Renwick
  • Journal:
    Journal of Linguistic Geography / Volume 9 / Issue 2 / October 2021
    Published online by Cambridge University Press:
    08 February 2022, pp. 86-105
    • Article
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      • Open access
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  • This project uses GIS mapping to analyze spatial trends in spoken language, testing how features identified as part of the “Southern dialect” by the Atlas of North American English (ANAE; Labov et al., 2006) are used in the Digital Archive of Southern Speech (DASS; Kretzschmar et al., 2013). We analyze vowel mergers, diphthongization, monophthongization, fronting, and several consonantal features. Rather than drawing isoglosses, we use local spatial autocorrelation analysis to reveal subregional patterning in the data. We present a series of maps illustrating the realization of Southern speech features as enumerated by ANAE. We find little evidence for ANAE’s Inland South region based on acoustics, and while some areas surveyed in DASS align well with the portrayal of Southern speech presented by ANAE, others do not.

  • 4 - Spatial Autocorrelation

  • George Grekousis
  • Book:
    Spatial Analysis Methods and Practice
    Published online:
    20 May 2020
    Print publication:
    11 June 2020, pp 207-274

  • Summary

    This chapter deals with

    • Spatial autocorrelation and its importance to geographical problems

    • Global and local spatial autocorrelation techniques like Moran’s I, Getis-Ord G and Geary C

    • Tracing spatial clusters of high values (hot spots) or low values (cold spots)

    • Tracing spatial outliers

    • Optimized hot spot analysis

    • Interpreting the statistical significance of results

    • Incremental spatial autocorrelation used to define the appropriate scale of analysis

    • The multiple comparison problem and spatial dependence

    • Introducing Bonferroni correction and the false discovery rate

    • Spatiotemporal autocorrelation analysis using bivariate and differential Local Moran’s I index

    • Presenting examples of step-by-step solutions using ArcGIS and GeoDa

    After a thorough study of the theory and lab sections, you will be able to

    • Distinguish between global and local spatial autocorrelation

    • Understand why spatial autocorrelation analysis is relevant to geographical analysis

    • Apply local and global indices of spatial autocorrelation like local Moran’s, Getis-Ord Gi and Gi∗

    • Use Moran’s I scatter plot to identify patterns

    • Identify if clustering of hot or cold spots exist

    • Identify and locate spatial outliers

    • Use Bivariate and Differential Local Moran’s I to identify if spatiotemporal autocorrelation exists and if changes cluster over time

    • Apply these tools using ArcGISand GeoDa

    • Interpret the results from both the statistical significance and spatial analysis standpoints