Abstract
Geographic concentration of employment types frequently yields clusters exhibiting moderate-to-strong positive spatial autocorrelation. Such clusters based upon geographic proximity, and frequently quantified with location quotients, also can relate to local indices of spatial autocorrelation, such as LISA and the Getis-Ord statistics. Carroll et al. (Ann Reg Sci 42:449–463, 2008) furnish a comparison of these sets of indices. This chapter adds to that literature, but by conceptualizing location quotients as spatially autocorrelated binomial random variables.
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Notes
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A random effects term treats a regression residual as a composite that is the sum of two terms, a random observation effect (differences among individual observational units) plus an independent and identically distributed space–time -varying residual error (which links to change over space and time). These two terms cannot be separated without additional information, such as priors for a Bayesian analysis, and repeated measures for a frequentist analysis.
References
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Griffith, D.A., Paelinck, J.H.P. (2018). Clustering: Spatial Autocorrelation and Location Quotients. In: Morphisms for Quantitative Spatial Analysis. Advanced Studies in Theoretical and Applied Econometrics, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-319-72553-6_6
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DOI: https://doi.org/10.1007/978-3-319-72553-6_6
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