Literature DB >> 12804873

Covariances among join-count spatial autocorrelation measures.

Bryan K Epperson1.   

Abstract

Spatial distributions of biological variables are often well-characterized with pairwise measures of spatial autocorrelation. In this article, the probability theory for products and covariances of join-count spatial autocorrelation measures are developed for spatial distributions of multiple nominal (e.g. species or genotypes) types. This more fully describes the joint distributions of pairwise measures in spatial distributions of multiple (i.e. more than two) types. An example is given on how the covariances can be used for finding standard errors of weighted averages of join-counts in spatial autocorrelation analysis of more than two types, as is typical for genetic data for multiallelic loci.

Mesh:

Year:  2003        PMID: 12804873     DOI: 10.1016/s0040-5809(03)00023-6

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  2 in total

1.  The use of spatial autocorrelation analysis to identify PAHs pollution hotspots at an industrially contaminated site.

Authors:  Geng Liu; Rutian Bi; Shijie Wang; Fasheng Li; Guanlin Guo
Journal:  Environ Monit Assess       Date:  2013-06-09       Impact factor: 2.513

2.  Spatial genetic structure in Pinus cembroides Zucc. at population and landscape levels in central and northern Mexico.

Authors:  Luis C García-Zubia; Javier Hernández-Velasco; José C Hernández-Díaz; Sergio L Simental-Rodríguez; Carlos A López-Sánchez; Carmen Z Quiñones-Pérez; Artemio Carrillo-Parra; Christian Wehenkel
Journal:  PeerJ       Date:  2019-11-06       Impact factor: 2.984

  2 in total

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