Testing for Network and Spatial Autocorrelation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting.

Original languageEnglish (US)
Title of host publicationProceedings of NetSci-X 2020
Subtitle of host publication6th International Winter School and Conference on Network Science
EditorsNaoki Masuda, Kwang-Il Goh, Tao Jia, Junichi Yamanoi, Hiroki Sayama
Number of pages14
ISBN (Print)9783030389642
StatePublished - 2020
Event6th International School and Conference on Network Science, NetSci-X 2020 - Tokyo, Japan
Duration: Jan 20 2020Jan 23 2020

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692


Conference6th International School and Conference on Network Science, NetSci-X 2020


  • Peer effects
  • Social networks
  • Spatial autocorrelation
  • Statistical dependence

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications


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