Testing for Network and Spatial Autocorrelation

Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


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.


Social networks Statistical dependence Spatial autocorrelation Peer effects 



Youjin Lee and Elizabeth Ogburn were supported by ONR grant N000141512343. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195 and HHSN268201500001I). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of PennsylvaniaPhiladelphiaUSA
  2. 2.Johns Hopkins Bloomberg School of Public HealthBaltimoreUSA

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