[W]hat lies beneath: using latent networks to improve spatial predictions

Cassy Dorff, Max Gallop, Shahryar Minhas

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Spatial interdependencies commonly drive the spread of violence in civil conflict. To address such interdependence, scholars often use spatial lags to model the diffusion of violence, but this requires an explicit operationalization of the connectivity matrices that represent the spread of conflict. Unfortunately, in many cases, there are multiple competing processes that facilitate the spread of violence making it difficult to identify the true data-generating process. We show how a network driven methodology can allow us to account for the spread of violence, even in the cases where we cannot directly measure the factors that drive diffusion. To do so, we estimate a latent connectivity matrix that captures a variety of possible diffusion patterns. We use this procedure to study intrastate conflict in eight conflict-prone countries and show how our framework enables substantially better predictive performance than canonical spatial lag measures. We also investigate the circumstances under which canonical spatial lags suffice, and those under which a latent network approach is beneficial.
Original languageEnglish
Article numbersqab086
Number of pages32
JournalInternational Studies Quarterly
Issue number1
Early online date11 Nov 2021
Publication statusPublished - 31 Mar 2022


  • spatial analysis
  • network analysis
  • conflict
  • civil war


Dive into the research topics of '[W]hat lies beneath: using latent networks to improve spatial predictions'. Together they form a unique fingerprint.

Cite this