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This paper studies a novel spatial auto-correlation model of voteruncertainty across districts. We use the Moran $I$ index to measure theauto-correlation of Shannon, relative Shannon, Tsallis and relative Tsallisentropies of regional electoral outcomes with respect to geographic adjacency,proximity and sectarian adjacency. Using data from the Lebanese parliamentaryelections, we find strong geographic and gravitational adjacency correlations.More importantly, there is a notably strong correlation in sectarian adjacencyin both $2018$ and $2022$ elections, with a very high level of confidence. Thisresult asserts the dominance of the sectarian factor in Lebanese politics. Wealso introduce the method of maximized general entropy estimation that allowsus to determine the Markov transition matrix of voters between consecutiveelections.

Entropic Spatial Auto-correlation of Voter Uncertainty and Voter Transitions in Parliamentary Elections