  We introduce a geospatial bounded confidence model with mega-influencers,
inspired by Hegselmann and Krause. The inclusion of geography gives rise to
large-scale geospatial patterns evolving out of random initial data; that is,
spatial clusters of like-minded agents emerge regardless of initialization.
Mega-influencers and stochasticity amplify this effect, and soften local
consensus. As an application, we consider national views on Covid-19 vaccines.
For a certain set of parameters, our model yields results comparable to real
survey results on vaccine hesitancy from late 2020.
