Details
Date: May 24, 2018
Location: Curitiba, Brazil
Abstract
In Brazil, socioeconomic data are available at census tracts (polygons), while election data are available at voting locations (point-referenced). The misaligned data make it challenging to study the association between election outcomes and socioeconomic variables. Given that the voters are assigned to the nearest electoral sections, we use Voronoi tessellation to associate each voting station with a Voronoi polygon. Areal interpolation is used to change the spatial support of the socioeconomic data.