The article conducts a spatial analysis of socioeconomic factors influencing electoral behavior in the USA during the 2012 and 2016 presidential elections. Using the geographically weighted regression (GWR) method, the author identifies territorial clusters of relationships between various socio-economic indicators and voting results. Moran’s I spatial autocorrelation index calculation and HotSpot Analysis of residuals were also used to compare regression models. The study focuses on the influence of demographic characteristics, social, and economic conditions on the electoral preferences of American voters. The research demonstrates that the impact of these factors is geographically non-stationary, and the use of locaf regression models provides more accurate explanations compared to global models. The article also examines clusters formed by the interaction of various factors and analyzes their spatial distribution. The findings highlight the significance of spatial heterogeneity and demonstrate intersections of clusters in several regions of the USA, opening new perspectives for further research in electoral geography.
electoral behavior, spatial analysis, geographically weighted regression, US elections, political geography
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