Religion or class? Measuring voting clustering on religious and socioeconomic lines in US presidential elections

Electoral behavior in the United States is shaped by more than geography and economics as it is deeply intertwined with cultural identity. Here, we quantify how voting patterns in the 2016, 2020 and 2024 presidential elections cluster not only across neighboring counties but also along shared religious and socioeconomic lines. By computing Moran's I, a standard measure of spatial autocorrelation, under four distinct "neighborhood" definitions (physical borders, dominant religion, income bracket and urbanization level), we show that counties sharing a majority faith vote in strikingly similar ways, second only to contiguous geography. In contrast, grouping by household income or urban status yields markedly weaker clustering. These findings reveal that cultural networks, embodied by religious affiliation, exert a stronger influence on aggregate voting behavior than class differences or the urban-rural divide. Our approach highlights the power of simple network models grounded in social traits to illuminate the dynamics of political polarization and suggests new pathways for understanding how cultural identity shapes large scale electoral outcomes.