The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008–2020)

Political and social processes that shape people's voting preferences might be linked to geographical location, varying from place to place, and operating at local, regional, and national scales. Here, we use a local modeling technique, multiscale geographically weighted regression (MGWR), to examine spatial and temporal variations in the influences of county-level socio-economic factors on voter preference during the 2008–2020 U.S. presidential elections. We argue that the local intercept in the MGWR model is an indicator of the effect of spatial context on voter preference and not only can this be separated from the effect of other socio-economic factors, but it needs to be in order to prevent misspecification bias in the indicators of these other factors. We also identify strong and consistent divisions across the country in how context shapes election results.