Visualizing Post-Election Presidential Approval Ratings in Texas and California: A County-Level Geospatial Analysis Using Eight Complementary Visualization Techniques

Abstract

While polarization has been extensively studied on the national level, there is notable geographical variability within states and counties when it comes to presidential approval ratings. In this paper, we investigate county-level temporal and geographical variations of presidential approval ratings in two contrasting states, namely Republican Texas and Democratic California, from January 2020 to October 2025. Based on aggregated presidential approval rating estimates provided by Gallup, Pew Research Center, and FiveThirtyEight, and merged using county-level shapefiles obtained from the U.S. Census Bureau TIGER/Line with the help of FIPS codes, we create an analysis workflow that results in eight different visualizations. Our major contribution is the justification for each visualization type based on Munzner's task decomposition method, showing how each visualized statistic answers its own research question not addressable by any other type. While California consistently registers approval ratings above the national average by 8-12 percentage points, Texas falls behind by 6-10 percentage points; the difference in approval ratings increased by approximately 5 points following the 2024 presidential elections. A continuous positive association between approval ratings and an urban-rural axis exists in both states (r = +0.71 in California and r = +0.58 in Texas). Finally, a population-weighted bubble map proves that national disapproval visible in choropleths is a consequence of geography rather than demography. We claim that visualization design choices in political science are epistemological: what gets shown in a chart affects the nature of analysis.