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President Donald Trump has made various decisions, many controversial, to manage the coronavirus pandemic. The reaction to President Trump’s leadership has been met with a mixed response from the public. This raises an important question; what factors influence a citizen’s evaluation of President Trump’s response to the pandemic? We develop a theory that links a citizen knowing someone diagnosed with COVID-19 with their evaluation of President Trump’s management of the pandemic, with the expectation that this relationship is conditioned by a citizen’s ideology. Using data from two surveys, we find that knowing someone diagnosed with COVID-19 diminishes the effect ideology has on a citizen’s evaluation. Additionally, we find that a citizen’s evaluation of President Trump’s leadership on COVID-19 is associated with their vote choice in the 2020 U.S. Presidential Election. Overall, this article contributes to our understanding of public opinion on COVID-19 and its political ramifications.

Theoretical units of interest often do not align with the spatial units at which data are available. This problem is pervasive in political science, particularly in subnational empirical research that requires integrating data across incompatible geographic units (e.g., administrative areas, electoral constituencies, and grid cells). Overcoming this challenge requires researchers not only to align the scale of empirical and theoretical units, but also to understand the consequences of this change of support for measurement error and statistical inference. We show how the accuracy of transformed values and the estimation of regression coefficients depend on the degree of nesting (i.e., whether units fall completely and neatly inside each other) and on the relative scale of source and destination units (i.e., aggregation, disaggregation, and hybrid). We introduce simple, nonparametric measures of relative nesting and scale, as ex ante indicators of spatial transformation complexity and error susceptibility. Using election data and Monte Carlo simulations, we show that these measures are strongly predictive of transformation quality across multiple change-of-support methods. We propose several validation procedures and provide open-source software to make transformation options more accessible, customizable, and intuitive.

Two important trends in applied statistics are an increased usage of geospatial models and an increased usage of big data. Naturally, there has been overlap as analysts utilize the techniques associated with each. With geospatial methods such as kriging, the computation required becomes intensive quickly, even with datasets that would not be considered huge in other contexts. In this work we describe a solution to the computational problem of estimating Bayesian kriging models with big data, Bootstrap Random Spatial Sampling (BRSS), and first provide an analytical argument that BRSS produces consistent estimates from the Bayesian spatial model. Second, with a medium-sized dataset on fracking in West Virginia, we show that bootstrap sample effects from a full-information Bayesian model are reduced with more bootstrap samples and more observations per sample as in standard bootstrapping. Third, we offer a realistic illustration of the method by analyzing campaign donors in California with a large geocoded dataset. With this solution, scholars will not be constrained in their ability to apply theoretically relevant geospatial Bayesian models when the size of the data produces computational intractability.