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Research in political science is increasingly, but independently, modeling heterogeneity and spatial dependence. This article draws together these two research agendas via spatial random effects survival models. In contrast to standard survival models, which assume spatial independence, spatial survival models allow for spatial autocorrelation at neighboring locations. I examine spatial dependence in both semiparametric Cox and parametric Weibull models and in both individual and shared frailty models. I employ a Bayesian approach in which spatial autocorrelation in unmeasured risk factors across neighboring units is incorporated via a conditionally autoregressive (CAR) prior. I apply the Bayesian spatial survival modeling approach to the timing of U.S. House members' position announcements on NAFTA. I find that spatial shared frailty models outperform standard nonfrailty models and nonspatial frailty models in both the semiparametric and parametric analyses. The modeling of spatial dependence also produces changes in the effects of substantive covariates in the analysis.

Political geography has long played a prominent role in conceptions of political realignments. In this article, I apply a spatial analysis to examine the political geography of voting during one of the principal political realignments in American electoral history, the 1928—1936 Democratic realignment. The spatial analysis challenges some of our common conceptions of this realignment. For example, increased support for the Democrats and Al Smith in 1928 was not limited to urban areas, as Smith enjoyed widespread increases in Democratic support in largely rural Western locations. In the 1932 election, unemployment actually impeded shifts toward Franklin Roosevelt and the Democrats in most locales. Changes in voter support during this period were highly localized and subnational. Geographically Weighted Regressions demonstrate that this localized political geography was shaped by extensive geographic variation in how political and demographic factors influenced voting behavior across the United States.

Intro -- Preface -- Contents -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Modern Partisan Polarization -- 1.2 Polarization and Geography -- 1.3 Outline of the Book -- References -- 2 Beyond ``Red State, Blue State'': The Political Geography of Presidential Competition, 1828-2016 -- 2.1 Partisanship, Voting, and Polarization -- 2.2 Polarization and Geography -- 2.3 The Political Geography of Presidential Voting -- 2.3.1 Global Measures of Spatial Autocorrelation -- 2.4 Analysis of County-Level Voting, 1828-2016 -- 2.4.1 Spatial Analysis -- 2.5 Political Geography and the American Party Systems -- 2.5.1 The Second American Party System -- 2.5.2 The Third American Party System -- 2.5.3 The Fourth American Party System -- 2.5.4 The Fifth American Party System -- 2.5.5 The Sixth American Party System -- 2.6 Conclusion -- References -- 3 Geographic Polarization in Historical Perspective -- 3.1 Bishop's Argument -- 3.2 Recent Assessment of The Big Sort -- 3.3 Historical Examination of Geographic Polarization -- 3.4 Education and the Geography of Presidential Voting -- 3.5 Conclusion -- Appendix -- References -- 4 Modeling the Political Geography of Presidential Voting -- 4.1 Diagnostics for Spatial Dependence -- 4.2 Analysis of Voting Behavior, 1828-2016 -- 4.3 The Second American Party System -- 4.4 The Third American Party System -- 4.5 The Fourth American Party System -- 4.6 The Fifth American Party System -- 4.7 The Sixth American Party System -- 4.8 The Interaction of Racial and Immigrant Demographics -- 4.9 Conclusion -- References -- 5 Clinton vs. Trump: Back to the Future? -- 5.1 The Relationship Between the 1828 and 2016 Elections -- 5.2 Looking Further on the 1828 and 2016 Relationship -- 5.3 Trump and Jackson: A Closer Look -- 5.4 Adams and Clinton: A Closer Look -- References -- 6 Conclusion.

Formal models of revolutionary collective action suggest that 'informational cascades' play a crucial role in overcoming collective action problems. These models highlight how information about the aggregate level of participation in collective action conveys information about others' political preferences, and how such informational cues allow potential participants to update their beliefs about the value of participating in antiregime collective action. In authoritarian regimes, foreign mass media are often the only credible source of information about antiregime protests. However, limited robust evidence exists on whether foreign media can indeed serve as a coordination device for collective action. This article makes use of a detailed dataset on protest events during the 1989 East German revolution and exploits the fact that West German television broadcasts could be received in most but not all parts of East Germany. Across a wide range of Cox proportional hazards models and conditional on a rich set of observables, it finds that the availability of West German television did not affect the probability of protest events occurring. The evidence presented here does not support the widely accepted 'fact' that West German television served as a coordination device for antiregime protests during the East German revolution. More broadly, it also calls into question strong claims about the effects of communication technology on revolutionary collective action.