Learning about Spatial and Temporal Proximity using Tree-Based Methods

References

Aidt, T. S., and R. Franck. 2015. “Democratization under the Threat of Revolution: Evidence from the Great Reform Act of 1832.” Econometrica 83 (2): 505–47, https://doi.org/10.3982/ecta11484.Search in Google Scholar

Branton, R., G. Dillingham, J. Dunaway, and B. Miller. 2007. “Anglo Voting on Nativist Ballot Initiatives: The Partisan Impact of Spatial Proximity to the US-Mexico Border.” Social Science Quarterly 88 (3): 882–97, https://doi.org/10.1111/j.1540-6237.2007.00488.x.Search in Google Scholar

Branton, R., V. Martinez-Ebers, T. E. Carey Jr, and T. Matsubayashi. 2015. “Social Protest and Policy Attitudes: The Case of the 2006 Immigrant Rallies.” American Journal of Political Science 59 (2): 390–402, https://doi.org/10.1111/ajps.12159.Search in Google Scholar

Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32, https://doi.org/10.1023/a:1010933404324.10.1023/A:1010933404324Search in Google Scholar

Carpini, M. X. D., K. Scott, and J. D. Kennamer. 1994. “Effects of the News Media Environment on Citizen Knowledge of State Politics and Government.” Journalism Quarterly 71 (2): 443–56, https://doi.org/10.1177/107769909407100217.Search in Google Scholar

Chipman, H. A., E. I. George, and R. E. McCulloch. 2007. “Bayesian Ensemble Learning.” In Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 265–72. Cambridge, MA: MIT Press.10.7551/mitpress/7503.003.0038Search in Google Scholar

Cho, W. K. T., and J. G. Gimpel. 2012. “Geographic Information Systems and the Spatial Dimensions of American Politics.” Annual Review of Political Science 15: 443–60, https://doi.org/10.1146/annurev-polisci-031710-112215.Search in Google Scholar

Cohen, A. P., D. Azrael, and M. Miller. 2014. Rate of Mass Shootings Has Tripled since 2011, Harvard Research Shows. San Francisco: Mother Jones, October 15, 2014. Also available at http://www.motherjones.com/politics/2014/10/mass-shootings-increasing-harvard-research (accessed March 1, 2022).Search in Google Scholar

Enos, R. D. 2017. The Space between Us: Social Geography and Politics. New York: Cambridge University Press.10.1017/9781108354943Search in Google Scholar

Follman, M., A. Gavin, D. Pan, and M. Caldwell. 2012. US Mass Shootings, 1982–2012: Data from Mother Jones’ Investigation. San Francisco: Mother Jones, Published December 28, 2012. Updated September 16, 2013. Latest version available at http://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data (accessed March 1, 2022).Search in Google Scholar

Friedman, J., T. Hastie, and R. Tibshirani. 2010. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software 33 (1), https://doi.org/10.18637/jss.v033.i01.Search in Google Scholar

Grimmer, J. 2015. “We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together.” PS: Political Science & Politics 48 (1): 80–3, https://doi.org/10.1017/s1049096514001784.Search in Google Scholar

Gleditsch, K. S., and M. D. Ward. 2001. “Measuring Space: A Minimum-Distance Database and Applications to International Studies.” Journal of Peace Research 38 (6): 739–58, https://doi.org/10.1177/0022343301038006006.Search in Google Scholar

Hastie, T., R. Tibshirani, and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.10.1007/978-0-387-84858-7Search in Google Scholar

King, G., M. Tomz, and J. Wittenberg. 2000. “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44 (2): 341–55, https://doi.org/10.2307/2669316.Search in Google Scholar

Liaw, A., and M. Wiener. 2002. “Classification and Regression by randomForest.” R News 2 (3): 18–22.Search in Google Scholar

Montgomery, J. M., and S. Olivella. 2018. “Tree-Based Models for Political Science Data.” American Journal of Political Science 62 (3): 729–44, https://doi.org/10.1111/ajps.12361.Search in Google Scholar

Newman, B. J., and T. K. Hartman. 2019. “Mass Shootings and Public Support for Gun Control.” British Journal of Political Science 49 (4): 1527–53, https://doi.org/10.1017/s0007123417000333.Search in Google Scholar

Reny, T. T., and B. J. Newman. 2018. “Protecting the Right to Discriminate: The Second Great Migration and Racial Threat in the American West.” American Political Science Review 112 (4): 1104–10, https://doi.org/10.1017/s0003055418000448.Search in Google Scholar

Sparapani, R., and C. Spanbauer. 2021. “Nonparametric machine learning and efficient computation with bayesian additive regression trees: the BART R package.” Journal of Statistical Software 97: 1–66.10.18637/jss.v097.i01Search in Google Scholar

Strobl, C., B. Anne-Laure, T. Kneib, T. Augustin, and A. Zeileis. 2008. “Conditional Variable Importance for Random Forests.” BMC Bioinformatics 9 (1): 307, https://doi.org/10.1186/1471-2105-9-307.Search in Google Scholar

Therneau, T., and B. Atkinson. 2015. “An Introduction to Recursive Partitioning Using the RPART Routines.” Also available at http://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf.Search in Google Scholar

Therneau, T., B. Atkinson, and B. Ripley. 2015. “Package ‘rpart.’.” Also available at http://cran.r-project.org/web/packages/rpart/rpart.pdf.Search in Google Scholar

Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society: Series B 58 (1): 267–88, doi:https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.Search in Google Scholar

Varian, H. R. 2014. “Big Data: New Tricks for Econometrics.” The Journal of Economic Perspectives 28 (2): 3–28, https://doi.org/10.1257/jep.28.2.3.Search in Google Scholar

Wallace, S. J., C. Zepeda-Millán, and M. Jones-Correa. 2014. “Spatial and Temporal Proximity: Examining the Effects of Protests on Political Attitudes.” American Journal of Political Science 58 (2): 433–48, https://doi.org/10.1111/ajps.12060.Search in Google Scholar

Wallach, H. 2014. “Big Data, Machine Learning, and the Social Sciences.” Montreal: NIPS 2014. Also available at https://hannawallach.medium.com/big-data-machine-learning-and-the-social-sciences-927a8e20460d (accessed March 1, 2022).Search in Google Scholar