Terra Politica Terra Politica Terra Politica 3033-537X 3033-7321 112027 10.63115/3092.2025.41.93.013 CXVVCU AD HOC AD HOC AD HOC SOCIO-ECONOMIC FACTORS OF ELECTORAL BEHAVIOR IN THE USA: SPATIAL ANALYSIS OF THE 2012 AND 2016 PRESIDENTIAL ELECTIONS Социально-экономические факторы электорального поведения в США: пространственный анализ результатов президентских выборов 2012 и 2016 гг. Милецкая Алиса Ростиславна Mileckaya Alisa Rostislavna 01 07 2025 01 07 2025 1 189 207 01 03 2025 01 06 2025 https://terrapolitica.ru/en/nauka/article/112027/view

В статье проводится пространственный анализ социально-экономических факторов, влияющих на электоральное поведение в США на президентских выборах 2012 и 2016 годов. Используя метод географически взвешенной регрессии (GWR), автор выявляет территориальные кластеры взаимосвязей между различными социально-экономическими показателями и результатами голосования. Для сравнения моделей регрессии также использовался расчет индекса пространственной автокорреляции (Moran’s I) и локальный индекс пространственной автокорреляции Гетиса-Орда. Внимание уделяется изучению влияния демографических характеристик, социальных и экономических условий на электоральные предпочтения американских избирателей. Исследование показывает, что воздействие данных факторов является географически нестационарным, а использование локальных моделей регрессии позволяет получить более точные объяснения в сравнении с глобальными моделями. В статье также рассматриваются кластеры, образованные взаимодействием различных факторов, и анализируется их пространственное распределение. Результаты работы подчеркивают значимость пространственной неоднородности и демонстрируют пересечения кластеров в ряде регионов США, что открывает новые перспективы для дальнейших исследований в области электоральной географии.

The article conducts a spatial analysis of socioeconomic factors influencing electoral behavior in the USA during the 2012 and 2016 presidential elections. Using the geographically weighted regression (GWR) method, the author identifies territorial clusters of relationships between various socio-economic indicators and voting results. Moran’s I spatial autocorrelation index calculation and HotSpot Analysis of residuals were also used to compare regression models. The study focuses on the influence of demographic characteristics, social, and economic conditions on the electoral preferences of American voters. The research demonstrates that the impact of these factors is geographically non-stationary, and the use of locaf regression models provides more accurate explanations compared to global models. The article also examines clusters formed by the interaction of various factors and analyzes their spatial distribution. The findings highlight the significance of spatial heterogeneity and demonstrate intersections of clusters in several regions of the USA, opening new perspectives for further research in electoral geography.

 электоральное поведение пространственный анализ географически взвешенная регрессия выборы в США политическая география electoral behavior spatial analysis geographically weighted regression US elections political geography

Ahmed R., Pesaran M.H. (2020), Regional heterogeneity and US presidential elections, CESifo Working Paper, no. 8615, Munich: Munich Society for the Promotion of Economic Research, 74 p. Ahmed R., Pesaran M.H. (2020), Regional heterogeneity and US presidential elections, CESifo Working Paper, no. 8615, Munich: Munich Society for the Promotion of Economic Research, 74 p. Akaike H. (1974), A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716-723. Akaike H. (1974), A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716-723. Brunsdon C., Fotheringham A.S., Charlton M.E. (1996), Geographically weighted regression: a method for exploring spatial non-stationarity, Geographical Analysis, vol. 28, no. 4, pp. 281-298. Brunsdon C., Fotheringham A.S., Charlton M.E. (1996), Geographically weighted regression: a method for exploring spatial non-stationarity, Geographical Analysis, vol. 28, no. 4, pp. 281-298. Campbell A. (1980), The American Voter, Chicago: University of Chicago Press, 576 p. Campbell A. (1980), The American Voter, Chicago: University of Chicago Press, 576 p. Cho W.K.T., Gimpel J.G. (2009), Presidential voting and the local variability of economic hardship, The Forum, vol. 7, no. 1. Cho W.K.T., Gimpel J.G. (2009), Presidential voting and the local variability of economic hardship, The Forum, vol. 7, no. 1. Cox K.R. (1970), Residential relocation and political behavior: Conceptual model and empirical tests, Acta Sociologica, vol. 13, no. 1, pp. 40-53. Cox K.R. (1970), Residential relocation and political behavior: Conceptual model and empirical tests, Acta Sociologica, vol. 13, no. 1, pp. 40-53. Evans G. (2000), The continued significance of class voting, Annual Review of Political Science, vol. 3, no. 1, pp. 401-417. Evans G. (2000), The continued significance of class voting, Annual Review of Political Science, vol. 3, no. 1, pp. 401-417. Forest B. (2018), Electoral geography: From mapping votes to representing power, Geography Compass, vol. 12, no. 1, e12352. Forest B. (2018), Electoral geography: From mapping votes to representing power, Geography Compass, vol. 12, no. 1, e12352. Fotheringham A.S., Brunsdon C., Charlton M.E. (2009), Geographically weighted regression, The Sage Handbook of Spatial Analysis, eds. Fotheringham A.S., Rogerson P.A., London: SAGE, pp. 243-254. Fotheringham A.S., Brunsdon C., Charlton M.E. (2009), Geographically weighted regression, The Sage Handbook of Spatial Analysis, eds. Fotheringham A.S., Rogerson P.A., London: SAGE, pp. 243-254. Hajnal Z.L., Lee T. (2011), Why Americans don't join the party: Race, immigration, and the failure of political parties to engage the electorate, Princeton: Princeton University Press, 320 p. Hajnal Z.L., Lee T. (2011), Why Americans don't join the party: Race, immigration, and the failure of political parties to engage the electorate, Princeton: Princeton University Press, 320 p. Hamilton L.C. (1992), Regression with graphics: A second course in applied statistics, Belmont, CA: Duxbury Press, 363 p. Hamilton L.C. (1992), Regression with graphics: A second course in applied statistics, Belmont, CA: Duxbury Press, 363 p. Hillier B., Hanson J., Peponis J. (1984), What do we mean by building function?, Designing for building utilisation, eds. Powell J.D., Cooper I., Lera S., London: E & FN Spon Ltd, pp. 61-72. Hillier B., Hanson J., Peponis J. (1984), What do we mean by building function?, Designing for building utilisation, eds. Powell J.D., Cooper I., Lera S., London: E & FN Spon Ltd, pp. 61-72. Ishizawa H., Stevens G. (2007) Non-English language neighborhoods in Chicago, Illinois: 2000, Social Science Research, vol. 36, no. 3, pp. 1042-1064. Ishizawa H., Stevens G. (2007) Non-English language neighborhoods in Chicago, Illinois: 2000, Social Science Research, vol. 36, no. 3, pp. 1042-1064. Iyengar S., Westwood S.J. (2015), Fear and loathing across party lines: New evidence on group polarization, American Journal of Political Science, vol. 59, no. 3, pp. 690-707. Iyengar S., Westwood S.J. (2015), Fear and loathing across party lines: New evidence on group polarization, American Journal of Political Science, vol. 59, no. 3, pp. 690-707. Jana M., Sar N. (2016), Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level, India, Modeling Earth Systems and Environment, vol. 2, no. 2, pp. 1-10. Jana M., Sar N. (2016), Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level, India, Modeling Earth Systems and Environment, vol. 2, no. 2, pp. 1-10. Johnston R., Jones K., Propper C., Burgess S. (2007), Region, local context, and voting at the 1997 general election in England, American Journal of Political Science, vol. 51, no. 3, pp. 640-654. Johnston R., Jones K., Propper C., Burgess S. (2007), Region, local context, and voting at the 1997 general election in England, American Journal of Political Science, vol. 51, no. 3, pp. 640-654. Kinder D.R., Kiewiet D.R. (1979), Economic discontent and political behavior: The role of personal grievances and collective economic judgments in congressional voting, American Journal of Political Science, vol. 23, pp. 495-527. Kinder D.R., Kiewiet D.R. (1979), Economic discontent and political behavior: The role of personal grievances and collective economic judgments in congressional voting, American Journal of Political Science, vol. 23, pp. 495-527. Kramer G.H. (1971), Short-term fluctuations in US voting behavior, 1896-1964, American Political Science Review, vol. 65, no. 1, pp. 131-143. Kramer G.H. (1971), Short-term fluctuations in US voting behavior, 1896-1964, American Political Science Review, vol. 65, no. 1, pp. 131-143. Lewis-Beck M.S. (1988), Economics and elections: The major Western democracies, Ann Arbor: University of Michigan Press, 183 P. Lewis-Beck M.S. (1988), Economics and elections: The major Western democracies, Ann Arbor: University of Michigan Press, 183 P. Lind J.T. (2007), Does permanent income determine the vote?, The BE Journal of Macroeconomics, vol. 7, no. 1. Lind J.T. (2007), Does permanent income determine the vote?, The BE Journal of Macroeconomics, vol. 7, no. 1. Menard S. (2002), Applied logistic regression analysis, Thousand Oaks: SAGE, 128 p. Menard S. (2002), Applied logistic regression analysis, Thousand Oaks: SAGE, 128 p. Moran P.A. (1948), The interpretation of statistical maps, Journal of the Royal Statistical Society. Series B., vol. 10, no. 2, pp. 243-251. Moran P.A. (1948), The interpretation of statistical maps, Journal of the Royal Statistical Society. Series B., vol. 10, no. 2, pp. 243-251. Ord J.K., Getis A. (1995), Local spatial autocorrelation statistics: distributional issues and an application, Geographical Analysis, vol. 27, no. 4, pp. 286-306. Ord J.K., Getis A. (1995), Local spatial autocorrelation statistics: distributional issues and an application, Geographical Analysis, vol. 27, no. 4, pp. 286-306. Paez A. (2004), Anisotropic variance functions in geographically weighted regression models, Geographical Analysis, vol. 36, no. 4, pp. 299-314. Paez A. (2004), Anisotropic variance functions in geographically weighted regression models, Geographical Analysis, vol. 36, no. 4, pp. 299-314. Steenbergen M.R. (2010), Decomposing the vote: Individual, communal, and cantonal sources of voting behavior in Switzerland, Swiss Political Science Review, vol. 16, no. 3, pp. 403-424. Steenbergen M.R. (2010), Decomposing the vote: Individual, communal, and cantonal sources of voting behavior in Switzerland, Swiss Political Science Review, vol. 16, no. 3, pp. 403-424. Sui D.Z., Hugill P.J. (2002), A GIS-based spatial analysis on neighborhood effects and voter turnout: A case study in College Station, Texas, Political Geography, vol. 21, no. 2, pp. 159-173. Sui D.Z., Hugill P.J. (2002), A GIS-based spatial analysis on neighborhood effects and voter turnout: A case study in College Station, Texas, Political Geography, vol. 21, no. 2, pp. 159-173. Tenn S. (2007), The effect of education on voter turnout, Political Analysis, vol. 15, no. 4, pp. 446-464. Tenn S. (2007), The effect of education on voter turnout, Political Analysis, vol. 15, no. 4, pp. 446-464. Tu J., Xia Z.G. (2008), Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation, Science of the Total Environment, vol. 407, no. 1, pp. 358-378. Tu J., Xia Z.G. (2008), Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation, Science of the Total Environment, vol. 407, no. 1, pp. 358-378. Weber D., Englund E. (1992), Evaluation and comparison of spatial interpolators, Mathematical Geology, vol. 24, pp. 381-391. Weber D., Englund E. (1992), Evaluation and comparison of spatial interpolators, Mathematical Geology, vol. 24, pp. 381-391.