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The Electoral Geography of Weimar Germany: Exploratory Spatial Data Analyses (ESDA) of Protestant Support for the Nazi Party

Published online by Cambridge University Press:  04 January 2017

John O'Loughlin*
Affiliation:
Institute of Behavioral Science and Department of Geography, University of Colorado, Boulder, CO 80309-0487. e-mail: johno@colorado.edu

Abstract

For more than half a century, social scientists have probed the aggregate correlates of the vote for the Nazi party (NSDAP) in Weimar Germany. Since individual-level data are not available for this time period, aggregate census data for small geographic units have been heavily used to infer the support of the Nazi party by various compositional groups. Many of these studies hint at a complex geographic patterning. Recent developments in geographic methodologies, based on Geographic Information Science (GIS) and spatial statistics, allow a deeper probing of these regional and local contextual elements. In this paper, a suite of geographic methods—global and local measures of spatial autocorrelation, variography, distance-based correlation, directional spatial correlograms, vector mapping, and barrier definition (wombling)—are used in an exploratory spatial data analysis of the NSDAP vote. The support for the NSDAP by Protestant voters (estimated using King's ecological inference procedure) is the key correlate examined. The results from the various methods are consistent in showing a voting surface of great complexity, with many local clusters that differ from the regional trend. The Weimar German electoral map does not show much evidence of a nationalized electorate, but is better characterized as a mosaic of support for “milieu parties,” mixed across class and other social lines, and defined by a strong attachment to local traditions, beliefs, and practices.

Type
Research Article
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2002 

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References

Agnew, J. A. 1987. Place and Politics: The Geographical Mediation of State and Society. Boston: Allen and Unwin.Google Scholar
Agnew, J. A. 1988. “‘Better Thieves than Reds’? The Nationalization Thesis and the Possibility of a Geography of Italian Politics.” Political Geography Quarterly 7:307321.Google Scholar
Anselin, L. 1988. Spatial Econometrics: Methods and Models. Dordrecht, Netherlands: Kluwer Academic Publishers.CrossRefGoogle Scholar
Anselin, L. 1995. “Local Indicators of Spatial Association—LISA.” Geographical Analysis 27:93115.Google Scholar
Anselin, L. 1998. Spacestat Tutorial: A Workbook for Using Spacestat in the Analysis of Spatial Data. Morgantown, WV: Regional Research Institute.Google Scholar
Anselin, L. 2000. “The Alchemy of Statistics, or Creating Data Where No Data Exist.” Annals of the Association of American Geographers 90:586592.Google Scholar
Anselin, L., and Bao, S. 1997. “Exploratory Spatial Data Analysis Linking SpaceStat and ArcView.” In Recent Developments in Spatial Analysis, eds. Fischer, M., and Getis, A. Berlin: Springer-Verlag, pp. 3559.Google Scholar
Anselin, L., and Tam Cho, W. 2002. “Spatial Effects and Ecological Inference.” Political Analysis 10:276297.Google Scholar
Ault, B., and Brustein, W. 1998. “Joining the Nazi Party.” American Behavioral Scientist 41:13041323.CrossRefGoogle Scholar
Bailey, T., and Gatrell, A. 1995. Interactive Spatial Data Analysis. Harlow, UK: Longman.Google Scholar
Barbujani, G., and Sokal, R. R. 1990. “Zones of Sharp Genetic Change in Europe Are also Linguistic Boundaries.” Proceedings of the National Academy of Sciences 87:18161819.Google Scholar
Barbujani, G., and Sokal, R. R. 1991. “Geographic Population Structure of Italy. II—Physical and Cultural Barriers to Gene Flow.” American Journal of Human Genetics 48:398411.Google ScholarPubMed
Bocquet-Appel, J. P., and Bacro, J. N. 1994. “Generalized Wombling.” Systematic Zoology 43:442448.Google Scholar
Brunsdon, C., Fotheringham, A. S., and Charlton, M. E. 1998. “Geographically Weighted Regression—Modelling Spatial Nonstationarity.” The Statistician 47(3): 431443.CrossRefGoogle Scholar
Brustein, W. 1990. “The Political Geography of Fascist Party Membership in Italy and Germany, 1918-1933.” In Social Institutions: Their Emergence, Maintenance and Effects, eds. Hechter, M., Opp, K.-D., and Wippler, R. New York: Aldine, pp. 245264.Google Scholar
Brustein, W. 1996. The Logic of Evil: The Social Origins of the Nazi Party, 1925-1933. New Haven, CT: Yale University Press.Google Scholar
Brustein, W., and Falter, J. 1995. “Who Joined the Nazi Party? Assessing Theories of the Social Origins of Nazism.” Zeitgeschichte 22:83108.Google Scholar
Childers, T. 1983. The Nazi Voter: The Social Foundations of Fascism in Germany, 1919-1933. Chapel Hill: University of North Carolina Press.Google Scholar
Cliff, A. D., and Ord, J. K. 1981. Spatial Processes: Models and Applications. London: Pion.Google Scholar
Cressie, N. 1991. Statistics for Spatial Data. New York: John Wiley and Sons.Google Scholar
Davies-Withers, S. 2001. “Quantitative Methods: Advancement in Ecological Inference.” Progress in Human Geography 25:8796.Google Scholar
Diggle, P. 2002. Statistical Analysis of Spatial Point Patterns. New York: Oxford University Press.Google Scholar
Dutilleul, P., Stockwell, J. D., Frigon, D., and Legendre, P. 2000. “The Mantel Test versus Pearson's Correlation Analysis: Assessment of the Differences for Biological and Environmental Studies.” Journal of Agricultural, Biological and Environmental Statistics 5:131150.Google Scholar
Falsetti, A. B., and Sokal, R. R. 1993. “Genetic Structure of Human Populations in the British Isles.” Annals of Human Biology 20:215229.Google Scholar
Falter, J. 1986. Wahlen und Abstimmungen in der Weimarer Republik. Munich: C.H. Beck.Google Scholar
Falter, J. 1991. Hitlers Wähler. Munich: C.H. Beck.Google Scholar
Falter, J., and Gruner, W. 1981. “Minor and Major Flaws of a Widely-Used Data Set: The ICPSR ‘German Weimar Republic Data, 1919-1933 Under Scrutiny.”’ Historical Social Research 6:416.Google Scholar
Flint, C. 1995. The Political Geography of Nazism: The Spatial Diffusion of the Nazi Party Vote in Germany. Ph.D. dissertation, Department of Geography, University of Colorado-Boulder.Google Scholar
Fotheringham, A. S. 1997. “Trends in Quantitative Methods, I: Stressing the Local.” Progress in Human Geography 21:8896.Google Scholar
Fotheringham, A. S. 2000. “A Bluffer's Guide to ‘A Solution to the Ecological Inference Problem.’” Annals of the Association of American Geographers 90:582586.Google Scholar
Fotheringham, A. S., and Brunsdon, C. 1999. “Local Forms of Spatial Analysis.” Geographical Analysis 31:340358.CrossRefGoogle Scholar
Fotheringham, A. S., Brunsdon, C., and Charlton, M. 2000. Quantitative Geography: Perspectives on Spatial Analysis. London: Sage.Google Scholar
Freeman, M. 1995. Atlas of Nazi Germany: A Political, Economic and Social Anatomy of the Third Reich, 2nd ed. New York: Longman.Google Scholar
Software, Golden. 1999. Surfer 7.0 Users Guide: Contouring and 3-D—Surface Mapping for Scientists and Engineers. Golden, CO: Golden Software.Google Scholar
Gould, P. 1970. “Is statistix inferens the Geographical Name for a Wild Goose.Economic Geography 46, no. 2 (Supplement):439448.Google Scholar
Griffith, D. A., Doyle, P. G., Wheeler, D. C., and Johnson, D. L. 1998. “A Tale of Two Swaths: Urban Childhood Blood-Levels Across Syracuse, New York.” Annals of the Association of American Geographers 88:640665.Google Scholar
Griffith, D. A., and Layne, L. J. 1999. A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets. New York: Oxford University Press.Google Scholar
Grill, J. P. 1983. The Nazi Movement in Baden, 1920-1945. Chapel Hill: University of North Carolina Press.Google Scholar
Grill, J. P. 1986. “Local and Regional Studies on National Socialism.” Journal of Contemporary History 21:253294.Google Scholar
Hamilton, R. 1982. Who Voted for Hitler? Princeton, NJ: Princeton University Press.Google Scholar
Hänisch, D. 1989. “Inhalt und Struktur der Datenbank ‘Wahl- und Sozialdaten der Kreise und Gemeinden des Deutschen Reiches von 1920 bis 1933.’” Historical Social Research 14:3967.Google Scholar
Heilbronner, O. 1998. Catholicism, Political Culture, and the Countryside: A Social History of the Nazi Party in South Germany. Ann Arbor: University of Michigan Press.Google Scholar
Johnston, K., ver Hoef, J. M., Krivoruchko, K., and Lucas, N. 2001. Using ArcGisTM Geostatistical Analyst. Redlands, CA: ESRI.Google Scholar
Johnston, R., and Pattie, C. 2000. “Ecological Inference and Entropy-Maximizing: An Alternative Estimation Procedure for Split-Ticket Voting.” Political Analysis 8:333345.Google Scholar
Jones, J. P., and Casetti, E., eds. 1991. Applications of the Expansion Method. New York: Routledge.Google Scholar
Jones, K., and Duncan, C. 1998. “Modelling Context and Heterogeneity: Applying Multilevel Models.” In Research Strategies in the Social Sciences, eds. Scarbrough, E., and Tanenbaum, E. Oxford: Oxford University Press, pp. 95123.Google Scholar
Kaluzny, S. P., Vega, S. C., Cardoso, T. P., and Shelly, A. A. 1998. S+ Spatial Stats: User's Manual for Windows® and Unix® . New York: Springer-Verlag.Google Scholar
Kater, M. H. 1983. The Nazi Party: A Social Profile of Members and Leaders, 1919-1945. Cambridge, MA: Harvard University Press.Google Scholar
Key, V. O. 1949. Southern Politics in State and Nation. New York: Vintage Books.Google Scholar
King, G. 1996. “Why Context Should Not Count.” Political Geography 15:159164.Google Scholar
King, G. 1997. A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data. Princeton, NJ: Princeton University Press.Google Scholar
Küchler, M. 1992. “The NSDAP Vote in the Weimar Republic: An Assessment of the State-of-the-Art in View of Modern Electoral Research.” Historical Social Research 17:2251.Google Scholar
Laponce, J. A. 1980. “Political Science: An Import-Export Analysis of Journals and Footnotes.” Political Studies 28:410419.Google Scholar
Mantel, N. 1967. “The Detection of Disease Clustering and a Generalized Regression Approach.” Cancer Research 27:209220.Google Scholar
Murray, C. J. L., King, G., Lopez, A. D., Tomijima, N., and Krug, E. G. 2002. “Armed Conflict as a Public Health Problem.” BMJ Journal 324(9 February): 346349.CrossRefGoogle ScholarPubMed
Oden, N. L., and Sokal, R. R. 1986. “Directional Autocorrelation: An Extension of Spatial Correlograms in Two Dimensions.” Systematic Zoology 35:608617.Google Scholar
O'Loughlin, J. 1986. “Spatial Models of International Conflict: Extending Theories of War Behavior.” Annals of the Association of American Geographers 76:6380.Google Scholar
O'Loughlin, J. 2000. “Can King's Ecological Inference Method Answer a Social Scientific Puzzle: Who Voted for the Nazi Party in Weimar Germany?Annals of the Association of American Geographers 90:592601.Google Scholar
O'Loughlin, J. Forthcoming. “Democratic Values, Trust, and Geographic Context: A Multi-Level Analysis of the World Values Survey Data, 1990-1997.” In Interrogating the Globalization Project, ed. Honey, R. Iowa City: University of Iowa Press.Google Scholar
O'Loughlin, J., and Anselin, L. 1991. “Bringing Geography Back to the Study of International Relations: Spatial Dependence and Regional Contexts in Africa, 1966-1978.” International Interactions 17:2961.CrossRefGoogle Scholar
O'Loughlin, J., Flint, C., and Anselin, L. 1994. “The Geography of the Nazi Vote: Context, Confession and Class in the Reichstag Election of 1930.” Annals of the Association of American Geographers 84:351380.Google Scholar
O'Loughlin, J., Flint, C., and Shin, M. 1995. “Regions and Milieux in Weimar Germany: The Nazi Party Vote of 1930 in Geographic Perspective.” Erdkunde 49:305314 (and 4 map supplements).Google Scholar
O'Loughlin, J., Kolossov, V., and Vendina, O. 1997. “The Electoral Geographies of a Polarizing City: Moscow, 1993-1996.” Post-Soviet Geography and Economics 38:567600.Google Scholar
Ord, J. K., and Getis, A. 1995. “Local Spatial Autocorrelation Statistics: Distributional Issues and an Application.” Geographical Analysis 27:286296.Google Scholar
Passchier, N. 1980. “The Electoral Geography of the Nazi Landslide.” In Who Were the Fascists? eds. Larsen, S. U., Hagtvet, B., and Myklebust, J. P. Bergen: Universitetsforlaget, pp. 283300.Google Scholar
Pollock, J. 1944. “An Areal Study of the German Electorate, 1930-1933.” American Political Science Review 38:8995.Google Scholar
Rogerson, P. A. 2000. Statistical Methods for Geography. Thousand Oaks, CA: Sage.Google Scholar
Rohe, K. 1990. “German Elections and Party Systems in Historical and Regional Perspective: An Introduction.” In Elections, Parties and Political Traditions: Social Foundations of German Parties and Political Traditions, 1867-1987, ed. Rohe, K. New York: Berg, pp. 125.Google Scholar
Rosenberg, M. S. 2000. “The Bearing Correlogram: A New Method of Analyzing Directional Spatial Autocorrelation.” Geographical Analysis 32:267278.Google Scholar
Rosenberg, M. S. 2002. PASSAGE: Pattern Analysis, Spatial Statistics, and Geographic Exegesis. Version 1.0. Tempe, AZ: Department of Biology, Arizona State University. (Available from www.public.asu.edu/∼mrosenb/PASSAGE/.) Google Scholar
Rosenberg, M. S., Sokal, R. R., Oden, N. L., and DiGiovanni, D. 1999. “Spatial Autocorrelation of Cancer in Western Europe.” European Journal of Epidemiology 15:1522.Google Scholar
Shin, M. 2001. “The Politicization of Place in Italy.” Political Geography 20:331353.Google Scholar
Siverson, R. M., and Starr, H. 1991. The Diffusion of War: A Study of Opportunity and Willingness. Ann Arbor: University of Michigan Press.CrossRefGoogle Scholar
Sokal, R. R., and Thompson, B. A. 1998. “Spatial Genetic Structure of Human Populations in Japan.” Human Biology 70:122.Google Scholar
Stachura, P. D. 1980. “The Political Strategy of the Nazi Party, 1919-1933.” German Studies Review 3:261288.Google Scholar
Starr, H. 2002. “Opportunity, Willingness and Geographic Information Systems (GIS): Reconceptualizing Borders in International Relations.” Political Geography 21:243261.Google Scholar
Stögbauer, C. 2001. Wählerverhalten und nationalsozialistische Machtergreifung: Ökonomische, soziostrukturelle, räumliche Determinanten sowie kontrafaktische Politiksimulation. St. Katherinen: Scripta Mercaturae Verlag.Google Scholar
Stone, N. 1982. “Pillars of the Third Reich.” New York Review of Books 29:2426.Google Scholar
Tobler, W. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46:234240.CrossRefGoogle Scholar
Tobler, W. 1976. “Spatial Interaction Patterns.” Journal of Environmental Systems VI:271301.Google Scholar
Ward, M. D., ed. 1992. The New Geopolitics. New York: Gordon and Breach.Google Scholar
Womble, W. H. 1951. “Differential Systematics.” Science 114:315322.Google Scholar
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