Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-06T09:20:21.655Z Has data issue: false hasContentIssue false

2 - Politics in Space

Methodological Considerations for Taking Space Seriously in Subnational Research

from Part I - Issues of Method and Research Design

Published online by Cambridge University Press:  13 June 2019

By
Imke Harbers  and
Matthew C. Ingram
Edited by
Agustina Giraudy ,
Eduardo Moncada  and
Richard Snyder
Agustina Giraudy
Affiliation:
American University, Washington DC
Eduardo Moncada
Affiliation:
Barnard College, Columbia University
Richard Snyder
Affiliation:
Brown University, Rhode Island
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Inside Countries
Subnational Research in Comparative Politics
 , pp. 57 - 91
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anselin, Luc. (1988). Spatial Econometrics: Methods and Models. Dordrecht, Netherlands: Springer Science-Business Media.Google Scholar
Anselin, Luc. (1995). Local indicators of spatial association – LISA. Geographical Analysis 27(2), 93–115.Google Scholar
Anselin, Luc, & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In Ullah, A. & Giles, D. E. A. (Eds.), Handbook of applied economic statistics, pp. 237–289. New York, NY: Marcel Dekker.Google Scholar
Anselin, Luc. (2005). Exploring spatial data with GeoDa: A workbook. Available at: https://geodacenter.asu.edu/og_tutorials (last accessed April 2, 2013).Google Scholar
Assunção, R., & Reis, E. A. (1999). A new proposal to adjust Moran’s I for population density. Statistics in Medicine 18(16), 2147–2161.Google Scholar
Baller, Robert D., Anselin, Luc, Messner, Steven F., Deane, Glenn, & Hawkins, Darnell F. (2001). Structural covariates of U.S. County homicide rates: Incorporating spatial effects. Criminology 39(3), 561–590.Google Scholar
Beck, Nathaniel, Gleditsch, Kristian Skrede, & Beardsley, Kyle. (2006). Space is more than geography: Using spatial econometrics in the study of political economy. International Studies Quarterly 50(1), 27–44.Google Scholar
Beck, Nathaniel L., & Katz, Jonathan N. (1996). Nuisance vs. substance: Specifying and estimating time-series-cross-section models. Political Analysis 6, 1–36.Google Scholar
Behrend, Jacqueline. (2011). The unevenness of democracy at the subnational level: Provincial closed games in Argentina. Latin American Research Review 46(1), 150–176.Google Scholar
Bivand, Roger, & Piras, Gianfranco. (2015). Comparing implementations of estimation methods for spatial econometrics. Journal of Statistical Software 63(18), 1–36.Google Scholar
Bivand, Roger, & Yu, Danlin. (2017). spgwr: Geographically Weighted Regression. R package version 0.6–31. Available at: https://CRAN.R-project.org/package=spgwr (last accessed December 7, 2018).Google Scholar
Cederman, Lars-Erik, & Gleditsch, Kristian Skrede. (2009). Introduction to special issue on “disaggregating civil war.” Journal of Conflict Resolution 53(4), 487–495.Google Scholar
Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and applications. London, England: Pion Ltd.Google Scholar
Darmofal, David. (2006). The political geography of macro-level turnout in American political development. Political Geography 25(2), 123–150.Google Scholar
Darmofal, David. (2008). The political geography of the New Deal realignment. American Politics Research 36(6), 934–961.Google Scholar
Darmofal, David. (2015). Spatial analysis for the social sciences. New York, NY: Cambridge University Press.Google Scholar
Deane, Glenn, Messner, Steven F., Stucky, Thomas D., McGeever, Kelly, & Kubrin, Charis. (2008). Not “islands, entire of themselves”: Exploring the spatial context of city-level robbery rates. Journal of Quantitative Criminology 24(4), 363–380.Google Scholar
Doreian, Patrick. (1980). Linear models with spatially distributed data: Spatial disturbances or spatial effects? Sociological Methods & Research 9(1), 29–60.Google Scholar
Doreian, Patrick. (1982). Maximum likelihood methods for linear models: Spatial effect and spatial disturbance terms. Sociological Methods & Research 10(3), 243–269.Google Scholar
Dow, Malcolm M. (2007). Galton’s problem as multiple network autocorrelation effects. Cross-Cultural Research 41(4), 336–363.Google Scholar
Elhorst, J. Paul. (2010). Applied spatial econometrics: Raising the bar. Spatial Economic Analysis 5(1), 9–28.Google Scholar
Fearon, James D., & Laitin, David D. (2003). Ethnicity, insurgency, and civil war. American Political Science Review 97(1), 75–90.Google Scholar
Fotheringham, A. Stewart, Brunsdon, Chris, & Charlton, Martin. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Chichester, England: Wiley and Sons.Google Scholar
Fotheringham, A. Stewart, Charlton, Martin, & Brunsdon, Chris. (1998). Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30(11), 1905–1927.Google Scholar
Franzese, Robert Jr., & Hays, Jude C. (2008a). Spatial interdependence in comparative politics: Substance, theory, empirics, substance. Comparative Political Studies 41(4/5), 742–780.Google Scholar
Franzese, Robert Jr., & Hays, Jude C. (2008b). Contagion, common exposure, and selection: Empirical modeling of theories and substance of interdependence in political science. Concepts & Methods 4(2), 2–8.Google Scholar
Gervasoni, Carlos. (2010). Measuring variance in subnational regimes: Results from an expert-based operationalization of democracy in the Argentine provinces. Journal of Politics in Latin America 2(2), 13–52.Google Scholar
Gibson, Edward L., & Suárez-Cao, Julieta. (2010). Federalized party systems and subnational party competition: Theory and an empirical application to Argentina. Comparative Politics 43(1), 21–39.Google Scholar
Giraudy, Agustina. (2010). The politics of subnational undemocratic regime reproduction in Argentina and Mexico. Journal of Politics in Latin America 2(2), 53–84.Google Scholar
Giraudy, Agustina. (2013). Varieties of subnational undemocratic regimes: Evidence from Argentina and Mexico. Studies in Comparative International Development 48(1), 1–30.Google Scholar
Gleditsch, Kristian Skrede, & Ward, Michael D. (2005). Visualization in international relations. In Mintz, Alex & Russett, Bruce (Eds.), New directions for international relations: Confronting the method-of-analysis problem, pp. 65–91. Lanham, MD: Lexington Books.Google Scholar
Gleditsch, Kristian Skrede, & Weidmann, Nils B. (2012). Richardson in the information age: Geographic information systems and spatial data in international studies. Annual Review of Political Science 15(1), 461–481.Google Scholar
Gleditsch, Kristian, & Ward, Michael. (2000). War and peace in time and space: The role of democratization. International Studies Quarterly 44(1), 1–29.Google Scholar
Goertz, Gary. (2006). Social science concepts: A user’s guide. Princeton, NJ: Princeton University Press.Google Scholar
Gollini, Isabella, Lu, Binbin, Charlton, Martin, Brunsdon, Christopher, & Harris, Paul. (2013). GWmodel: An R package for exploring spatial heterogeneity using geographically weighted models. arXiv:1306.0413.Google Scholar
Griffith, Daniel A. (1987). Spatial autocorrelation: A primer. Washington DC: Association of American Geographers.Google Scholar
Hafner-Burton, Emilie M., Kahler, Miles, & Montgomery, Alexander H. (2009). Network analysis for international relations. International Organization 63(3), 559–592.Google Scholar
Harbers, Imke, & Ingram, Matthew C. (2014). Democratic institutions beyond the nation state: Measuring institutional dissimilarity in federal countries. Government and Opposition 49(1), 24–46.Google Scholar
Harbers, Imke, & Ingram, Matthew C. (2017a). Geo-nested analysis: Mixed-methods research with spatially dependent data. Political Analysis 75(3), 289–307.Google Scholar
Harbers, Imke, & Ingram, Matthew C. (2017b). Incorporating space in multimethod research: Combining spatial analysis with case-study research. PS: Political Science & Politics 50(4), 1032–1037.Google Scholar
Harbers, Imke. (2014). States and strategy in new federal democracies: Competitiveness and intra-party resource allocation in Mexico. Party Politics 20(6), 823–835.Google Scholar
Harbers, Imke. (2015). Taxation and the unequal reach of the state: Mapping state capacity in Ecuador. Governance 28(3), 373–391.Google Scholar
Hilgers, Tina. (2011). Clientelism and conceptual stretching: Differentiating among concepts and among analytical levels. Theory and Society 40(5), 567–588.Google Scholar
Hooghe, Liesbet, & Marks, Gary. (2016). Community, scale and regional governance. Oxford, England: Oxford University Press.Google Scholar
Huber, Gregory A., & Arceneaux, Kevin. (2007). Identifying the persuasive effects of presidential advertising. American Journal of Political Science 51(4), 957–977.Google Scholar
Huckfeldt, R. Robert, & Sprague, John. (1995). Citizens, politics and social communication: Information and influence in an election campaign. New York, NY: Cambridge University Press.Google Scholar
Ingram, Matthew C. (2013). Elections, ideology, or opposition? Assessing competing explanations of judicial spending in the Mexican states. Journal of Law, Economics, and Organization 29(1), 178–209.Google Scholar
Ingram, Matthew C. (2014). The local educational and regional economic foundations of violence: A spatial analysis of homicide in Mexico’s municipalities. Mexico Institute Working Paper Series, Woodrow Wilson International Center for Scholars, Washington, DC.Google Scholar
Ingram, Matthew C. (2016). Crafting courts in new democracies: The politics of subnational judicial reform in Brazil and Mexico. Cambridge, England: Cambridge University Press.Google Scholar
Ingram, Matthew C., & da Costa, Marcelo Marchesini. (2017). A spatial analysis of homicide across Brazil ’s municipalities. Homicide Studies 21(2), 87–110.Google Scholar
Keele, Luke, & Titiunik, Rocio. (2015). Geographic boundaries as regression discontinuities. Political Analysis 23(1), 127–155.Google Scholar
King, Gary. (1997). A solution to the ecological inference problem. Princeton, NJ: Princeton University Press.Google Scholar
Kopstein, Jeffrey, & Reilly, David A. (2000). Geographic diffusion and the transformation of the postcommunist world. World Politics 53(1), 1–37.Google Scholar
Krasno, Jonathan S., & Green, Donald P. (2008). Do televised presidential ads increase voter turnout? Evidence from a natural experiment. The Journal of Politics 70(1), 245–261.Google Scholar
Land, Kenneth C., McCall, Patricia L., & Cohen, Lawrence E. (1990). Structural covariates of homicide rates: Are there any invariances across time and social space? American Journal of Sociology 95(4), 922–963.Google Scholar
Lankina, Tomila, & Getachew, Lullit. (2006). A geographic incremental theory of democratization: Territory, aid, and democracy in postcommunist regions. World Politics 58, 536–582.Google Scholar
LeSage, James P., & Pace, R. Kelley. (2010). Spatial econometric models. In Fischer, Manred M. & Getis, Arthur (Eds.). Handbook of applied spatial analysis: Software tools, methods, and applications, pp. 355–376. Berlin, Germany: Springer.Google Scholar
Logan, John R., Zhang, Weiwei, & Xu, Hongwei. (2010). Applying spatial thinking in social science research. GeoJournal 75(1), 15–27.Google Scholar
Manski, Charles F. (1993). Identification of endogenous social effects: The reflection problem. The Review of Economic Studies 60(3), 531–542.Google Scholar
Mears, Daniel P., & Bhati, Avitash S. (2006). No community is an island: The effects of resource deprivation on urban violence in spatially and socially proximate communities. Criminology 44(3), 509–548.Google Scholar
Messner, Steven F., Anselin, Luc, Baller, Robert D., Hawkins, Darnell F., Deane, Glenn, & Tolnay, S. E. (1999). The spatial patterning of county homicide rates: An application of exploratory spatial data analysis. Journal of Quantitative Criminology 15(4), 423–450.Google Scholar
Messner, Steven F., Jr., Teske, Raymond, H.C., Baller, Robert D., & Thome, Helmut. (2011). Structural covariates of violent crime rates in Germany: Exploratory spatial analyses of Kreise. Justice Quarterly, 30(6), 1015–1041. doi:10.1080/07418825.2011.645862Google Scholar
Moncada, Eduardo, & Snyder, Richard. (2012). Subnational comparative research on democracy: Taking stock and looking forward. Newsletter of the Comparative Democratization Section of the American Political Science Association 10(1), 1–9.Google Scholar
Moran, Patrick Allen Pierce. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society B, 10, 243–251.Google Scholar
Neumayer, E., & Plümper, T. (2016). W. Political Science Research and Methods 4(1), 175–193.Google Scholar
O’Donnell, Guillermo. (1999). Counterpoints: Selected essays on authoritarianism and democratization. Notre Dame, CA: University of Notre Dame Press.Google Scholar
Parker, Robert Nash, & Asencio, Emily K. (2009). GIS and spatial analysis for the social sciences: Coding, mapping and modeling. New York, NY: Routledge.Google Scholar
PySAL Developers. (2017). PySAL—Python Spatial Analysis Library. Release 1.13.0 (May 2017). Available at: http://pysal.github.io/index.html (last accessed June 14, 2017).Google Scholar
Python Software Foundation. (2016). Python. Version 3.5.2. Available at: www.python.org/ (last accessed June 14, 2017).Google Scholar
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: www.R-project.org/ (last accessed December 10, 2018).Google Scholar
Rey, S. J., & Anselin, L. (2007). PySAL: a python library of spatial analytical methods. Review of Regional Studies 37(1), 5–27.Google Scholar
Rodrigues-Silveira, Rodrigo. (2013). The subnational method and social policy provision: Socioeconomic context, political institutions and spatial inequality. Working Paper No. 36. desiguALdades.net, Berlin, Germany.Google Scholar
Rokkan, Stein. (1970). Citizens, elections, parties. Oslo, Norway: Universitetsforlaget.Google Scholar
Sampson, Robert J., & Graif, Corina. (2009). Neighborhood social capital as differential social organization: Resident and leadership dimensions. American Behavioral Scientist 52(11), 1579–1605.Google Scholar
Sartori, Giovanni. (1970). Concept misformation in comparative politics. American Political Science Review 64(4), 1033–1053.Google Scholar
Sartori, Giovanni. (2005 [1976]). Parties and party systems: A framework for analysis. Colchester, England: ECPR Press.Google Scholar
Schedler, Andreas. (2014). The criminal subversion of Mexican democracy. Journal of Democracy 25(1), 5–18.Google Scholar
Snyder, Richard. (2001). Scaling down: The subnational comparative method. Studies in Comparative International Development 36(1), 93–110.Google Scholar
Snyder, Richard, & Durán-Martínez, Angélica. (2009). Does illegality breed violence? Drug trafficking and state-sponsored protection rackets. Crime, Law and Social Change 52(3), 253–273.Google Scholar
Tollefsen, Andreas Forø, Strand, Håvard, & Buhaug, Halvard. (2012). PRIO-GRID: A unified spatial data structure. Journal of Peace Research 49(2), 363–374.Google Scholar
Trelles, Alejandro, & Carreras, Miguel. (2012). Bullets and votes: Violence and electoral participation in Mexico. Journal of Politics in Latin America 4(2), 89–123.Google Scholar
Ward, Michael, & Gleditsch, Kristian Skrede. (2018). Spatial Regression Models. 2nd ed. Thousand Oaks, CA: SAGE Publications.Google Scholar
Weidmann, Nils B., Kuse, Doreen, & Gleditsch, Kristian Skrede. (2010). The geography of the international system: The cshapes dataset. International Interactions 36(1), 86–106.Google Scholar
Wheeler, David. (2013). gwrr: Fits geographically weighted regression models with diagnostic tools. R package version 0.2–1. Available at: https://CRAN.R-project.org/package=gwrr (last accessed December 10, 2018).Google Scholar
Wickham, Hadley. (2009). ggplot2: Elegant graphics for data analysis. New York, NY: Springer-Verlag.Google Scholar
Yang, Tse-Chuan, Noah, Aggie J., & Shoff, Carla. (2015). Exploring geographic variation in S.S. mortality rates using a spatial durbin approach. Population, Space, and Place 21(1), 18–37.Google Scholar
Zhukov, Yuri M. (2012). Roads and the diffusion of insurgent violence: The logistics of conflict in Russia’s North Caucasus. Political Geography 31(3), 144–156.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×