Political Science 000 Spatial and Network Analysis TTh 0:00 - 0:00 PM, Room: TBD

Instructor: Yuri M. Zhukov, GCIS E-207, Phone: 617-495-0989

Office Hours: 0:00-0:00 AM, TTh and by appointment.

Course Description: This course offers an introduction to two inter-related topics: (1) geograph- ical information systems and (2) network analysis. Both topics are built on the proposition that the behavior and attributes of individual units (e.g. people, organizations, cities, countries) depend on their physical or social context. The goal of the course is to provide students with the background necessary to visualize, manipulate and analyze spatially-referenced or network data, evaluate liter- ature that employs these methods, and apply these methods to their own research. Our focus will be on methods and topics of most direct relevance to political science, including voting, conflict, migration, organizational learning, and international political economy.

The course is organized into one 80-minute lecture/discussion per week, and one 80-minute soft- ware tutorial. The lectures will cover and basic spatial/network , with an emphasis on applications in the social sciences. After week 3, each lecture will be followed by 5-10 minute student presentations on assigned readings. The tutorials are designed to equip students with the practical tools needed to collect, manage and visualize geostatistical and network data, and implement basic statistical analyses and models.

Prerequisites: Students are expected to have a rudimentary background in statistics, up to and including linear regression. Experience with statistical computing (e.g. MATLAB, R, S-PLUS, SAS, SPSS, STATA) is also helpful. Students without this background must obtain the instructor’s permission prior to enrolling in the course. We will try to cover the main topics without using complex , but will provide pointers to students who want to explore them in more technical depth.

Software: We will use the R statistical programming language for all tutorials and problem sets. R is a free, cross-platform software environment for statistical computing and graphics. A background in R is helpful, but not required. Students who would like to get a head start are encouraged to download the software here (http://cran.us.r-project.org/), and consult the introductory tutorial (http://cran.r-project.org/doc/manuals/R-intro.pdf). Some students may prefer the slightly more user-friendly GUI, R Studio (http://www.rstudio.com/). Code and data for all tutorials will be made available through the course website. For additional background on statistical computing with R, see • Venables, W. N. and B. D. Ripley. 2002. Modern Applied Statistics with S, 4th ed. Springer.

Grade Policy: Grades will be based on 5 problem sets (40%), a final project (40%), and partic- ipation in classroom discussions (20%). The final project will be a term paper, 15-20 pp. (25-30 pp. for graduates). Paper topics – to be negotiated directly with the instructor – must involve an application of spatial or network analysis to a substantive topic of interest. Collaboration within study groups is permitted on problem sets, although students are required to submit individual homeworks. Projects may be executed individually or in small (2-3 person) groups, with the under- standing that higher expectations (i.e. tougher grading standards) will apply to group assignments.

Important Dates: Drop Deadline ...... Month Day Add Deadline ...... Month Day PS1 Due ...... Month Day PS2 Due ...... Month Day PS3 Due ...... Month Day Project Proposal Due ...... Month Day PS4 Due ...... Month Day PS5 Due ...... Month Day Final Project Due ...... Month Day

Class Schedule:

1. Course introduction ...... Day, Month, Year

Theory:

• Brody, H., Rip, M. R., Vinten-Johansen, P., Paneth, N., and Rachman, S. 2000. “Map- making and myth-making in Broad Street: the London cholera epidemic, 1854.” Lancet, 356(9223): pp. 64-68. • Tobler, W. R. 1970. “A computer movie simulating urban growth in the Detroit region.” Economic Geography, 46: 234-240.

Application:

• John Agnew. 1996. “Mapping politics: how context counts in electoral geography.” Political Geography, 15(2): 129-146. • Gary King. 1996. “Why context should not count.” Political Geography, 15: pp. 159- 164. • Ward, M. D., Stovel, K., and Sacks, A. 2011. “Network analysis and political science.” Annual Review of Political Science, 14: pp. 245-264.

Recommended:

• Anselin, L. 1988. Spatial econometrics: methods and models. Kluwer Academic Pub: chapter 1. • John Agnew. 1996. “Maps and models in political studies: a reply to comments.” Political Geography, 15(2): 165-167.

Tutorial: Introduction to R ...... Day, Month, Year 2. Basics of Geographic Analysis ...... Day, Month, Year

Theory: • LeSage, James and Robert K. Pace. 2010. Introduction to Spatial Econometrics. CRC Press, 2010: chapter 1. • Bivand, R. S., Pebesma, E. J., and Rubio, V. G. 2008. Applied spatial data: analysis with R. Springer: chapter 1. Application: • Brady, Henry E. and John E. McNulty. “Turning out to vote: The costs of finding and getting to the polling place.” American Political Science Review, 105. • Tam Cho, Wendy K. and James G. Gimpel. 2012. “Geographic information systems and the spatial dimensions of American politics.” Annual Review of Political Science, 15. Recommended: • Bivand, R. S., Pebesma, E. J., and Rubio, V. G. 2008. Applied spatial data: analysis with R. Springer: chapters 2-5. • Schabenberger, O. and Gotway, C.A. 2005. Statistical Methods for Spatial Data. Chap- man and Hall: chapter 1. Tutorial: Working with Spatial Data ...... Day, Month, Year 3. Basics of Network Analysis ...... Day, Month, Year

Theory: • Wasserman, Stanley and Katherine L. Faust. 1994. Analysis: Methods and Applications. New York: Cambridge University Press: chapter 4. • O’Malley, A. James and Peter V. Marsden. 2008. “The Analysis of Social Networks.” Health Services Outcomes and Research Methodology 8: 222-269. Application: • Tam Cho, Wendy K. 2003. “Contagion effects and ethnic contribution networks.” Amer- ican Journal of Political Science, 47(2). • Lazer, D. 2011. “Networks in political science: Back to the future.” PS: Political Science and Politics, 44(1): p. 61. Recommended: • Wasserman, S., and Galaskiewicz, J., Eds. 1994. Advances in : Research in the social and behavioral sciences. Sage. Tutorial: Working with Network Data ...... Day, Month, Year 4. Spatial Dependence ...... Day, Month, Year

Theory: • Leenders, Roger . 2002. “Modeling Social Influence Through Network Autocorrelation: Constructing the Weight Matrix.” Social Networks 24: 21-47. • Anselin, L. 1988. Spatial econometrics: methods and models. Kluwer Academic Pub: chapters 2-3.

Application:

• Zhukov, Y. M., and Stewart, B. M. 2013. “Choosing Your Neighbors: Networks of Diffusion in International Relations.” International Studies Quarterly (57): pp. 271- 287. • Weidmann, N. B., and Salehyan, I. 2013. “Violence and ethnic segregation: A computa- tional model applied to Baghdad.” International Studies Quarterly. • Gay, Claudine. 2012. “Moving to opportunity: The political effects of a housing mobility experiment.” Urban Affairs Review, 48(2).

Recommended:

• Haining, R. 1990. Spatial data analysis in the social and environmental sciences. Cam- bridge Univ. Press: chapter 8. • Bivand, R. S., Pebesma, E. J., and Rubio, V. G. 2008. Applied spatial data: analysis with R. Springer: chapter 9.

Tutorial: Measuring Spatial Dependence ...... Day, Month, Year

5. Network ...... Day, Month, Year

Theory:

• Wasserman and Faust, Social Network Analysis: chapter 5.

Applications:

• Hafner-Burton, E., Kahler, M. and Montgomery, A. 2009. “Network analysis for inter- national relations.” International Organization, 63: pp. 559-92. • Rich Nielsen. 2012. “Jihadi radicalization of Muslim Clerics.” Working paper.

Recommended:

• Marsden, P. V. 2002. “Egocentric and sociocentric measures of network centrality.” Social networks, 24(4): pp. 407-422. • Friedkin, N. E. 1991. “Theoretical foundations for centrality measures.” American journal of Sociology, pp. 1478-1504.

Tutorial: Measuring Network Centrality ...... Day, Month, Year

6. Point Processes and Geostatistics ...... Day, Month, Year

Theory: • Cressie, N., and Wikle, C. K. 2011. Statistics for spatio-temporal data. Wiley: chapter 4.

Application

• Agnew, John, Thomas W. Gillespie, Jorge Gonzalez, and Brian Min. “Baghdad nights: evaluating the U.S. military ‘Surge’ using nighttime light signatures.” Environment and Planning A, 40(10): 2285-2295. • Zammit-Mangion, A., Dewar, M., Kadirkamanathan, V., and Sanguinetti, G. 2012. “Point process modelling of the Afghan War Diary.” Proceedings of the National Academy of Sciences, 109(31): pp. 12414-12419.

Recommended:

• Diggle, P.J. 1983. Statistical Analysis of Spatial Point Patterns. Academic Press: chapter 1. • Chiles, J. and Delfiner, P. 1999. Geostatistics: Modeling Spatial Uncertainty. Wiley: chapters 2-3. • Bivand, R. S., Pebesma, E. J., and Rubio, V. G. 2008. Applied spatial data: analysis with R. Springer: chapters 7-8.

Tutorial: Point Processes ...... Day, Month, Year

7. Spatial and Network Regression ...... Day, Month, Year

• LeSage, James and Robert K. Pace. 2010. Introduction to Spatial Econometrics. CRC Press, 2010: chapters 2-3. • Anselin, L. 1988. Spatial econometrics: methods and models. Kluwer Academic Pub: chapter 4.

Application:

• Shalizi, C. R., and Thomas, A. C. 2011. “ and contagion are generically confounded in observational social network studies.” Sociological Methods and Research, 40(2): pp. 211-239. • Weidmann, N. B., and Ward, M. D. 2010. “Predicting conflict in space and time.” Journal of Conflict Resolution, 54(6): pp. 883-901.

Recommended:

• Banerjee, S., B. Carlin, and A. Gelfand. 2004. Bayesian and Hierarchical Modeling of Spatial Data: Hierarchical Modeling and Analysis for Spatial Data, Chapman and Hall: chapters 5, 7-9. • Schabenberger, O. and Gotway, C.A. 2005. Statistical Methods for Spatial Data. Chap- man and Hall: chapter 6. • Bivand, R. S., Pebesma, E. J., and Rubio, V. G. 2008. Applied spatial data: analysis with R. Springer: chapter 10.

Tutorial: Fitting spatial regressions ...... Day, Month, Year 8. Exponential models ...... Day, Month, Year

Theory:

• Goudreau, Steven M. 2007. “Advances in Exponential Random Graph (p*) Models, Applied to a Large Social Network.” Social Networks 29: 231-248. • Cranmer, S. J., and Desmarais, B. A. 2011. “Inferential network analysis with exponen- tial random graph models.” Political Analysis, 19(1): pp. 66-86.

Application:

• Leifeld, P., and Schneider, V. 2012. “Information exchange in policy networks.” Ameri- can Journal of Political Science, 56(3): pp. 731-744. • Cranmer, S. J., Desmarais, B. A., and Menninga, E. J. 2012. “Complex dependencies in the alliance network.” Conflict Management and Peace Science, 29(3): pp. 279-313. • Kinne, B. J. 2013. “Dependent Diplomacy: Signaling, Strategy, and Prestige in the Diplomatic Network.” International Studies Quarterly. Forthcoming.

Recommended:

• Snijders, T. A. 2011. “Statistical models for social networks.” Annual Review of Sociol- ogy, 37: pp. 131-153. • Poast, P. 2010. “(Mis) Using dyadic data to analyze multilateral events.” Political Analysis, 18(4): pp. 403-425. • Desmarais, B. A., and Cranmer, S. J. 2012. “Statistical inference for valued-edge net- works: the generalized exponential random graph model.” PloS one, 7(1): e30136.

Tutorial: Fitting ERGMs ...... Day, Month, Year

9. Student Presentations I ...... Day, Month, Year

10. Student Presentations II ...... Day, Month, Year