News

MeMa - Michael Shin - 17/5-15/06/2022

MeMa - Methodology Matters
Course
Geospatial Analysis for Social and Political Sciences

Michael Shin
UCLA - Department of Geography

17 May 2022 - 15 June 20221
Computer Room - NASP Graduate School
Via Pace, 10 - Milan

Abstract

This 20-hours course introduces key concepts and techniques in the use and application of geospatial analysis for political science research.

No prior experience with statistical computing is required or presumed.

Designed for those new to geospatial analysis, the content of the course covers essentials such as software, programming languages, and libraries for geospatial analysis, location analysis, basic thematic mapping techniques, multi-layer mapping, geoprocessing, and an introduction to exploratory spatial data analysis.

The first 4 hours of the course are devoted to introducing the basic elements of the geospatial analysis. The goal of this session is to increase participants' awareness about thinking spatially and geographic problem-solving.

Participants who want to get an idea of ​​what geospatial analysis consists of can attend only this module.

The rest of the course is a 'hands-on' workshop where participants will be installing software on their own computers, working with a variety of political data sets, and creating maps and associated geographic visualizations. 

 

Candidate participants are kindly requested to fill in the application form by May 9, 2022, specifying whether they want to attend only the first module or all modules. 

Faculty members are all welcome as observers.

 

For information, please contact  This email address is being protected from spambots. You need JavaScript enabled to view it.  

 

Program

Session 1: Thinking spatially - 17-18 May, h. 16.30-18.30

The goal of the first session is to increase participant awareness about thinking spatially and geographic problem solving. Exercises introduce the fundamentals of R, the tidyverse, and spatial data.

• Why the spatial is special?

• Tools of the trade: maps, statistical computing v. GIS, and R and the Tidyverse

• Exercise #1: How many Italies?

• Exercise #2: Making democracy work

 

Session 2: Geospatial Data Management

The focus of this session is to provide participants an overview of essential features of geospatial data. Exercises provide training on working with different data types and table joins.

• Raster or vector? Points, lines or polygons?

• Coordinates, tables, and tables of coordinates.

• Exercise #3: Selection & Queries – Where are...?

• Exercise #4: Distance and point pattern analysis – How far?

 

Session 3: Geospatial Political Analysis 1

Building upon the first two sessions, this session covers basic geoprocessing techniques and exploratory spatial data analyses.

• How to ask geospatial questions

• Exercise #6: Mapping elections

• Exercise #7: The politics of health – SARS-CoV-2 across Italy

 

Session 4: Geospatial Political Analysis 2

Building upon the first three sessions, this session covers spatial autorcorrelation and exploratory spatial data analyses.

• What is spatial autocorrelation and why does it matter?

• Exercise #6: Detecting and assessing spatial autocorrelation

• Exercise #7: Spatial regression techniques

r causal diagrams. However, it is very technically written.

Morgan, Stephen L. and Christopher Winship. (2007). Counterfactuals and Causal Inference: Methods and Principles of Social Research. Cambridge: Cambridge University Press.

Chapter 3 (p. 77-95) is a more applied discussion of directed graphs embedded in examples from sociology.

Robins, James M. (2001). “Data, Design, and Background Knowledge in Etiologic Inference,” Epidemiology 11 (3): 313-320.

            Another less technical description of directed graphs.

 

 

Part II – Necessary and Unnecessary Controls or Closing the Backdoor

Time: 10:45-12:15

 

Recommended Literature

Morgan, Stephen L. and Christopher Winship. (2007). Counterfactuals and Causal Inference: Methods and Principles of Social Research. Cambridge: Cambridge University Press.

Chapter 3 (p. 95-130) is an applied discussion of the back-door criteria.

Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: a primer. John Wiley & Sons.

            Chapter 3 (p. 53-75).

 

 

Lunch Break 12:15-13:30

 

 

Part III – Effect Heterogeneity and the Average Treatment Effect

Time 13:30-15:00

 

Recommended Literature

Elwert, F., & Winship, C. (2010). Effect heterogeneity and bias in main-effects-only regression models. Heuristics, probability and causality: A tribute to Judea Pearl, 327-36.

Falleti, T. G., & Lynch, J. F. (2009). Context and causal mechanisms in political analysis. Comparative political studies, 9(42), 1143-1166.

 

 

Part IV – Interactions versus Structural Equations with Mediation Effects

Time 15:15-17:15

 

Recommended Literature

Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: a primer. John Wiley & Sons.

            Chapter 3 (p. 75-87).

Brambor, T., Clark, W. R., & Golder, M. (2006). Understanding interaction models: Improving empirical analyses. Political analysis, 14(1), 63-82.

Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 1(25), 51-71.

 

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