Please note: This course will be taught online only. In person study is not available for this course.
Charles Crabtree is an Assistant Professor in the Department of Government at Dartmouth College, director of the Fundamental Needs Lab, co-founder and past co-director of the Baltic LEAP foreign study program, and current co-director of the Department of Government’s Honors Program.
Course Content
This five-day intensive course provides social science researchers with a hands-on introduction to Geographic Information Systems (GIS) using R. Participants will learn how to collect, manipulate, visualize, and analyze spatial data to answer research questions. The course emphasizes practical applications, ensuring that attendees develop the technical skills needed to incorporate spatial analysis into their work. By the end of the course, participants will be equipped to conduct basic GIS analysis, create informative maps, incorporate geographic context into their research designs and analyses, and critically assess spatial research in social science research.
Background Knowledge Required
Maths
Calculus – Moderate
Linear Regression – Elementary
Statistics
OLS – Elementary
Software
R – Elementary
Software used for teaching: R
Day 1: Introduction to GIS and Spatial Data in R
Objectives:
• Understand the fundamentals of GIS and spatial data structures.
• Learn how to import and manage spatial data in R.
Session Breakdown:
- What is GIS?
Overview of spatial analysis in social science research.
Key concepts: coordinate reference systems (CRS), spatial data types (vector vs. raster).
- Working with Spatial Data in R
Introduction to key R packages (sf, sp, raster, ggplot2).
Importing shapefiles, GeoJSON, and CSVs with spatial coordinates.
- Hands-On: Exploring Spatial Data
Loading and visualizing geographic datasets.
- Discussion: Ethical Considerations in Spatial Analysis
Privacy, data accuracy, and potential biases in GIS research.
Day 2: Mapping and Spatial Visualization
Objectives:
• Learn to create effective visualizations of spatial data in R.
• Understand best practices for mapping in social science research.
Session Breakdown:
- Creating Maps with ggplot2 and tmap
Thematic maps, choropleth maps, and basemaps.
Adjusting color schemes, labels, and legends for clarity.
- Adding Context to Maps
Overlaying additional data layers (e.g., demographic, political, or economic indicators).
Working with geospatial boundaries (e.g., country, region, municipality).
- Hands-On: Creating a Custom Map
Participants create their own maps using real-world social science data.
- Discussion: Communicating Spatial Findings
Designing maps for publication and policy reports.
Day 3: Spatial Data Wrangling and Geoprocessing
Objectives:
• Learn how to manipulate and process spatial data in R.
• Perform spatial joins, intersections, and distance calculations.
Session Breakdown:
- Data Cleaning and Preprocessing
Handling missing spatial data and correcting projection issues.
Transforming coordinate reference systems (CRS).
- Geoprocessing in R
Spatial joins (merging point, line, and polygon data).
Buffering, clipping, and intersecting spatial features.
- Hands-On: Conducting Spatial Joins and Analysis
Practical exercises with demographic or political datasets.
- Discussion: Common Pitfalls in GIS Analysis
Data precision, modifiable areal unit problem (MAUP), and ecological fallacy.
Day 4: Spatial Analysis for Social Science
Objectives:
• Conduct spatial statistics and pattern analysis.
• Understand how spatial relationships impact research findings.
Session Breakdown:
- Measuring Spatial Relationships
Spatial autocorrelation (Moran’s I, Geary’s C).
Hotspot analysis and clustering methods.
- Geocoding and Network Analysis
Converting addresses to coordinates and analyzing spatial networks.
- Hands-On: Conducting Spatial Analysis in R
Participants work with real-world spatial data to analyze geographic trends.
- Discussion: The Role of Spatial Analysis in Policy and Research
Case studies from political science, sociology, and economics.
Day 5: Context, LLMs, and Trends
Objectives:
• Explore how to incorporate space into empirical models.
• Using AI to supplement GIS-based research designs.
• Examine recent advances in GIS for social science.
Session Breakdown:
- Incorporating Context into Models
Spatial regression and geographically weighted regression (GWR).
Considering and incorporating space as a contextual moderator.
- LLMs and GIS
Review how LLMs, like ChatGPT, can be used to enhance GIS-based research designs.
- Hands-On: Mini-Project Presentation
Participants present a short analysis using GIS techniques learned in the course.
- Trends in GIS for Social Science
Remote sensing, big spatial data, web-based mapping tools, and disaggregated analyses.
