Please note: This course will be taught in hybrid mode. Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously.

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.

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:

  1. What is GIS?
    1. Overview of spatial analysis in social science research.
    2. Key concepts: coordinate reference systems (CRS), spatial data types (vector vs. raster).
  2. Working with Spatial Data in R
    1. Introduction to key R packages (sf, sp, raster, ggplot2).
    2. Importing shapefiles, GeoJSON, and CSVs with spatial coordinates.
  3. Hands-On: Exploring Spatial Data
    1. Loading and visualizing geographic datasets.
  4. Discussion: Ethical Considerations in Spatial Analysis
    1. 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:

  1. Creating Maps with ggplot2 and tmap
    1. Thematic maps, choropleth maps, and basemaps.
    2. Adjusting color schemes, labels, and legends for clarity.
  2. Adding Context to Maps
    1. Overlaying additional data layers (e.g., demographic, political, or economic indicators).
    2. Working with geospatial boundaries (e.g., country, region, municipality).
  3. Hands-On: Creating a Custom Map
    1. Participants create their own maps using real-world social science data.
  4. Discussion: Communicating Spatial Findings
    1. 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:

  1. Data Cleaning and Preprocessing
    1. Handling missing spatial data and correcting projection issues.
    2. Transforming coordinate reference systems (CRS).
  2. Geoprocessing in R
    1. Spatial joins (merging point, line, and polygon data).
    2. Buffering, clipping, and intersecting spatial features.
  3. Hands-On: Conducting Spatial Joins and Analysis
    1. Practical exercises with demographic or political datasets.
  4. Discussion: Common Pitfalls in GIS Analysis
    1. 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:

  1. Measuring Spatial Relationships
    1. Spatial autocorrelation (Moran’s I, Geary’s C).
    2. Hotspot analysis and clustering methods.
  2. Geocoding and Network Analysis
    1. Converting addresses to coordinates and analyzing spatial networks.
  3. Hands-On: Conducting Spatial Analysis in R
    1. Participants work with real-world spatial data to analyze geographic trends.
  4. Discussion: The Role of Spatial Analysis in Policy and Research
    1. 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:

  1. Incorporating Context into Models
    1. Spatial regression and geographically weighted regression (GWR).
    2. Considering and incorporating space as a contextual moderator.
  2. LLMs and GIS
    1. Review how LLMs, like ChatGPT, can be used to enhance GIS-based research designs.
  3. Hands-On: Mini-Project Presentation
    1. Participants present a short analysis using GIS techniques learned in the course.
  4. Trends in GIS for Social Science
    1. Remote sensing, big spatial data, web-based mapping tools, and disaggregated analyses.

Background Knowledge:

Maths

Linear Regression – Elementary

Statistics

OLS – Moderate
Maximum likelihood – Elementary

Software

R – Elementary

Software used for teaching: R