Spatial Analysis of Food Security

Globally, over 2 billion people are suffering from moderate-to-severe food insecurity and the Sudan famine being the first famine declared in 2025 since 2020. One key aspect to understanding food insecurity is its spatial distribution and trends that contribute to how food secure a population is. This course will teach students how to collect and analyze spatial data related to food security, as well as touch on important topics in food insecurity. The course will focus on taking real-life food security questions and applying spatial analysis techniques to these questions. In the course, we will cover an introduction to spatial analysis, natural experiments in geography, applying remote sensing to food insecurity, climate shocks and food security, and seasonal forecasting and food security.  

This class will have a lecture aspect, which will mainly focus on topics in food security and how they relate to data collection, and a lab section, which will be an opportunity for students to collect data directly, clean the data, and analyze the data using the R programming language with spatial research methods. Example topics in class will be climate variability and food insecurity, policies that can successfully address food insecurity, and understanding spatial aggregation in food security statistics and how that can influence interpretation. These topics will then be further explored in the lab section of the class: specifically focusing on downloading weather data for time series analysis, using a convergence of datasets to map hotspots, and investigating how survey data intersects with spatial datasets.