Spatial Analysis of Food Security

Globally, there are over 2 billion people suffering from moderate-to-severe food insecurity, with an estimated 600 million people projected to be chronically undernourished by 2030. 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.

It will have an in-class 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, women’s role in agriculture and their rates of food insecurity relative to men, and population and health. 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.

In this course, there will be two components; a lecture and a lab. The lecture will be short and focus on relevant topics in Food Security and methodology used to quantitative analyze these topics. The lab will be a computer-based lab in R, analyzing relevant food security data using techniques discussed during the lecture to provide a practical base for quantitative analyses.