Spatial Economics and Big Data
Module Code: ECP77443
- ECTS Credit: 5
- Mandatory/Optional: Optional
- Semester/Term Taught: Michaelmas Term
- Module Coordinator: Professor Martina Kirchberger
Aims of Module
The module aims to introduce students to the use of spatial data in economics research.
Learning Outcomes
On completion of the module, students will be able to:- describe recent trends using spatial data in economics research
- understand a range of methods using spatial data
- critically evaluate whether and how spatial data can assist in answering a particular research question
- know of the possible sources of spatial data and possible applications
- conscientiously build their own spatial dataset
Module Content
The use of spatial data has become increasingly popular in economics research. Micro-surveys now routinely collect GPS coordinates of households and communities, satellites provide real-time measurements of night-time luminosity, and geo-referenced historic maps are linked to outcomes both across long time spans and space.
Spatial data serve in general two main purposes. First, they allow measuring outcomes that are otherwise hard to measure. Second, they aid identification of causal effects by, for example, controlling for covariates, enabling the construction of instruments, or exploiting boundaries. In the first part of the course, we will discuss how papers are using geo-referenced data, focusing on the role of spatial data in answering research questions. The second part of the course will be hands on: we will cover basic spatial tools, such as creating datasets on our own, merging spatial datasets, computing distances and the basics of map algebra.
Recommended Reading List
A full reading list will be provided at the start of lectures.
Assessment
- 50% Project
- 50% Group coursework: referee report
Module Website
Blackboard