Mapping Economic Landscapes

Author

Alba Miñano-Mañero

Published

May 31, 2024

About this course

This course updates and extends material prepared for the courses “Introduction to Python” and “Geographic Data Science for Public Policy Evaluation” taught in 2024 at CEMFI and AiREF, respectively.

While the first course was designed as an introduction to Python, it has been included in this course to provide newcomers with a gentle introduction to coding with Python and to ease their transition into the rest of the material. The second course was intended to equip public policy evaluators with tools to effectively utilize the geographic dimension of data in their work. I am grateful to the students in both courses, as their class participation helped shape the content of this course.

By combining both courses and extending the material to more advanced topics, this course becomes a valuable resource for economists interested in applying geographic data science in their work, whether for research purposes or evaluation tasks. It covers both basic and advanced material, making it useful for users with varying levels of coding experience.

The course is structured as follows:

Part 1: Introduction to Python

  1. Jupyter Notebook foundations
  2. Basic programming syntax.
  3. NumPy
  4. Data management with pandas
  5. Data visualization with matplotlib and seaborn.

Part 2: Visualization of geographic data

  1. Introducing geographic data.
  2. Data visualization with QGIS.
  3. Data visualization with Python.

Part 3: Processing and analysis of geographic data

  1. Spatial Feature Engineering.
  2. Distance computation.
  3. Distributed computation.
  4. Geographic data analysis.
  5. Spatial Econometrics.
  6. Leveraging geography for identification.

Further resources:

While this book contains original material (unless otherwise indicated), it has also been structured and, in some cases, supplemented by material from various sources. The following list contains these resources:

  • Arribas-Bel (2019): A course in Geographic Data Science
  • Arribas-Bel, Puga (2024): Geographic Data Science for Applied Economists
  • Boeing (2024): Advanced Urban Analytics
  • GEOLab (2021): GIS4Schools
  • Krishna, Shi, Besler, Kuype (2022): Introduction to Data Science with Python
  • Marbet (2024): Data Science
  • McKinney (2022): Python for Data Analysis, 3E
  • Rey, Arribas-Bel, Wolf (2023): Geographic Data Science with Python
  • Tenkanen, Heikinheimo, Aagesen, Fink, Hasanzadeh (2024): Automating GIS Processes
  • Tenkanen, Heikinheimo, Whipp (2024): Python for Geographic Data Analysis
  • The Geo-Python Team (University of Helsinki) (2023): Geo-Python

The banner image is a capture of Harold Fisk’s Meander Maps of the Mississippi River, elaborated in 1944 for the US Army Corps of Engineers.

Suggested citation for materials of this course

@misc{minano_mapppingecon_course2024,
  author = {Alba Miñano-Mañero},
  title = {Mapping Economic Landscapes: A Course on Geographic Data Science},
  year = {2024}
}