88353 - GIS Tools Laboratory

Academic Year 2022/2023

  • Docente: Bruno Conte Leite
  • Credits: 3
  • SSD: SECS-P/01
  • Language: English
  • Teaching Mode: Traditional lectures
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Economics (cod. 8408)

Learning outcomes

  1. Implementing big data/data sciences tools to handle spatial (GIS) data with R.
  2. Apply these techniques to infer empirical patterns that relates to a research question.
  3. Have practice with real examples from academic literature (i.e. replicating patterns from existing papers and producing your own).
  4. Get in touch with the technical frontier when working with spatial data (e.g. coding in R, using its most modern libraries and tools, integrating code pipelines to build scalable routines).

Course contents

Please find the most updated course content here.

This course provides theory and tools to use geographical data in economic research. First, it (i) presents recent economic research that utilizes this type of data and (ii) stresses how doing so allows for addressing meaningful research questions. Second, the course introduces the technicalities related to the construction and use of geographic data using R.

Readings/Bibliography

A bibliography of the academic literature will be distributed in class. The technical references for using R to handle GIS data will comprise both the slides and codes provided by the instructor and:

- Lovelace, R., Nowosad, J. and Muenchow, J., 2019. Geocomputation with R. Chapman and Hall/CRC.

- Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009

- Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.".

Teaching methods

The teaching methods will be:

  1. Classes with discussions and papers and methods.
  2. Practice in laboratory-like sessions in class.

Note that participants are asked to have bring own laptop (or at least one every two people) to follow and replicate the work done by the instructor.

Assessment methods

The assessment methods and grading will follow:

  1. Replication assignments.

  2. Final essay (empirical research project).

  3. Class engagement.

Teaching tools

  1. R and RStudio for practice in class.
  2. Slides and notes.

Links to further information

https://brunoconteleite.github.io/02-gis-unibo/

Office hours

See the website of Bruno Conte Leite