Spatial Data in Economics: Theory and Tools

Contents of the course on spatial data (a.k.a. GIS Tools Laboratory)
Bruno Conte | b.conte@unibo.it
LMEC Master - Università di Bologna

Overview

This is a master/Ph.D. course on basic theory and tools for using spatial data in economic research.

All practical applications are taking place in R and RStudio. Please have both installed in your computer before starting the course.

Course structure

This is a 15 hours course, divided in five sessions of 3 hours. Its overall structure follows:

  • Session 1: Introduction to spatial data and data wrangling in R

    • Basic aspects of R (data.table, tidyverse, ggplot2), spatial data, and applications in economic research

    • Class slides in html and pdf

    • Hands-in material here

  • Session 2: Vector spatial data (points, lines, and polygons)

    • Loading and manipulating with sf. Basic principles of spatial (re)projections. Basic attribute data operations (e.g. filter(), slice()) with data.table and data visualization with ggplot2

    • Assignment: replicating maps from academic papers. Instructions here

    • Class slides in html and pdf

    • Hands-in material here

  • Session 3: Spatial data operations with vector data

    • Spatial Subsetting and topological relations (e.g. st_intersects(), st_overlaps(), st_touches()), spatial joining and aggregation (e.g. st_join(), aggregate()), and calculating distances (and spatial networks) with gdistance, rgeos, and igraph

    • Assignment: reproducing operations with proposed data and replicating results from academic papers. Instructions here

    • Class slides html and pdf

    • Hands-in material here

  • Session 4: Spatial geometry operations and miscelania

    • Basic operations with vector data (e.g. centroids, shift, rotate, buffering)

    • Time allowing: Reporting data work with R: a short introduction to rmarkdown

    • Class follow-up: feedback on course pace, reinforcing concepts, etc.

    • Assignment: reproducing operations with proposed data and replicating results from academic papers. Instructions here

    • Class slides html and pdf

    • Hands-in material here

  • Session 5: Raster data

    • Loading and manipulating raster data with terra. Basic raster operations (e.g. crop, vectorize, and rasterize)

    • Managing curse of dimensionality and large disk and memory needs for dealing with raster data

    • Raster-vector operations with terra and exactextractr (e.g. extracting, rasterizing, vectorizing, zonal statistics)

    • Distance over rasters: using rasters as frictions surfaces to calculate optimal paths with gdistance

    • Assignment: replication of results and spatial statistics. Instructions here

    • Class slides html and pdf

    • Hands-in material here

References

  • Donaldson, D. and Storeygard, A., 2016. The view from above: Applications of satellite data in economics. Journal of Economic Perspectives, 30(4), pp.171-98.

  • 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.”.