References

Books references

• Arbia, G. A primer for spatial econometrics with applications in R. Palgrave Macmillan, 2014.
• VanderPlas, Jake. Python data science handbook: Essential tools for working with data. O’Reilly Media, Inc., 2016. Website
• McKinney, Wes. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O’Reilly Media, Inc., 2012. Website
• Anselin, Luc, and Sergio Joseph Rey. Modern spatial econometrics in practice: A guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press LLC, 2014.
• Sergio J. Rey, Dani Arribas-Bel, Levi J. Wolf - Geographic Data Science with PySAL and the PyData Stack Website

Codes references

• Python Data Science Handbook: full text in Jupyter Notebooks https://github.com/jakevdp/PythonDataScienceHandbook
• A course on Geographic Data Science by Dani Arribas-Bel Website

Useful links

R

dplyr Official website https://dplyr.tidyverse.org/ ggplot2 Official website https://ggplot2.tidyverse.org/

tmap Official website https://r-tmap.github.io/tmap/

sf Official website https://r-spatial.github.io/sf/ sf cheatsheet

spdep Official website https://r-spatial.github.io/spdep/

spregression Official website https://r-spatial.github.io/spatialreg/

Python

Numpy

Official website https://numpy.org/

Numpy cheatsheet

Pandas

Official website https://pandas.pydata.org/

Pandas cheatsheet

Matplotlib

Official website https://matplotlib.org/

Matplotlib cheatsheet

Seaborn

Official website https://seaborn.pydata.org/

Seaborn cheatsheet

GeoPandas

Official website https://geopandas.org/index.html

pySAL

Official website https://pysal.org/

Carto Frames

Official website https://carto.com/developers/cartoframes/