Data Visualization in R and Python

Part IVSpatial Data and Geographic Maps

The visualization of spatial data and geographical maps represents a broad and relatively recent area of data visualization which, for some aspects, is close and sometimes partially overlaps traditional cartography and geographical maps produced with Geographical Information Systems (GISs). In this last part of the book, we introduce the main techniques available in R and Python environments, while cartographic techniques and GISs remain out of the scope, being a technical and scientific sector clearly distinct from data visualization and data science with its own peculiarities, skills, and practices.

With regards to data science and visualization, in recent years choropleth maps have become popular on the press, the web, or other publications, including professional and corporate material, and their typical look, with colored regions on a map to indicate differences with respect to a certain phenomenon, should be now familiar to many readers. Choropleth maps are generally easy to produce, both for the diffusion of open and proprietary tools to produce them and for spatial data with geographic information. Tools made available in R and Python, however, permit to work on far more complex and rich geographical representations than choropleth maps, such as managing cartographic shape files, creating maps with several layers of geographical information, executing complex operations on spatial data, or introduce interactive widgets in web-based ...