Geospatial Data Visualization: Reference

Key Points

Introduction
  • Visualizing data augments human cognition and, if done right, can lead to richer understanding of the available data.

  • Cartographic visualizations (maps) can draw significant insights from spatial data.

  • Data visualization is a tool to assist several steps of the researcher’s pipeline, from initial data exploration to communication of final results.

Basics: Projections
  • Map projections are interesting, complicated, and mostly handled for us.

  • Choosing a projection appropriately to match the intent of the visualization is critical to accurately conveying information (“how much distortion is OK?”).

Basics: Quick + Simple maps with cartopy.
  • Cartopy can easily manage projections.

  • A few simple modifications to matplotlib code (namely the projection keyword) can turn any matplotlib plot into a spatially-aware one.

Plotting Actual Things: geopandas and cartopy
(optional) Interactive Maps
(optional) Other Powerful Plotting Tools

FIXME: more reference material.