Exercise 16 - Maps

Exercise 16 - Maps

Andrew Valentine, Louis Moresi, louis.moresi@anu.edu.au

Often in geosciences research, we wish to produce maps displaying spatial data. Python provides two sets of tools for handling mapping: Basemap and Cartopy. Basemap is currently probably more popular, but its developers have announced that it will shortly be phased out in favour of Cartopy. We will therefore use Cartopy in this course.

We only need certain submodules from Cartopy; we will do

import cartopy.crs as ccrs
import matplotlib.pyplot as plt

The second line here is, of course, just our familiar way of importing matplotlib for plotting.

Now, making a map is exactly the same as plotting in polar coordinates: we simply need to specify projection=Projection() when creating the plot. Here, Projection() is one of the Cartopy-supported map projections. For example,

ax = plt.subplot(111,projection=ccrs.PlateCarree())

The ax.set_global() command informs Cartopy that we want to make a map of the whole globe, while the ax.coastlines() command draws coastline information onto it.

# Try it here!

By default, the map is centred on the 0 longitude meridian. To change this, we can pass central_longitude=longitude to ccrs.PlateCarree().

Make a single figure that consists of multiple subplots, illustrating the following projections:

  • ccrs.PlateCarree

  • ccrs.Mercator

  • ccrs.Mollweide

  • ccrs.Robinson

  • ccrs.InterruptedGoodeHomolosine

  • ccrs.NearsidePerspective

# Try it here!

The maps we have made so far are global. If you only want to work with a subset of the globe, you can specify a different region (after creating the axes, but before doing any plotting) by removing ax.set_global() and instead calling ax.set_extent((llon,rlon,llat,ulat)) where llon and rlon are the longitudes of the left- and right-hand sides of the region, and llat and ulat are the lower and upper latitudes of the region. Remember to set central_longitude appropriately, or you may get surprising results.

Make a map of the area around your hometown. You may wish to pass resolution='50m' or resolution='10m' to ax.coastlines() to obtain a better-looking result.

# Try it here!

To add features such as rivers, national boundaries and so on to your map, we must import another submodule of Cartopy:

import cartopy.feature as cf

This provides immediate access to several low-resolution feature datasets, including:

  • cf.BORDERS


  • cf.LAND

  • cf.OCEAN

  • cf.LAKES

  • cf.RIVERS

Each of these can be added to your plot by calling ax.add_feature(feature); additional arguments such as color=colorname can be used to control how they are displayed.

# Try it here!

For higher-resolution data, and for other features, Cartopy allows you to make use of data from Natural Earth. Unfortunately, the documentation for doing so is currently rather poor, and getting everything to work can require some amount of trial and error. The basic syntax is:

ax.add_feature(cf.NaturalEarthFeature(category, name, scale),

where category is either 'physical' or 'cultural', scale is '10m', '50m' or '110m', and 'name' is the name of the appropriate dataset. It seems this has to be inferred from the ‘download’ links on the Natural Earth website. The color options, edgecolor and facecolor, specify the colour used to draw outlines and fills; for some reason, it is necessary to pass the text string 'none' (and not, as is more usual, the Python object None) if one does not want an object to be filled. Thus, for example, high-resolution rivers can be drawn by the command


Add rivers and country boundaries (as appropriate) to your hometown map.

# Try it here!

There is also an interface to GSHHS coastline data.

Because Cartopy is built as an add-on to matplotlib.pyplot, you can use all of the standard plotting tools to add data to your map. For example, point data and lines can be added using plt.plot(), contours can be drawn using plt.contour(), and gridded data using plt.imshow(). However, it is critically important that you include transform = <something appropriate> in each plotting command, to ensure that Cartopy correctly interprets the data you provide. For most common cases, where your data is expressed in terms of latitude and longitude, the most appropriate choice will be transform=ccrs.Geodetic(). When a geodetic coordinate system is used, using plt.plot() to draw a line between two points will result in a geodesic curve (the great-circle path representing the shortest distance on the surface of the spherical Earth). If you instead wish to draw a line that appears straight in the 2-D plane, you can use transform=ccrs.PlateCarree().

Create a global map and plot a line joining your hometown and Canberra using both transform=ccrs.PlateCarree() and transform=ccrs.Geodetic(). Satisfy yourself that you understand the difference. Plot (and label) the locations of your hometown, the capital city of your country, and Canberra.

# Try it here!

There is a more comprehensive guide to cartopy that you can find in the in-depth section under Mapping