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Change map projection in Python charts

Every map requires a projection to represent the 3D Earth on a 2D surface. This process is crucial as it influences the map's appearance and accuracy.

In Python, you can use matplotlib and cartopy to plot maps with different projections. This post will guide you through the essentials of map projections in Python, including how to change projections, use them in bubble maps, and explore available projection options.

For creating the charts, we will need to load the following libraries:

• matplotlib for creating the chart and the arrows
• pandas for loading the data
• geopandas for loading the world boundaries
• cartopy for changing the projections

import pandas as pd
import matplotlib.pyplot as plt

import geopandas as gpd
import cartopy.crs as ccrs

# set a higher resolution
plt.rcParams['figure.dpi'] = 300

# load the world dataset
url = "https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/all_world.geojson"
world = gpd.read_file(url)
world.head()

How can we make a 2D version of a 3D object like the Earth? This challenge is tackled through map projections, which transform the Earth's spherical surface into a flat map. Projections are crucial because they inevitably distort some aspects of the Earth's surface, such as shape, area, distance, or direction.

The choice of projection can significantly alter the message conveyed by a map; for instance, using a Mercator projection preserves direction but distorts the size of landmasses, making regions near the poles appear disproportionately large and potentially misleading viewers about the true scale of countries.

Let's check how the default matplotlib/geopandas map looks like by using the plot() method on our previous dataset:

fig, ax = plt.subplots(figsize=(12, 8))

ax.set_axis_off() # remove border around the axes
world.plot(ax=ax)

plt.show()

This projection is called PlateCarree. The default Matplotlib map plot often shows countries with varying shapes. Countries near the equator appear relatively normal, while those farther from the equator, especially near the poles, look distorted.

This occurs because the projection stretches landmasses more as they move away from the equator, causing countries near the poles to spread horizontally along the x-axis.

One of the most famous projection is called "Mercator". The Mercator projection maintains accurate direction by representing lines of constant course as straight segments, but it distorts size and shape increasingly towards the poles.

To change our previous chart projection to the Mercator projection, we follow these steps:

• initiate a projection variable using the crs module from cartopy
• change the polygon values in our world geodataframe
• pass our projection to subplot_kw

projection = ccrs.Mercator()
world_merc = world.to_crs(projection.proj4_init)

fig, ax = plt.subplots(figsize=(12, 8), subplot_kw={"projection": projection})

ax.set_axis_off() # remove border around the axes
world_merc.plot(ax=ax)

plt.show()

Our map looks way better, but the main problem with Mercator is that, for example, Greenland appears to be larger than Africa when in fact it is 14 times smaller!

A common map type is the bubble map, which overlays a scatter plot on a map, using longitudes as x-axis values and latitudes as y-axis values. However, simply changing the map projection doesn’t automatically adjust the scatter plot points accordingly.

Changing the projection alters the representation of the world's shape, thus shifting the actual locations of given latitudes and longitudes. Consequently, we must recalculate the new positions of each scatter plot point. Let's begin by loading a dataset with earthquake positions:

#Load data
url = "https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/earthquakes.csv"
df = pd.read_csv(url)
df = df[df['Depth (km)']>=0.01] # depth of at least 10 meters
df.sort_values(by='Depth (km)', ascending=False, inplace=True)
df.head()

Now we add the earthquakes on our first map:

fig, ax = plt.subplots(figsize=(12, 8))

ax.set_axis_off()
world.plot(ax=ax)
ax.scatter(
   x=df['Longitude'], y=df['Latitude'], s=df['Depth (km)']/3,
   color='darkred', alpha=0.3
)

plt.show()

If we want to use the Mercator projection in our bubble map, we have to do multiple things:

• define the wanted projection with Mercator -> projection = ccrs.Mercator()
• specify that our current data projection is PlateCarree previous_proj = ccrs.PlateCarree()
• transform the positions -> new_coords = projection.transform_points(previous_proj, df['Longitude'].values, df['Latitude'].values)
• change our background map projection -> world_merc = world.to_crs(projection.proj4_init)

Everything else stays the same: we just use new_coords when creating the scatter plot!

projection = ccrs.Mercator()
previous_proj = ccrs.PlateCarree()
new_coords = projection.transform_points(previous_proj, df['Longitude'].values, df['Latitude'].values)
world_merc = world.to_crs(projection.proj4_init)

fig, ax = plt.subplots(figsize=(12, 8), dpi=300, subplot_kw={'projection':projection})

ax.set_axis_off()
world_merc.plot(ax=ax)

# transform the coordinates to the projection's CRS
ax.scatter(
   x=new_coords[:, 0], y=new_coords[:, 1], s=df['Depth (km)']/3,
   color='darkred', alpha=0.3
)

plt.show()

The number of different projections is in fact very large. Below is an exhaustive list of the projections available in cartopy (the first ones are the most common ones, and recommended for most use cases):z

• PlateCarree(): most basic projection
• AlbersEqualArea(): preserves area, conical
• AzimuthalEquidistant(): preserves distance from center
• EquidistantConic(): preserves distance along meridians
• LambertConformal(): preserves shape, conical
• LambertCylindrical(): equal-area cylindrical
• Mercator(): preserves direction, exaggerates poles
• Miller(): compromise between Mercator and cylindrical
• Mollweide(): equal-area, elliptical
• ObliqueMercator(): rotated Mercator for oblique aspects
• Orthographic(): view from infinite distance
• Robinson(): pseudo-cylindrical, compromise
• Sinusoidal(): equal-area, pseudo-cylindrical
• Stereographic(): conformal, azimuthal
• TransverseMercator(): Mercator rotated 90 degrees
• UTM(): grid-based, preserves shape locally
• InterruptedGoodeHomolosine(): interrupted equal-area
• RotatedPole(): shifts pole position
• OSGB(): British National Grid
• EuroPP(): Europe polar stereographic
• Geostationary(): view from geostationary orbit
• NearsidePerspective(): view from finite distance
• EckertI(): pseudo-cylindrical, equal-area
• EckertII(): pseudo-cylindrical, equal-area
• EckertIII(): pseudo-cylindrical, compromise
• EckertIV(): pseudo-cylindrical, equal-area
• EckertV(): pseudo-cylindrical, compromise
• EckertVI(): pseudo-cylindrical, equal-area
• Aitoff(): modified azimuthal, compromise
• EqualEarth(): equal-area, pseudo-cylindrical
• Gnomonic(): straight great circles
• Hammer(): equal-area, elliptical
• LambertAzimuthalEqualArea(): equal-area, azimuthal
• NorthPolarStereo(): stereographic centered on North Pole
• OSNI(): Northern Ireland Grid
• SouthPolarStereo(): stereographic centered on South Pole

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