Geospatial Data Frames

In this lesson, we'll introduce methods to manipulate geospatial data: data about locations on Earth, such as the information in a map. By the end of this lesson, students will be able to:

• Apply filters and plot geospatial data stored in shapefiles using geopandas.
• Describe the difference between numeric coordinate data and geospatial data.
• Draw multiple plots on the same figure by using subplots to specify axes.

Geospatial data is often tabular just like CSV files. But they typically contain extra data representing the geometry of each area. geopandas is a library that extends pandas to automatically process the geometries.

import geopandas as gpd
import matplotlib.pyplot as plt

GeoDataFrame

Geospatial data is often communicated shapefile format with the .shp extension. We can create a GeoDataFrame from a shapefile by calling gpd.read_file. The following dataset of world countries has 169 columns: today, we'll work with only a handful of them.

columns = ["POP_EST", "GDP_MD", "CONTINENT", "SUBREGION", "geometry"]
countries = gpd.read_file("ne_110m_admin_0_countries.shp").set_index("NAME")[columns]
countries

	POP_EST	GDP_MD	CONTINENT	SUBREGION	geometry
NAME
Fiji	889953.0	5496	Oceania	Melanesia	MULTIPOLYGON (((180 -16.06713, 180 -16.55522, ...
Tanzania	58005463.0	63177	Africa	Eastern Africa	POLYGON ((33.90371 -0.95, 34.07262 -1.05982, 3...
W. Sahara	603253.0	907	Africa	Northern Africa	POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
Canada	37589262.0	1736425	North America	Northern America	MULTIPOLYGON (((-122.84 49, -122.97421 49.0025...
United States of America	328239523.0	21433226	North America	Northern America	MULTIPOLYGON (((-122.84 49, -120 49, -117.0312...
...	...	...	...	...	...
Serbia	6944975.0	51475	Europe	Southern Europe	POLYGON ((18.82982 45.90887, 18.82984 45.90888...
Montenegro	622137.0	5542	Europe	Southern Europe	POLYGON ((20.0707 42.58863, 19.80161 42.50009,...
Kosovo	1794248.0	7926	Europe	Southern Europe	POLYGON ((20.59025 41.85541, 20.52295 42.21787...
Trinidad and Tobago	1394973.0	24269	North America	Caribbean	POLYGON ((-61.68 10.76, -61.105 10.89, -60.895...
S. Sudan	11062113.0	11998	Africa	Eastern Africa	POLYGON ((30.83385 3.50917, 29.9535 4.1737, 29...

177 rows × 5 columns

What makes a GeoDataFrame different from a regular DataFrame is the inclusion of a geometry column that is automatically plotted when we call plot().

type(countries)

geopandas.geodataframe.GeoDataFrame

ax = countries.plot()
ax.set(title="World Map")
ax.set_axis_off()

The result of calling plot is a matplotlib Axes object, which is different from the seaborn FacetGrid objects that we used earlier. There will be some syntactical differences, but we can similarly customize an Axes by calling methods like set(...) to define labels or set_axis_off() to remove the automatic latitude and longitude label markings.

We can also pass keyword arguments to plot to create more interesting choropleth maps: maps where the color of each shape is based on a corresponding value. For example, to plot each country shaded according to population, specify column="POP_EST". To add a legend, specify legend=True.

countries.plot(column="POP_EST", legend=True)

<Axes: >

Practice: South American country populations

Write an expression to plot a choropleth map of the POP_EST column for every country in the CONTINENT "South America". Include a legend.

# When you drop the geometry column, you're left with a plain old DataFrame that can't plot maps
type(countries[countries["CONTINENT"] == "South America"].drop("geometry", axis="columns"))

pandas.core.frame.DataFrame

countries[countries["CONTINENT"] == "South America"].drop("geometry", axis="columns").plot()

<Axes: xlabel='NAME'>

countries[countries["CONTINENT"] == "South America"].plot(column="POP_EST", legend=True)

<Axes: >

Customizing Axes with subplots

By default, each call to plot will return a new set of matplotlib Axes to represent the map. Since we're working with matplotlib rather than seaborn, our plots will typically require some work to arrange everything into a single figure.

To add more space to a plot, call plt.subplots to create a new figure and a new set of Axes within that figure. To make a larger figure, specify figsize=(width, height) where width and height are numbers. We want a wider figure to make more space for the legend.

fig, ax = plt.subplots(figsize=(13, 5))
countries.plot(ax=ax, column="POP_EST", legend=True)

<Axes: >

ax = countries.plot(color="#aaa")
countries[countries["CONTINENT"] == "South America"].plot(ax=ax, column="POP_EST", legend=True)

<Axes: >

countries.plot(figsize=(13, 5), column="POP_EST", legend=True)

<Axes: >

The first two keyword arguments for plt.subplots specify nrows and ncols. We can create a 2-tall by 3-wide figure and use tuple unpacking to handle the 6 resulting Axes objects. Let's plot the countries that belong to each of the following continents.

continents = ["Oceania", "Africa", "North America", "Asia", "South America", "Europe"]
fig, [[ax1, ax2, ax3], [ax4, ax5, ax6]] = plt.subplots(2, 3, figsize=(15, 10))
# Want to loop over the axes (of which there are 6) and continents (there are also 6!),
# use zip! It's a nice alternative to range and trying to pick out the same ax and continent from each list.
# for i in range(6):
#     ax = [ax1, ax2, ax3, ax4, ax5, ax6][i]
#     continent = continents[i]
#     ...
for ax, continent in zip([ax1, ax2, ax3, ax4, ax5, ax6], continents):
    countries[countries["CONTINENT"] == continent].plot(ax=ax, column="POP_EST")
    ax.set(title=f"Map of {continent}", xlabel="Latitude", ylabel="Longitude")

Practice: Trillionaire GDP choropleth

Plot the GDP_MD (GDP in millions of US dollars) for every country with a GDP_MD value greater than 1000000 atop this background of the world map.

ax = countries.plot(color="#EEE")
countries[countries["GDP_MD"] > 1000000].plot(ax=ax, column="GDP_MD")

<Axes: >