Plotting Data on Maps

Data Science, AI and ML
#1

Hello Everyone,

This blog will teach you how to plot data on Maps. Sometimes we have been provided with global data such as cities to cities comparison, country to country and further more. We can’t visualize those points, insights through any other basic charts/graphs, at that we need Maps chart.
The most useful piece of the Basemap toolkit is the ability to over-plot a variety of data onto a map background. For simple plotting and text, any plt function
works on the map; you can use the Basemap instance to project latitude and longitude
coordinates to (x, y) coordinates for plotting with plt, as we saw earlier in the Seattle example.
In addition to this, there are many map-specific functions available as methods of the
Basemap instance. These work very similarly to their standard Matplotlib counter‐
parts, but have an additional Boolean argument latlon, which if set to True allows
you to pass raw latitudes and longitudes to the method, rather than projected (x, y)
coordinates.
Some of these map-specific methods are:

contour()/contourf()
Draw contour lines or filled contours
imshow()
Draw an image
pcolor()/pcolormesh()
Draw a pseudocolor plot for irregular/regular meshes
plot()
Draw lines and/or markers
scatter()
Draw points with markers
quiver()
Draw vectors
barbs()
Draw wind barbs
drawgreatcircle()
Draw a great circle

We’ll see examples of a few of these as we continue.

Suppose we have data set of California cities. Here, we’ll create a plot using Basemap to put the data in context.
We start with loading the data:

In[10]:
import pandas as pd
cities = pd.read_csv(‘data/california_cities.csv’)

Extract the data we’re interested in

lat = cities[‘latd’].values
lon = cities[‘longd’].values
population = cities[‘population_total’].values
area = cities[‘area_total_km2’].values

Next, we set up the map projection, scatter the data, and then create a colorbar and
legend:

In[11]:

1. Draw the map background

fig = plt.figure(figsize=(8, 8))
m = Basemap(projection=‘lcc’, resolution=‘h’,
lat_0=37.5, lon_0=-119,
width=1E6, height=1.2E6)
m.shadedrelief()
m.drawcoastlines(color=‘gray’)
m.drawcountries(color=‘gray’)
m.drawstates(color=‘gray’)

2. scatter city data, with color reflecting population

and size reflecting area

m.scatter(lon, lat, latlon=True,
c=np.log10(population), s=area,
cmap=‘Reds’, alpha=0.5)

3. create colorbar and legend

plt.colorbar(label=r’$\log_{10}({\rm population})$’)
plt.clim(3, 7)

make legend with dummy points

for a in [100, 300, 500]:
plt.scatter([], [], c=‘k’, alpha=0.5, s=a,
label=str(a) + ’ km$^2$’)
plt.legend(scatterpoints=1, frameon=False,
labelspacing=1, loc=‘lower left’);

This shows us roughly where larger populations of people have settled in California:
they are clustered near the coast in the Los Angeles and San Francisco areas,
stretched along the highways in the flat central valley, and avoiding almost completely
the mountainous regions along the borders of the state.

Thankyou.