Scatter plots are often used to plot 2D data, to look for correlations and other patterns. However, they can also loosely be used to plot geographical X-Y coordinates (in reality, the field of plotting geographical points is far more complicated). We'll use a subset from the city data from simplemaps to generate our next plot. Each row of the data set represents one city in the USA, and gives us its latitude, longitude, and two-letter state code.

from matplotlib import pyplot as plt

import requests

import random

data_url = "https://raw.githubusercontent.com/sixhobbits/ritza/master/data/us-cities.txt"

r = requests.get(data_url)

with open("us-cities.txt", "w") as f:

f.write(r.text)

lats = []

lons = []

colors = []

state_colors = {}

# matplotlib uses single letter shortcuts for common colors

# blue, green, red, cyan, magenta, yellow, black

all_colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']

with open("us-cities.txt") as f:

for i, line in enumerate(f):

state, lat, lon = line.split()

lats.append(float(lat))

lons.append(float(lon))

# we assign each state a random color, but once we've picked

# a color we always use it for all cities in that state.

if state not in state_colors:

state_colors[state] = random.choice(all_colors)

colors.append(state_colors[state])

plt.scatter(lons, lats, c=colors)

plt.show()

from matplotlib import pyplot as plt

import requests

import random

data_url = "https://raw.githubusercontent.com/sixhobbits/ritza/master/data/us-cities.txt"

r = requests.get(data_url)

with open("us-cities.txt", "w") as f:

f.write(r.text)

lats = []

lons = []

colors = []

state_colors = {}

# matplotlib uses single letter shortcuts for common colors

# blue, green, red, cyan, magenta, yellow, black

all_colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']

with open("us-cities.txt") as f:

for i, line in enumerate(f):

state, lat, lon = line.split()

lats.append(float(lat))

lons.append(float(lon))

# we assign each state a random color, but once we've picked

# a color we always use it for all cities in that state.

if state not in state_colors:

state_colors[state] = random.choice(all_colors)

colors.append(state_colors[state])

plt.scatter(lons, lats, c=colors)

plt.show()

If you run this, you'll notice it takes a little bit longer than the six point plot we created before, as it now has to plot nearly 30 000 data points. Once it's done, you should see something similar to the following (though, as the colours were chosen randomly, yours might look different).