Run this notebook: Open in Colab Open in Kaggle

import plotly.plotly as py
import pandas as pd
import plotly 
from plotly.graph_objs import *


df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.head()

df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
df_flight_paths.head()

airports = [ dict(
        type = 'scattergeo',
        locationmode = 'USA-states',
        lon = df_airports['long'],
        lat = df_airports['lat'],
        hoverinfo = 'text',
        text = df_airports['airport'],
        mode = 'markers',
        marker = dict( 
            size=2, 
            color='rgb(255, 0, 0)',
            line = dict(
                width=3,
                color='rgba(68, 68, 68, 0)'
            )
        ))]
        
flight_paths = []
for i in range( len( df_flight_paths ) ):
    flight_paths.append(
        dict(
            type = 'scattergeo',
            locationmode = 'USA-states',
            lon = [ df_flight_paths['start_lon'][i], df_flight_paths['end_lon'][i] ],
            lat = [ df_flight_paths['start_lat'][i], df_flight_paths['end_lat'][i] ],
            mode = 'lines',
            line = dict(
                width = 1,
                color = 'red',
            ),
            opacity = float(df_flight_paths['cnt'][i])/float(df_flight_paths['cnt'].max()),
        )
    )
    
layout = dict(
        title = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
        showlegend = False, 
        geo = dict(
            scope='north america',
            projection=dict( type='azimuthal equal area' ),
            showland = True,
            landcolor = 'rgb(243, 243, 243)',
            countrycolor = 'rgb(204, 204, 204)',
        ),
    )
    
fig = dict( data=flight_paths + airports, layout=layout )
plot_url = plotly.offline.plot(fig)

Geographic Visualization – Flight Path Maps with Plotly

This notebook creates an interactive geographic visualization of American Airlines flight paths across the United States using Plotly’s scattergeo trace type. It loads airport locations and flight path data from CSV files, plots airports as markers on a map, draws flight paths as lines with opacity proportional to flight frequency, and renders the result as an interactive map.

Why this matters: Geographic data visualization is essential in logistics, transportation analysis, and location-based business intelligence. This example demonstrates how to combine Pandas data loading with Plotly’s geo-mapping capabilities to create compelling spatial visualizations that reveal network patterns and traffic density.