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Scatter Plots on Maps in Python

How to make scatter plots on maps in Python. Scatter plots on maps highlight geographic areas and can be colored by value.

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In [1]:
import plotly
plotly.__version__
Out[1]:
'3.6.1'

U.S. Airports Map

In [2]:
import plotly.plotly as py
import plotly.graph_objs as go

import pandas as pd

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

df['text'] = df['airport'] + '' + df['city'] + ', ' + df['state'] + '' + 'Arrivals: ' + df['cnt'].astype(str)

scl = [ [0,"rgb(5, 10, 172)"],[0.35,"rgb(40, 60, 190)"],[0.5,"rgb(70, 100, 245)"],\
    [0.6,"rgb(90, 120, 245)"],[0.7,"rgb(106, 137, 247)"],[1,"rgb(220, 220, 220)"] ]

data = [ go.Scattergeo(
        locationmode = 'USA-states',
        lon = df['long'],
        lat = df['lat'],
        text = df['text'],
        mode = 'markers',
        marker = dict( 
            size = 8, 
            opacity = 0.8,
            reversescale = True,
            autocolorscale = False,
            symbol = 'square',
            line = dict(
                width=1,
                color='rgba(102, 102, 102)'
            ),
            colorscale = scl,
            cmin = 0,
            color = df['cnt'],
            cmax = df['cnt'].max(),
            colorbar=dict(
                title="Incoming flights<br>February 2011"
            )
        ))]

layout = dict(
        title = 'Most trafficked US airports<br>(Hover for airport names)', 
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showland = True,
            landcolor = "rgb(250, 250, 250)",
            subunitcolor = "rgb(217, 217, 217)",
            countrycolor = "rgb(217, 217, 217)",
            countrywidth = 0.5,
            subunitwidth = 0.5        
        ),
    )

fig = go.Figure(data=data, layout=layout )
py.iplot(fig, filename='d3-airports' )
Out[2]:

North American Precipitation Map

In [3]:
import plotly.plotly as py
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2015_06_30_precipitation.csv')

scl = [0,"rgb(150,0,90)"],[0.125,"rgb(0, 0, 200)"],[0.25,"rgb(0, 25, 255)"],\
[0.375,"rgb(0, 152, 255)"],[0.5,"rgb(44, 255, 150)"],[0.625,"rgb(151, 255, 0)"],\
[0.75,"rgb(255, 234, 0)"],[0.875,"rgb(255, 111, 0)"],[1,"rgb(255, 0, 0)"]

data = [go.Scattergeo(
    lat = df['Lat'],
    lon = df['Lon'],
    text = df['Globvalue'].astype(str) + ' inches',
    marker = dict(
        color = df['Globvalue'],
        colorscale = scl,
        reversescale = True,
        opacity = 0.7,
        size = 2,        
        colorbar = dict(
            thickness = 10,
            titleside = "right",
            outlinecolor = "rgba(68, 68, 68, 0)",
            ticks = "outside",
            ticklen = 3,
            showticksuffix = "last",
            ticksuffix = " inches",
            dtick = 0.1
        )                
    )
)]

layout = dict(
    geo = dict(
        scope = 'north america',
        showland = True,
        landcolor = "rgb(212, 212, 212)",
        subunitcolor = "rgb(255, 255, 255)",
        countrycolor = "rgb(255, 255, 255)",
        showlakes = True,
        lakecolor = "rgb(255, 255, 255)",
        showsubunits = True,
        showcountries = True,
        resolution = 50,
        projection = dict(
            type = 'conic conformal',
            rotation = dict(
                lon = -100
            )
        ),
        lonaxis = dict(
            showgrid = True,
            gridwidth = 0.5,
            range= [ -140.0, -55.0 ],
            dtick = 5
        ),
        lataxis = dict (
            showgrid = True,
            gridwidth = 0.5,
            range= [ 20.0, 60.0 ],
            dtick = 5
        )
    ),
    title = 'US Precipitation 06-30-2015<br>Source: <a href="http://water.weather.gov/precip/">NOAA</a>',
)

fig = go.Figure(data=data, layout=layout )
py.iplot(fig, filename='precipitation')
Out[3]:

Reference

See https://plot.ly/python/reference/#scattergeo and https://plot.ly/python/reference/#layout-geo for more information and chart attribute options!