Geographic Maps¶

ggplotly supports geographic visualizations including choropleths, point maps, and multiple map projections.

Choropleth Maps¶

US State Choropleth¶

In [1]:

import pandas as pd
from ggplotly import *

# State data
state_data = pd.DataFrame({
    'state': ['CA', 'TX', 'FL', 'NY', 'PA', 'IL', 'OH', 'GA', 'NC', 'MI'],
    'population': [39.5, 29.0, 21.5, 19.5, 13.0, 12.8, 11.8, 10.7, 10.4, 10.0]
})

# Get state boundaries
states = map_data('state')

(ggplot(state_data, aes(map_id='state', fill='population'))
 + geom_map(map=states, palette='Blues')
 + labs(title='US States by Population'))

import pandas as pd from ggplotly import * # State data state_data = pd.DataFrame({ 'state': ['CA', 'TX', 'FL', 'NY', 'PA', 'IL', 'OH', 'GA', 'NC', 'MI'], 'population': [39.5, 29.0, 21.5, 19.5, 13.0, 12.8, 11.8, 10.7, 10.4, 10.0] }) # Get state boundaries states = map_data('state') (ggplot(state_data, aes(map_id='state', fill='population')) + geom_map(map=states, palette='Blues') + labs(title='US States by Population'))

Out[1]:

World Choropleth¶

In [2]:

country_data = pd.DataFrame({
    'country': ['USA', 'CHN', 'JPN', 'DEU', 'GBR', 'IND', 'FRA', 'BRA'],
    'gdp': [25.5, 18.3, 4.2, 4.1, 3.1, 3.4, 2.8, 1.9]
})

countries = map_data('world')

(ggplot(country_data, aes(map_id='country', fill='gdp'))
 + geom_map(map=countries, map_type='world', palette='Viridis')
 + labs(title='GDP by Country (Trillions USD)'))

country_data = pd.DataFrame({ 'country': ['USA', 'CHN', 'JPN', 'DEU', 'GBR', 'IND', 'FRA', 'BRA'], 'gdp': [25.5, 18.3, 4.2, 4.1, 3.1, 3.4, 2.8, 1.9] }) countries = map_data('world') (ggplot(country_data, aes(map_id='country', fill='gdp')) + geom_map(map=countries, map_type='world', palette='Viridis') + labs(title='GDP by Country (Trillions USD)'))

Out[2]:

Map Palettes¶

Available palettes for geom_map:

• Sequential: Blues, Greens, Reds, Oranges, Purples, Greys, YlOrRd, YlGnBu, BuPu, RdPu
• Viridis family: Viridis, Plasma, Inferno, Magma, Cividis
• Diverging: RdBu, RdYlGn, BrBG, PiYG, Spectral

Point Maps¶

Adding Points to Maps¶

In [3]:

# Cities data
cities = pd.DataFrame({
    'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'],
    'lat': [40.7128, 34.0522, 41.8781, 29.7604, 33.4484],
    'lon': [-74.0060, -118.2437, -87.6298, -95.3698, -112.0740],
    'pop': [8.3, 3.9, 2.7, 2.3, 1.6]
})

(ggplot(state_data, aes(map_id='state', fill='population'))
 + geom_map(map=states, palette='Blues')
 + geom_point(cities, aes(x='lon', y='lat', size='pop'), color='red')
 + labs(title='US Population: States and Major Cities'))

# Cities data cities = pd.DataFrame({ 'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'], 'lat': [40.7128, 34.0522, 41.8781, 29.7604, 33.4484], 'lon': [-74.0060, -118.2437, -87.6298, -95.3698, -112.0740], 'pop': [8.3, 3.9, 2.7, 2.3, 1.6] }) (ggplot(state_data, aes(map_id='state', fill='population')) + geom_map(map=states, palette='Blues') + geom_point(cities, aes(x='lon', y='lat', size='pop'), color='red') + labs(title='US Population: States and Major Cities'))

Out[3]:

Points on World Map¶

In [4]:

cities = pd.DataFrame({
    'city': ['New York', 'London', 'Tokyo', 'Sydney'],
    'lon': [-74.006, -0.128, 139.692, 151.209],
    'lat': [40.713, 51.507, 35.690, -33.868]
})

(ggplot(cities, aes(x='lon', y='lat'))
 + geom_map(map_type='world')
 + geom_point(color='red', size=10)
 + labs(title='Major World Cities'))

cities = pd.DataFrame({ 'city': ['New York', 'London', 'Tokyo', 'Sydney'], 'lon': [-74.006, -0.128, 139.692, 151.209], 'lat': [40.713, 51.507, 35.690, -33.868] }) (ggplot(cities, aes(x='lon', y='lat')) + geom_map(map_type='world') + geom_point(color='red', size=10) + labs(title='Major World Cities'))

Out[4]:

Map Projections¶

Use coord_sf() to apply different map projections:

Robinson Projection¶

A compromise projection often used for world maps:

In [5]:

(ggplot(cities, aes(x='lon', y='lat'))
 + geom_map(map_type='world')
 + geom_point(color='red', size=10)
 + coord_sf(crs='robinson')
 + labs(title='Robinson Projection'))

(ggplot(cities, aes(x='lon', y='lat')) + geom_map(map_type='world') + geom_point(color='red', size=10) + coord_sf(crs='robinson') + labs(title='Robinson Projection'))

Out[5]:

Orthographic (Globe)¶

Shows Earth as seen from space:

In [6]:

(ggplot(cities, aes(x='lon', y='lat'))
 + geom_map(map_type='world')
 + geom_point(color='yellow', size=8)
 + coord_sf(crs='orthographic')
 + theme_dark()
 + labs(title='Orthographic Projection'))

(ggplot(cities, aes(x='lon', y='lat')) + geom_map(map_type='world') + geom_point(color='yellow', size=8) + coord_sf(crs='orthographic') + theme_dark() + labs(title='Orthographic Projection'))

Out[6]:

Mercator¶

Standard web map projection (preserves angles):

In [7]:

(ggplot(cities, aes(x='lon', y='lat'))
 + geom_map(map_type='world')
 + geom_point(color='red', size=8)
 + coord_sf(crs='mercator')
 + labs(title='Mercator Projection'))

(ggplot(cities, aes(x='lon', y='lat')) + geom_map(map_type='world') + geom_point(color='red', size=8) + coord_sf(crs='mercator') + labs(title='Mercator Projection'))

Out[7]:

Available Projections¶

Projection	Best For
equirectangular	Default, simple lat/lon
mercator	Navigation, web maps
robinson	World thematic maps
orthographic	Globe visualization
natural earth	Balanced world view
albers usa	US-specific maps

Map Styling¶

Custom Map Colors¶

In [8]:

airports_df = pd.DataFrame({
    'lon': [-122.4, -73.8],
    'lat': [37.8, 40.6]
})

(ggplot(airports_df, aes(x='lon', y='lat'))
 + geom_map(
     map_type='usa',
     landcolor='#333333',
     oceancolor='#111111',
     bgcolor='black',
     countrycolor='#666666',
     subunitcolor='#444444'  # State borders
 )
 + geom_point(color='#00ff00', size=10)
 + theme_dark())

airports_df = pd.DataFrame({ 'lon': [-122.4, -73.8], 'lat': [37.8, 40.6] }) (ggplot(airports_df, aes(x='lon', y='lat')) + geom_map( map_type='usa', landcolor='#333333', oceancolor='#111111', bgcolor='black', countrycolor='#666666', subunitcolor='#444444' # State borders ) + geom_point(color='#00ff00', size=10) + theme_dark())

Out[8]:

Map Parameters¶

Parameter	Default	Description
map_type	'usa'	'usa', 'world', 'europe', 'asia', etc.
palette	'Blues'	Color palette for choropleth
landcolor	'rgb(243, 243, 243)'	Land fill color
oceancolor	'rgb(255, 255, 255)'	Ocean fill color
bgcolor	'white'	Background color
countrycolor	'rgb(217, 217, 217)'	Country border color
subunitcolor	'rgb(217, 217, 217)'	State/province border color

Theme Integration¶

Themes automatically style maps appropriately:

Dark Theme¶

In [9]:

(ggplot(airports_df, aes(x='lon', y='lat'))
 + geom_map(map_type='usa')
 + geom_point(color='cyan', size=10)
 + theme_dark()  # Applies dark map styling
 + labs(title='Airports'))

(ggplot(airports_df, aes(x='lon', y='lat')) + geom_map(map_type='usa') + geom_point(color='cyan', size=10) + theme_dark() # Applies dark map styling + labs(title='Airports'))

Out[9]:

Combining with Other Geoms¶

Flight Routes¶

In [10]:

airports = pd.DataFrame({
    'name': ['SFO', 'JFK', 'ORD'],
    'lon': [-122.4, -73.8, -87.6],
    'lat': [37.8, 40.6, 41.9]
})

flights = pd.DataFrame({
    'from_lon': [-122.4, -73.8],
    'from_lat': [37.8, 40.6],
    'to_lon': [-73.8, -87.6],
    'to_lat': [40.6, 41.9]
})

(ggplot()
 + geom_map(map_type='usa')
 + geom_segment(
     data=flights,
     mapping=aes(x='from_lon', y='from_lat', xend='to_lon', yend='to_lat'),
     color='red', size=1
 )
 + geom_point(
     data=airports,
     mapping=aes(x='lon', y='lat'),
     color='blue', size=10
 )
 + theme_minimal())

airports = pd.DataFrame({ 'name': ['SFO', 'JFK', 'ORD'], 'lon': [-122.4, -73.8, -87.6], 'lat': [37.8, 40.6, 41.9] }) flights = pd.DataFrame({ 'from_lon': [-122.4, -73.8], 'from_lat': [37.8, 40.6], 'to_lon': [-73.8, -87.6], 'to_lat': [40.6, 41.9] }) (ggplot() + geom_map(map_type='usa') + geom_segment( data=flights, mapping=aes(x='from_lon', y='from_lat', xend='to_lon', yend='to_lat'), color='red', size=1 ) + geom_point( data=airports, mapping=aes(x='lon', y='lat'), color='blue', size=10 ) + theme_minimal())

Out[10]:

Heatmaps on Maps¶

Combine geom_tile with geographic data for spatial heatmaps:

In [11]:

import numpy as np

# Grid data
grid = pd.DataFrame({
    'lon': np.repeat(np.linspace(-125, -65, 20), 15),
    'lat': np.tile(np.linspace(25, 50, 15), 20),
    'value': np.random.rand(300)
})

(ggplot(grid, aes(x='lon', y='lat', fill='value'))
 + geom_map(map_type='usa', alpha=0.3)
 + geom_tile(alpha=0.6)
 + scale_fill_viridis_c()
 + labs(title='Spatial Heatmap'))

import numpy as np # Grid data grid = pd.DataFrame({ 'lon': np.repeat(np.linspace(-125, -65, 20), 15), 'lat': np.tile(np.linspace(25, 50, 15), 20), 'value': np.random.rand(300) }) (ggplot(grid, aes(x='lon', y='lat', fill='value')) + geom_map(map_type='usa', alpha=0.3) + geom_tile(alpha=0.6) + scale_fill_viridis_c() + labs(title='Spatial Heatmap'))

Out[11]: