Geo Maps

• Outline-based map
• Geo maps have a built-in base map layer composed of "physical" and "cultural"
  • Physical map, coastlines, land, ocean, lakes, rivers
  • Cultural map, country borders, selected sub-country border
• Map Projections
  • Map the Earth's roughly-spherical surface into a 2-dimensional space

Express

• px.scatter_geo, px.line_geo or px.choropleth

Graph Objective

• go.Choropleth or go.Scattergeo

Layout

• layout.geo

px.choropleth

import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()

fig = px.choropleth(
        df, geojson=geojson, color='Joly',
        locations="district", featureidkey="properties.district",
        projection="mercator", range_color=[0, 6500],
        hover_data=['Coderre', 'Bergeron', 'Joly'])

# auto-computed to fit trace data in map 
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})

go.Choropleth

import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv')
df.head()

colors = ['rgb(239,243,255)','rgb(189,215,231)','rgb(107,174,214)','rgb(33,113,181)']
months = {6:'June',7:'July',8:'Aug',9:'Sept'}

df_sept = df.query('Month == 9')
fig = go.Figure()
fig.add_trace(go.Choropleth(
        locationmode = 'country names',
        locations = df_sept['Country'],
        z = df_sept['Value'],
        text = df_sept['Country'],
    ))
fig.update_geos(showframe = False, lonaxis_range= [ -15.0, -5.0 ], lataxis_range= [ 0.0, 12.0 ],)

go.Scattergeo

fig = go.Figure(go.Scattergeo())
fig.update_geos(
    resolution=50, # resolution 1:50m
    showcoastlines=True, coastlinecolor="RebeccaPurple",
    showland=True, landcolor="LightGreen",
    showocean=True, oceancolor="LightBlue",
    showlakes=True, lakecolor="Blue",
    showrivers=True, rivercolor="Blue",
    scope="usa",
    showcountries=True, countrycolor="Black",
    showsubunits=True, subunitcolor="Blue",
)

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

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

fig = go.Figure()

fig.add_trace(go.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)):
    fig.add_trace(
        go.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()),
        )
    )

fig.update_layout(
    title_text = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
    showlegend = False,
    geo = dict(
        scope = 'north america',
        projection_type = 'azimuthal equal area',
        showland = True,
        landcolor = 'rgb(243, 243, 243)',
        countrycolor = 'rgb(204, 204, 204)',
    ),
)

fig.show()

px.scatter_geo

import plotly.express as px
fig = px.scatter_geo(lat=[0,15,20,35], lon=[5,10,25,30], hover_name = ['t0<br>temp', 't1', 't2', 't3'])
fig.update_geos(fitbounds="locations")
fig.update_traces(mode="markers")

df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations="iso_alpha",
                     size="pop", # size of markers, "pop" is one of the columns of gapminder
                     )
fig.show()

import geopandas

geo_df = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))

fig = px.scatter_geo(geo_df,
                    lat=geo_df.geometry.y,
                    lon=geo_df.geometry.x,
                    hover_name="name")
fig.show()

px.line_geo

import plotly.express as px
fig = px.line_geo(lat=[0,15,None, 20,35], lon=[5,10,None, 25,30], hover_name = ['t0<br>temp', 't1', None, 't2', 't3'])
fig.update_geos(fitbounds="locations")

df = px.data.gapminder().query("year == 2007")
fig = px.line_geo(df, locations="iso_alpha",
                  color="continent", # "continent" is one of the columns of gapminder
                  projection="orthographic")
fig.show()

Data indexed by id

• By default, automatically map lications to id in geojson
• GeoJSON either does not have an id field or wish to use one of the keys in the properties field, use featureidkey

from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})

import plotly.express as px

fig = px.choropleth(df, geojson=counties, locations='fips', color='unemp', 
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           scope="usa",
                           labels={'unemp':'unemployment rate'}
                          )
fig.show()

GeoPandas

# convert geopandas Dataframe from geojson
gdf = geopandas.GeoDataFrame.from_features(counties['features']) 

# convert geopandas to geojson
gjson = gdf[['geometry', 'GEO_ID']].to_json() # str
gjson = json.loads(gjson) # json

fig = px.choropleth(gdf, geojson=gjson, locations='GEO_ID', featureidkey="properties.GEO_ID", color='CENSUSAREA',
                           scope="usa",
                          )

fig.show()

df = px.data.election()
geo_df = geopandas.GeoDataFrame.from_features(
    px.data.election_geojson()["features"]
).merge(df, on="district")

fig = px.choropleth(geo_df,
                   geojson=geo_df.geometry,
                   locations=geo_df.index,
                   color="Joly",
                   projection="mercator")
fig.update_geos(fitbounds="locations", visible=False)
fig.show()

import wget
wget.download("https://plotly.github.io/datasets/ne_50m_rivers_lake_centerlines.zip")
geo_df = geopandas.read_file("zip://ne_50m_rivers_lake_centerlines.zip")

import shapely.geometry
import numpy as np

lats = []
lons = []
names = []

for feature, name in zip(geo_df.geometry, geo_df.name):
    if isinstance(feature, shapely.geometry.linestring.LineString):
        linestrings = [feature]
    elif isinstance(feature, shapely.geometry.multilinestring.MultiLineString):
        linestrings = feature.geoms
    else:
        continue
    for linestring in linestrings:
        x, y = linestring.xy
        lats = np.append(lats, y)
        lons = np.append(lons, x)
        names = np.append(names, [name]*len(y))
        lats = np.append(lats, None) # seperate lines with None
        lons = np.append(lons, None)
        names = np.append(names, None)
        
fig = px.line_geo(lat=lats, lon=lons, hover_name=names)
fig.update_layout(
    margin = dict(l=0, r=0, b=0, t=0, pad=0)
)
fig.update_geos(
    resolution=50,
    showcoastlines=True, coastlinecolor="RebeccaPurple",
    showland=True, landcolor="FloralWhite",
    showocean=True, oceancolor="CornflowerBlue",
    showlakes=True, lakecolor="CornflowerBlue",
    showrivers=True, rivercolor="CornflowerBlue"
)
fig.show()

Using Built-in Country and State Geometries

• To use the built-in countries geometry, provide locations as three-letter ISO country codes

df = px.data.gapminder().query("year==2007")
df.head()

fig = px.choropleth(df, locations="iso_alpha",
                    color="lifeExp", # lifeExp is a column of gapminder
                    hover_name="country", # column to add to hover information
                    color_continuous_scale=px.colors.sequential.Plasma)
fig.show()

fig = px.choropleth(locations=["CA", "TX", "NY"], locationmode="USA-states", color=[1,2,3], scope="usa")
fig.show()

Plot Points, Choropleth in one Figure

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv')

colors = ['rgb(239,243,255)','rgb(189,215,231)','rgb(107,174,214)','rgb(33,113,181)']
months = {6:'June',7:'July',8:'Aug',9:'Sept'}

fig = go.Figure()

for i in range(6,10)[::-1]:
    df_month = df.query('Month == %d' %i)
    fig.add_trace(go.Scattergeo(
            lon = df_month['Lon'],
            lat = df_month['Lat'],
            text = df_month['Value'],
            name = months[i],
            marker = dict(
                size = df_month['Value']/50,
                color = colors[i-6],
                line_width = 0
            )))

df_sept = df.query('Month == 9')
fig.add_trace(go.Choropleth(
        locationmode = 'country names',
        locations = df_sept['Country'],
        z = df_sept['Value'],
        text = df_sept['Country'],
        colorscale = [[0,'rgb(0, 0, 0)'],[1,'rgb(0, 0, 0)']],
        autocolorscale = False,
        showscale = False,
        geo = 'geo2' # plot in an individual figure
    ))

fig.add_trace(go.Scattergeo(
        lon = [21.0936],
        lat = [7.1881],
        text = ['Africa'],
        mode = 'text',
        showlegend = False,
        geo = 'geo2'
    ))

fig.update_layout(
    title = go.layout.Title(
        text = 'Ebola cases reported by month in West Africa 2014<br> \
Source: <a href="https://data.hdx.rwlabs.org/dataset/rowca-ebola-cases">\
HDX</a>'),
    geo = go.layout.Geo(
        resolution = 50,
        scope = 'africa',
        showframe = False,
        showcoastlines = True,
        landcolor = "rgb(229, 229, 229)",
        countrycolor = "white" ,
        coastlinecolor = "white",
        projection_type = 'mercator',
        lonaxis_range= [ -15.0, -5.0 ],
        lataxis_range= [ 0.0, 12.0 ],
        domain = dict(x = [ 0, 1 ], y = [ 0, 1 ])
    ),
    geo2 = go.layout.Geo(
        scope = 'africa',
        showframe = False,
        landcolor = "rgb(229, 229, 229)",
        showcountries = False,
        domain = dict(x = [ 0, 0.6 ], y = [ 0, 0.6 ]),
        bgcolor = 'rgba(255, 255, 255, 0.0)',
    ),
    legend_traceorder = 'reversed'
)
    
fig.show()

Reference

• Plotly Python Open Source Graphing Library Maps
• Mapbox Maps vs Geo Maps
• Map Configuration and Styling in Python