Filled Area on Tile Maps in Python

How to make an area on tile-based maps in Python with Plotly.


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Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

There are three different ways to show a filled area on a tile-based map:

  • Using a Scattermap trace and setting the fill attribute to 'toself'
  • Using a map layout (i.e. by minimally using an empty Scattermap trace) and adding a GeoJSON layer
  • Using the Choroplethmap trace type

Filled Scattermap Trace

The following example uses Scattermap and sets fill = 'toself'

In [1]:
import plotly.graph_objects as go

fig = go.Figure(go.Scattermap(
    fill = "toself",
    lon = [-74, -70, -70, -74], lat = [47, 47, 45, 45],
    marker = { 'size': 10, 'color': "orange" }))

fig.update_layout(
    map = {
        'style': "open-street-map",
        'center': {'lon': -73, 'lat': 46 },
        'zoom': 5},
    showlegend = False)

fig.show()

Multiple Filled Areas with a Scattermap trace

The following example shows how to use None in your data to draw multiple filled areas. Such gaps in trace data are unconnected by default, but this can be controlled via the connectgaps attribute.

In [2]:
import plotly.graph_objects as go

fig = go.Figure(go.Scattermap(
    mode = "lines", fill = "toself",
    lon = [-10, -10, 8, 8, -10, None, 30, 30, 50, 50, 30, None, 100, 100, 80, 80, 100],
    lat = [30, 6, 6, 30, 30,    None, 20, 30, 30, 20, 20, None, 40, 50, 50, 40, 40]))

fig.update_layout(
    map = {'style': "open-street-map", 'center': {'lon': 30, 'lat': 30}, 'zoom': 2},
    showlegend = False,
    margin = {'l':0, 'r':0, 'b':0, 't':0})

fig.show()

GeoJSON Layers

In this map we add a GeoJSON layer.

In [3]:
import plotly.graph_objects as go

fig = go.Figure(go.Scattermap(
    mode = "markers",
    lon = [-73.605], lat = [45.51],
    marker = {'size': 20, 'color': ["cyan"]}))

fig.update_layout(
    map = {
        'style': "open-street-map",
        'center': { 'lon': -73.6, 'lat': 45.5},
        'zoom': 12, 'layers': [{
            'source': {
                'type': "FeatureCollection",
                'features': [{
                    'type': "Feature",
                    'geometry': {
                        'type': "MultiPolygon",
                        'coordinates': [[[
                            [-73.606352888, 45.507489991], [-73.606133883, 45.50687600],
                            [-73.605905904, 45.506773980], [-73.603533905, 45.505698946],
                            [-73.602475870, 45.506856969], [-73.600031904, 45.505696003],
                            [-73.599379992, 45.505389066], [-73.599119902, 45.505632008],
                            [-73.598896977, 45.505514039], [-73.598783894, 45.505617001],
                            [-73.591308727, 45.516246185], [-73.591380782, 45.516280145],
                            [-73.596778656, 45.518690062], [-73.602796770, 45.521348046],
                            [-73.612239983, 45.525564037], [-73.612422919, 45.525642061],
                            [-73.617229085, 45.527751983], [-73.617279234, 45.527774160],
                            [-73.617304713, 45.527741334], [-73.617492052, 45.527498362],
                            [-73.617533258, 45.527512253], [-73.618074188, 45.526759105],
                            [-73.618271651, 45.526500673], [-73.618446320, 45.526287943],
                            [-73.618968507, 45.525698560], [-73.619388002, 45.525216750],
                            [-73.619532966, 45.525064183], [-73.619686662, 45.524889290],
                            [-73.619787038, 45.524770086], [-73.619925742, 45.524584939],
                            [-73.619954486, 45.524557690], [-73.620122362, 45.524377961],
                            [-73.620201713, 45.524298907], [-73.620775593, 45.523650879]
                        ]]]
                    }
                }]
            },
            'type': "fill", 'below': "traces", 'color': "royalblue"}]},
    margin = {'l':0, 'r':0, 'b':0, 't':0})

fig.show()

Mapbox Maps

Mapbox traces are deprecated and may be removed in a future version of Plotly.py.

The earlier examples using go.Scattermap use Maplibre for rendering. This trace was introduced in Plotly.py 5.24 and is now the recommended way to draw filled areas on tile-based maps. There is also a trace that uses Mapbox, called go.Scattermapbox.

To use the Scattermapbox trace type, in some cases you may need a Mapbox account and a public Mapbox Access Token. See our Mapbox Map Layers documentation for more information.

Here's one of the earlier examples rewritten to use Scattermapbox.

import plotly.graph_objects as go

fig = go.Figure(go.Scattermapbox(
    fill = "toself",
    lon = [-74, -70, -70, -74], lat = [47, 47, 45, 45],
    marker = { 'size': 10, 'color': "orange" }))

fig.update_layout(
    mapbox = {
        'style': "open-street-map",
        'center': {'lon': -73, 'lat': 46 },
        'zoom': 5},
    showlegend = False)

fig.show()

Reference

See https://plotly.com/python/reference/scattermap/ for available attribute options, or for go.Scattermapbox, see https://plotly.com/python/reference/scattermapbox/.

What About Dash?

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter