Plotly maps - Data to Decision

Various map types and overlays are available.
Open Street Map is open for use without a token.
Overlays include scatter, lines, densities, etc.
Choropleths (coloured map sections) can be used as overlays or as separate plots when a GeoJSON formated dictionary of map polygons are available.

# The following renders plotly graphs in Jupyter Notebook, Jupyter Lab and VS Code formats
import plotly.io as pio
pio.renderers.default = "notebook+plotly_mimetype"

Map¶

Various maps with user defined overlays are available, e.g., scatter_map.
From Plotly version 5.24, Mapbox-es are deprecated, e.g., scatter_mapbox.
- Writer has, as of 16. November 2024, not made the switch yet.
- In most cases, switching between map and mapbox does not require other code changes.

import plotly.express as px
import pandas as pd

us_cities = pd.read_csv(
    "https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv"
)

fig = px.scatter_map(
    us_cities,
    lat="lat",
    lon="lon",
    hover_name="City",
    hover_data=["State", "Population"],
    color_discrete_sequence=["fuchsia"],
    zoom=3,
    height=300,
    width=600,
)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.update_layout(mapbox_bounds={"west": -180, "east": -50, "south": 20, "north": 70})
fig

A local example from Ås¶

import pandas as pd

# Local restaurants and cafes
restaurants = pd.read_csv('../../data/restaurants.csv')
restaurants

fig_restaurants = px.scatter_map(
    restaurants,
    lat="lat",
    lon="lon",
    hover_name="name",
    hover_data=["type","lat","lon"],
    color_discrete_sequence=["green"],
    zoom=14,
    height=600,
    width=700,
)
fig_restaurants.update_layout(mapbox_style="open-street-map")
fig_restaurants.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
#fig_restaurants.update_traces(cluster=dict(enabled=True)) # Group restaurants when zooming out
fig_restaurants

print(fig_restaurants)

Figure({
    'data': [{'customdata': array([['Restaurant', 59.6647728, 10.7896737],
                                   ['Restaurant', 59.6646628, 10.7887347],
                                   ['Restaurant', 59.6654962, 10.7953659],
                                   ['Restaurant', 59.6642813, 10.7922755],
                                   ['Restaurant', 59.6636019, 10.7929767],
                                   ['Kiosk', 59.6637434, 10.7932373],
                                   ['Restaurant', 59.663465, 10.7935577],
                                   ['Café', 59.6646862, 10.7908304],
                                   ['Café', 59.663324, 10.7943351]], dtype=object),
              'hovertemplate': ('<b>%{hovertext}</b><br><br>lat' ... '{customdata[0]}<extra></extra>'),
              'hovertext': array(['Sushiko Ås', "Charlie's Diner", "Jojo's Pizza", 'Desi Zaiqa',
                                  'Babylon Pizza', 'NT kiosk', 'Aas Bistro', 'Whytes Coffee',
                                  'Station kiosk Ås'], dtype=object),
              'lat': {'bdata': ('m6JtRhfVTUAUb66rE9VNQF9Ov/ou1U' ... 'XOa+zUTUAfjflvFNVNQHJTA83n1E1A'),
                      'dtype': 'f8'},
              'legendgroup': '',
              'lon': {'bdata': ('ywV4HFCUJUDUcNsI1ZMlQAy1ATM6ly' ... 'rhMU2WJUB+XeG455QlQDkhGRezliVA'),
                      'dtype': 'f8'},
              'marker': {'color': 'green'},
              'mode': 'markers',
              'name': '',
              'showlegend': False,
              'subplot': 'map',
              'type': 'scattermap'}],
    'layout': {'height': 600,
               'legend': {'tracegroupgap': 0},
               'map': {'center': {'lat': np.float64(59.66422595555556), 'lon': np.float64(10.792331888888889)},
                       'domain': {'x': [0.0, 1.0], 'y': [0.0, 1.0]},
                       'zoom': 14},
               'mapbox': {'center': {'lat': np.float64(59.66422595555556), 'lon': np.float64(10.792331888888889)},
                          'style': 'open-street-map',
                          'zoom': 14},
               'margin': {'b': 0, 'l': 0, 'r': 0, 't': 0},
               'template': '...',
               'width': 700}
})

Interacting with a Plotly map in Streamlit¶

Selecting points¶

import pandas as pd
import plotly.express as px
import streamlit as st

# Local restaurants and cafes
restaurants = pd.read_csv('../D2Dbook/data/restaurants.csv')

fig_restaurants = px.scatter_map(
    restaurants,
    lat="lat",
    lon="lon",
    hover_name="name",
    hover_data=["type","lat","lon"],
    color_discrete_sequence=["green"],
    # Size of sequence
    size=[10]*len(restaurants),
    size_max=8,
    zoom=14,
    height=600,
    width=700,
)
fig_restaurants.update_layout(mapbox_style="open-street-map")
fig_restaurants.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})

# If "restaurant" is not in the session state, initialize it
st.title("Local Restaurants and Cafes")

st.plotly_chart(fig_restaurants, key = "restaurant", on_select="rerun", use_container_width=False)
st.subheader("")
st.subheader("Clicked Restaurant Info")
if st.session_state["restaurant"]:
    st.json(st.session_state["restaurant"])

# !streamlit run "/Users/kristian/Documents/GitHub/IND320/streamlit/map.py"

Choropleths¶

A pure choropleth can be plotted using a GeoJSON file.
In addition a DataFrame containing the map region properties to use for colouring and hover information is needed.

# Choropleth map of US counties with unemployment rate
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_chl = px.choropleth(df, geojson=counties, locations='fips', color='unemp',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           scope="usa",
                           labels={'unemp':'unemployment rate'}
                          )
fig_chl.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig_chl

#counties

Choropleth with map¶

The map version of choropleth plotting is more robust to choropleth specifications.
Examples of GeoJSON sources: https://norgeskart.no/json/norge/ and https://temakart.nve.no/tema/nettanlegg.

custom_scale = [
    (0.0, "rgba(68,1,84,0.6)"),
    (0.5, "rgba(58,82,139,0.6)"),
    (1.0, "rgba(33,145,140,0.6)")
]

fig_chl = px.choropleth_map(
    df,
    geojson=counties,
    locations="fips",
    featureidkey="id",
    color="unemp",
    color_continuous_scale=custom_scale,
    range_color=(2, 8),
    map_style="open-street-map",
    zoom=3,
    center={"lat": 40.0, "lon": -95.0},
)
fig_chl.update_traces(marker_line=dict(width=0.5, color="white"))
fig_chl.update_layout(margin=dict(r=0, t=0, l=0, b=0))
fig_chl

Choropleth and pin¶

Using Folium, one can extract latitudes and longitudes from choropleth overlayed maps.
If the GeoJSON is large, expect some latency when clicking.

# !streamlit run "/Users/kristian/Documents/GitHub/IND320/streamlit/map_folium_choropleth.py"

# Gapminder dataset as a map
import plotly.express as px
df = px.data.gapminder()
fig_chl2 = px.choropleth(df, locations="iso_alpha", color="lifeExp", hover_name="country", animation_frame="year", range_color=[20,80])
fig_chl2