22  Interactive Maps - Choropleths

Let’s first load in, filter and inspect the dataset we will be using for these choropleths.

import pandas as pd
import geopandas

lsoa_2011_crime_figures_df = geopandas.read_file("https://github.com/hsma-programme/h6_3b_advanced_qgis_mapping_python/raw/main/h6_3b_advanced_qgis_and_mapping_in_python/example_code/lsoa_2011_sw5forces_crime_figures.gpkg")

lsoa_2011_crime_figures_df_devon=lsoa_2011_crime_figures_df.cx[250000:350000, 0:150000]

lsoa_2011_crime_figures_df.head()

    LSOA11CD  ...                                           geometry
0  E01014370  ...  MULTIPOLYGON (((375207.458 165659.881, 375312....
1  E01014371  ...  MULTIPOLYGON (((375613.903 165217.218, 375635....
2  E01014372  ...  MULTIPOLYGON (((375273.457 165743.254, 375335....
3  E01014373  ...  MULTIPOLYGON (((377835.224 168339.576, 377910....
4  E01014374  ...  MULTIPOLYGON (((378721.671 167591.617, 378472....

[5 rows x 20 columns]

22.1 Creating choropleths

We can also create choropleths in Folium.

As with point data, we start by importing Folium and creating a basemap.

We then create a new choropleth layer and add this to the basemap.

import folium

#create base map
bike_crime_map_interactive = folium.Map(
    location=[50.71671, -3.50668],
    zoom_start=9,
    tiles='cartodbpositron'
    )

# create and add choropleth map
choropleth = folium.Choropleth(
    geo_data=lsoa_2011_crime_figures_df_devon, # dataframe with geometry in it
    data=lsoa_2011_crime_figures_df_devon, # dataframe with data in - may be the same dataframe or a different one
    columns=['LSOA11NM', 'sw_5forces_street_by_lsoa_Bicycle theft'], # [key (field for geometry), field to plot]
    key_on='feature.properties.LSOA11NM'
    )

choropleth = choropleth.add_to(bike_crime_map_interactive)

bike_crime_map_interactive

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22.1.1 Adding complexity

We can pass in additional arguments to the folium.Choropleth() call to tweak the display further.

fill_colour takes a matplotlib colourmap string to change the colours used for the scale.

fill_opacity affects how see-through (transparent) the choropleth layer is. This is a value between 0 and 1, with 1 being totally opaque and 0 being totally see-through.

line_weight affects the thickness of the outlines around different boundaries.

legend_name adjusts the label attached to the legend.

highlight highlights the LSOA shape when mouse pointer enters it if set to True; it defaults to False.

smooth_factor affects how simplified the boundaries of each region will be; 0 ensures no simplification occurs.

#create base map
bike_crime_map_interactive = folium.Map(
    location=[50.71671, -3.50668],
    zoom_start=9,
    tiles='cartodbpositron'
    )

# create and add choropleth map
choropleth = folium.Choropleth(
    geo_data=lsoa_2011_crime_figures_df_devon, # dataframe with geometry in it
    data=lsoa_2011_crime_figures_df_devon, # dataframe with data in - may be the same dataframe or a different one
    columns=['LSOA11NM', 'sw_5forces_street_by_lsoa_Bicycle theft'], # [key (field for geometry), field to plot]
    key_on='feature.properties.LSOA11NM',
    fill_color='OrRd',
    fill_opacity=0.4,
    line_weight=0.3,
    legend_name='Bicycle Thefts',
    highlight=True, # highlight the LSOA shape when mouse pointer enters it
    smooth_factor=0
    )

choropleth = choropleth.add_to(bike_crime_map_interactive)

bike_crime_map_interactive

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22.2 Tooltips

Simple tooltips can be added with this code, passing in the column of interest as well as the region label:

choropleth = choropleth.geojson.add_child(
    folium.features.GeoJsonTooltip(
        ['LSOA11NM', 'sw_5forces_street_by_lsoa_Bicycle theft'],
        labels=True
        )
)

Here is the full code.

#create base map
bike_crime_map_interactive = folium.Map(
    location=[50.71671, -3.50668],
    zoom_start=9,
    tiles='cartodbpositron'
    )

# create and add choropleth map
choropleth = folium.Choropleth(
    geo_data=lsoa_2011_crime_figures_df_devon, # dataframe with geometry in it
    data=lsoa_2011_crime_figures_df_devon, # dataframe with data in - may be the same dataframe or a different one
    columns=['LSOA11NM', 'sw_5forces_street_by_lsoa_Bicycle theft'], # [key (field for geometry), field to plot]
    key_on='feature.properties.LSOA11NM',
    fill_color='OrRd',
    fill_opacity=0.4,
    line_weight=0.3,
    legend_name='Bicycle Thefts',
    highlight=True, # highlight the LSOA shape when mouse pointer enters it
    smooth_factor=0
    )

choropleth = choropleth.add_to(bike_crime_map_interactive)

choropleth = choropleth.geojson.add_child(
    folium.features.GeoJsonTooltip(
        ['LSOA11NM', 'sw_5forces_street_by_lsoa_Bicycle theft'],
        labels=True
        )
)

bike_crime_map_interactive

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22.2.1 Adding additional columns to choropleths

By adding additional columns to the list we pass in to the folium.features.GeoJsonTooltip() function, we can include as many additional columns as we would like in our choropleth.

For example:

choropleth.geojson.add_child(
    folium.features.GeoJsonTooltip(
        ['LSOA11NM',
        'sw_5forces_street_by_lsoa_Bicycle theft',
        'sw_5forces_street_by_lsoa_Total number crimes'],
        labels=True
        )
)

Here is the full code.

#create base map
bike_crime_map_interactive = folium.Map(
    location=[50.71671, -3.50668],
    zoom_start=9,
    tiles='cartodbpositron'
    )

# create and add choropleth map
choropleth = folium.Choropleth(
    geo_data=lsoa_2011_crime_figures_df_devon, # dataframe with geometry in it
    data=lsoa_2011_crime_figures_df_devon, # dataframe with data in - may be the same dataframe or a different one
    columns=['LSOA11NM', 'sw_5forces_street_by_lsoa_Bicycle theft'], # [key (field for geometry), field to plot]
    key_on='feature.properties.LSOA11NM',
    fill_color='OrRd',
    fill_opacity=0.4,
    line_weight=0.3,
    legend_name='Bicycle Thefts',
    highlight=True, # highlight the LSOA shape when mouse pointer enters it
    smooth_factor=0
    )

choropleth = choropleth.add_to(bike_crime_map_interactive)

choropleth = choropleth.geojson.add_child(
    folium.features.GeoJsonTooltip(
        ['LSOA11NM',
        'sw_5forces_street_by_lsoa_Bicycle theft',
        'sw_5forces_street_by_lsoa_Total number crimes'],
        labels=True
        )
)

bike_crime_map_interactive

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