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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