python_geopandas_folium_choropleths.qmd

# Interactive Maps - Choropleths

```{r, echo = FALSE}
source("glossary_funcs.R")
glossary_setup()
```

```{r, echo = FALSE, results='asis'}
glossary_setup_style()
```

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

```{python}
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()
```

## 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.

```{python}
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
```

### 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.

```{python}
#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
```

## Tooltips

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

```{python}
#| eval: True
choropleth = choropleth.geojson.add_child(
    folium.features.GeoJsonTooltip(
        ['LSOA11NM', 'sw_5forces_street_by_lsoa_Bicycle theft'],
        labels=True
        )
)
```

Here is the full code.

```{python}
#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

```

### 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:

```{python}
#| eval: False
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.

```{python}
#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

```