Jupyter notebook snippet for plotting H3 hexagons on Folium maps

h3-helpers-for-folium-maps.py

!conda install -y folium geojson

import folium
from geojson import Feature, Point, FeatureCollection
import json

def hexagons_dataframe_to_geojson(df_hex, file_output = None, column_name = "value"):
    """
    Produce the GeoJSON for a dataframe, constructing the geometry from the "hex_id" column
    and with a property matching the one in column_name
    """    
    list_features = []
    
    for i,row in df_hex.iterrows():
        try:
            geometry_for_row = { "type" : "Polygon", "coordinates": [h3.h3_to_geo_boundary(h=row["hex_id"],geo_json=True)]}
            feature = Feature(geometry = geometry_for_row , id=row["hex_id"], properties = {column_name : row[column_name]})
            list_features.append(feature)
        except:
            print("An exception occurred for hex " + row["hex_id"]) 

    feat_collection = FeatureCollection(list_features)
    geojson_result = json.dumps(feat_collection)
    return geojson_result

def get_color(custom_cm, val, vmin, vmax):
    return matplotlib.colors.to_hex(custom_cm((val-vmin)/(vmax-vmin)))

def choropleth_map(df_aggreg, column_name = "value", border_color = 'black', fill_opacity = 0.7, color_map_name = "Blues", initial_map = None):
    """
    Creates choropleth maps given the aggregated data. initial_map can be an existing map to draw on top of.
    """    
    #colormap
    min_value = df_aggreg[column_name].min()
    max_value = df_aggreg[column_name].max()
    mean_value = df_aggreg[column_name].mean()
    print(f"Colour column min value {min_value}, max value {max_value}, mean value {mean_value}")
    print(f"Hexagon cell count: {df_aggreg['hex_id'].nunique()}")
    
    # the name of the layer just needs to be unique, put something silly there for now:
    name_layer = "Choropleth " + str(df_aggreg)
    
    if initial_map is None:
        initial_map = folium.Map(location= [47, 4], zoom_start=5.5, tiles="cartodbpositron")

    #create geojson data from dataframe
    geojson_data = hexagons_dataframe_to_geojson(df_hex = df_aggreg, column_name = column_name)

    # color_map_name 'Blues' for now, many more at https://matplotlib.org/stable/tutorials/colors/colormaps.html to choose from!
    custom_cm = matplotlib.cm.get_cmap(color_map_name)

    folium.GeoJson(
        geojson_data,
        style_function=lambda feature: {
            'fillColor': get_color(custom_cm, feature['properties'][column_name], vmin=min_value, vmax=max_value),
            'color': border_color,
            'weight': 1,
            'fillOpacity': fill_opacity 
        }, 
        name = name_layer
    ).add_to(initial_map)

    return initial_map