Interactive maps on Leaflet¶

Whenever you go into a website that has some kind of interactive map, it is quite probable that you are wittnessing a map that has been made with a JavaScipt library called Leaflet (the other popular one that you might have wittnessed is called OpenLayers).

There is also a Python module called Folium that makes it possible visualize data that’s been manipulated in Python on an interactive Leaflet map.

Creating a simple interactive web-map¶

Let’s first see how we can do a simple interactive web-map without any data on it. We just visualize OpenStreetMap on a specific location of the a world.

First thing that we need to do is to create a Map instance. There are few parameters that we can use to adjust how in our Map instance that will affect how the background map will look like. We should already be able to see what our map looks like.

import folium

# Create a Map instance
m = folium.Map(location=[58.37, 26.72], zoom_start=11, control_scale=True)
m

The first parameter location takes a pair of lat, lon values as list as an input which will determine where the map will be positioned when user opens up the map. zoom_start -parameter adjusts the default zoom-level for the map (the higher the number the closer the zoom is). control_scale defines if map should have a scalebar or not.

We can already save the map without any content. It will now just show the basemap in such a way that we initialized it. Let’s save the map as Data/base_map.html.

# Filepath to the output
outfp = r"Data\base_map.html"

# Save the map
m.save(outfp)

Take a look at the map by clicking it with right mouse and open it with Google Chrome which then opens it up in a web browser.

Let’s change the basemap style to Stamen Toner and change the location of our map slightly. The tiles -parameter is used for changing the background map provider and map style (see here for all possible ones).

# Let's change the basemap style to 'Stamen Toner'
m = folium.Map(location=[58.37, 26.72], tiles='Stamen Toner', zoom_start=12, control_scale=True, prefer_canvas=True)
m

And in order to save the file:

# Filepath to the output
outfp = r"Data\base_map2.html"

# Save the map
m.save(outfp)

Task

Play around with the parameters and save the map and see how those changes affect the look of the map.

Adding layers to the map¶

Adding layers to a web-map is fairly straightforward and similar procedure as with Bokeh and we can use familiar tools to handle the data, i.e. Geopandas. Our ultimate aim is to create a plot like this where population in Tartumaa, road network and the schools are plotted on top of a web-map:

First we need to prepare the data.

from fiona.crs import from_epsg
from shapely.geometry import LineString, MultiLineString

# Filepaths
grid_fp = r"L5_Data\population_square_km.shp"
roads_fp = r"L5_Data\roads.shp"
schools_fp = r"L5_Data\schools_tartu.shp"

# Read files
grid = gpd.read_file(grid_fp)
roads = gpd.read_file(roads_fp)
schools = gpd.read_file(schools_fp)

# Re-project to WGS84
grid['geometry'] = grid['geometry'].to_crs(epsg=4326)
roads['geometry'] = roads['geometry'].to_crs(epsg=4326)
schools['geometry'] = schools['geometry'].to_crs(epsg=4326)

# Make a selection (only data above 0 and below 1000)
grid = grid.loc[(grid['Population'] > 0)]

# Create a Geo-id which is needed by the Folium (it needs to have a unique identifier for each row)
grid['geoid'] = grid.index.astype(str)
schools['geoid'] = schools.index.astype(str)

# Select data
grid = grid[['geoid', 'Population', 'geometry']]

# Save the file as geojson
jsontxt = grid.to_json()

Now we have our population data stored in the jsontxt variable as GeoJSON format which basically contains the data as text in a similar way that it would be written in a .geojson -file.

Now we can start visualizing our data with Folium.

from folium.plugins import MarkerCluster

map_osm = folium.Map(location=[58.37, 26.72], tiles='Stamen toner', zoom_start=8, control_scale=True, prefer_canvas=True, width=600, height=450)

# Create a Clustered map where points are clustered
marker_cluster = MarkerCluster().add_to(map_osm)

# Create Choropleth map where the colors are coming from a column "Population".
# Notice: 'geoid' column that we created earlier needs to be assigned always as the first column
# with threshold_scale we can adjust the class intervals for the values
map_osm.choropleth(geo_data=jsontxt, data=grid, columns=['geoid', 'Population'], key_on="feature.id",
                   fill_color='YlOrRd', fill_opacity=0.5, line_opacity=0.2, line_color='white', line_weight=0,
                   threshold_scale=[0, 5, 10, 100, 1000],
                   legend_name='Population in Tartu', highlight=False, smooth_factor=1.0)

def getLinesAsPointList(row, geom):
    """Returns a list of coordinate pair tuples for the line ('lat', 'lon') of a LineString geometry"""
    if isinstance(row[geom], MultiLineString):
        return []
    else:
        list_x = list(row[geom].coords.xy[0])
        list_y = list(row[geom].coords.xy[1])
        # we need lat lon order for the folium map!!!
        return list(zip(list_y, list_x))

# Calculate x and y coordinates of the line
roads['points_list'] = roads.apply(getLinesAsPointList, geom='geometry', axis=1)

# list of lat lon coordinate pair tuples
roadpoints = list(roads['points_list'])

for road in roadpoints:
    folium.PolyLine(road, color="grey", weight=2.5, opacity=1).add_to(map_osm)

# Create Schools points on top of the map
for idx, row in schools.iterrows():
    # Get lat and lon of points
    lon = row['geometry'].x
    lat = row['geometry'].y

    # Get schools name information
    names = row['name']
    # Add marker to the map
    folium.RegularPolygonMarker(location=[lat, lon], popup=names, fill_color='#2b8cbe', number_of_sides=6, radius=8).add_to(marker_cluster)


# Save the output
outfp = r'Data\pop15.html'
map_osm.save(outfp)

That’s it! Now we have a cool interactive map with some markers on it and grid showing the population in the Tartumaa Region on top of a basemap. Open it with your browser and see the result.