[Python] Folium 사용

import folium
from folium import Choropleth, Circle, Marker
from folium.plugins import HeatMap, MarkerCluster

# Create a map
m_1 = folium.Map(location=[42.32,-71.0589], tiles='openstreetmap', zoom_start=10)

# Display the map
m_1


* tiles
cartodbdark_matter
cartodbpositron
cartodbpositronnolabels
cartodbpositrononlylabels
cloudmade
mapbox
mapboxbright
mapboxcontrolroom
openstreetmap
stamenterrain
stamentoner
stamentonerbackground
stamentonerlabels
stamenwatercolor


# Add points to the map
for idx, row in daytime_robberies.iterrows():
    Marker([row['Lat'], row['Long']]).add_to(m_2)



# Add Cluster to the map
mc = MarkerCluster()
for idx, row in daytime_robberies.iterrows():
    if not math.isnan(row['Long']) and not math.isnan(row['Lat']):
        mc.add_child(Marker([row['Lat'], row['Long']]))
m_3.add_child(mc)


def color_producer(val):
    if val <= 12:
        return 'forestgreen'
    else:
        return 'darkred'


# Add a bubble map to the base map
for i in range(0,len(daytime_robberies)):
    Circle(
        location=[daytime_robberies.iloc[i]['Lat'], daytime_robberies.iloc[i]['Long']],
        radius=20,
        popup= '...' ,
        color=color_producer(daytime_robberies.iloc[i]['HOUR'])).add_to(m_4)




# Add a heatmap to the base map
HeatMap(data=crimes[['Lat', 'Long']], radius=10).add_to(m_5)




# Add a choropleth map to the base map
Choropleth(geo_data=districts.__geo_interface__, 
           data=plot_dict, 
           key_on="feature.id", 
           fill_color='YlGnBu', 
           legend_name='Major criminal incidents (Jan-Aug 2018)'
          ).add_to(m_6)



geo_data is a GeoJSON FeatureCollection containing the boundaries of each geographical area.

In the code above, we convert the districts GeoDataFrame to a GeoJSON FeatureCollection with the __geo_interface__ attribute.


data is a Pandas Series containing the values that will be used to color-code each geographical area.
key_on will always be set to feature.id.

This refers to the fact that the GeoDataFrame used for geo_data and the Pandas Series provided in data have the same index. To understand the details, we'd have to look more closely at the structure of a GeoJSON Feature Collection (where the value corresponding to the "features" key is a list, wherein each entry is a dictionary containing an "id" key).


fill_color sets the color scale.
legend_name labels the legend in the top right corner of the map.