按位置对 osmnx 地图进行着色（使用 graph_from_place() 方法）

Question

We plot 2 places:

# Get data
import osmnx as ox
place = ["Broughton Hackett", "Crowle"]
G = ox.graph_from_place(place, retain_all=True, simplify = True, network_type='all')

# Prepare data
u = []
v = []
key = []
data = []
for uu, vv, kkey, ddata in G.edges(keys=True, data=True): # added ww
    u.append(uu)
    v.append(vv)
    key.append(kkey)
    data.append(ddata)

# Define colors according to length of streets
roadCols = []

for item in data:    
    if "length" in item.keys():
        if item["length"] <= 200:
            color = "#FFFFF0"             
        elif item["length"] > 200 and item["length"] <= 1000:
            color = "#6a0dad"  
        else:
            color = "#00FFFF"
    else:
        color = "#FFFFFF"         
    roadCols.append(color)

#Plot 
bgcolor = "#1e1e1e"
fig, ax = ox.plot_graph(G, node_size=0, 
                        dpi = 100,bgcolor = bgcolor,
                        save = False, edge_color=roadCols,
                        edge_linewidth=1, edge_alpha=1)
fig.tight_layout(pad=0)
fig.savefig("stack.jpg", dpi=100, bbox_inches='tight', format="jpg", 
            facecolor=fig.get_facecolor(), transparent=False)

As we can see in output below, streets have been colored according to their length.

My question: how do we color streets by place , not length? So that all streets of the first village are in one color, and that all streets of the other village are in another color?

Contrary to length, the place is not contained in the data object, and I don't know how to retrieve it.

This is a mock example and I have intentionally chosen tiny villages. In reality I want to color by a series of whole cities.

Answer 1

• core to being able to color edges based on place is knowing the place
• have used approach of getting osmnx * graph for each place and assigning place to it
• then it's simple to use geopandas / folium integration to color edges
• also shown what appears to be preferred way matplotlib

Matplotlib

# Get data
import osmnx as ox
import pandas as pd
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt

plt.style.use('dark_background')

place = ["Broughton Hackett", "Crowle"]
gdf_nodes = gdf_edges = None
for place in place:
    G = ox.graph_from_place(place, retain_all=True, simplify = True, network_type='all')
    n_, e_ = ox.graph_to_gdfs(G)
    n_["place"] = place
    e_["place"] = place
    if gdf_nodes is None:
        gdf_nodes = n_
        gdf_edges = e_
    else:
        gdf_nodes = pd.concat([gdf_nodes, n_])
        gdf_edges = pd.concat([gdf_edges, e_])

# it's a bit imperfect way to map a value to a color
colors = {'Broughton Hackett':'red', 'Crowle':'blue'}
gdf_edges.plot(column="place", cmap=ListedColormap([colors[k] for k in sorted(colors.keys())]))

folium

# Get data
import osmnx as ox
import pandas as pd
place = ["Broughton Hackett", "Crowle"]
gdf_nodes = gdf_edges = None
for place in place:
    G = ox.graph_from_place(place, retain_all=True, simplify = True, network_type='all')
    n_, e_ = ox.graph_to_gdfs(G)
    n_["place"] = place
    e_["place"] = place
    if gdf_nodes is None:
        gdf_nodes = n_
        gdf_edges = e_
    else:
        gdf_nodes = pd.concat([gdf_nodes, n_])
        gdf_edges = pd.concat([gdf_edges, e_])

gdf_edges.explore(column="place", height=300, width=500)