Fixing your road network graph

Fixing your road network graph#

Note: This notebook includes processes covered in the Step 2 Tutorial Notebook.

The following notebook showcases some common practices to inspect the quality of your road-network graph, and possibilities to improve it.

Import Libraries and Load Data#

We will start by importing the necessary libraries and loading the data.

Import Libraries#

We will use the following libraries:

  • os to set the working directory and handle file paths

  • networkx to handle the graph data structure

  • osmnx to plot the network graph

  • pickle to load the network data

  • matplotlib to help plot and format the graph

  • GOSTnets to access custom GOSTnets functionality

import os
import networkx as nx
import osmnx as ox
import pickle as pkl
import matplotlib.pyplot as plt

# import the GOSTnets library
import GOSTnets as gn

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 5
      3 import osmnx as ox
      4 import pickle as pkl
----> 5 import matplotlib.pyplot as plt
      7 # import the GOSTnets library
      8 import GOSTnets as gn

ModuleNotFoundError: No module named 'matplotlib'

Load Data#

Define the path to the tutorial data and then load the network.

pth = "./"  # change this path to your working folder
data_pth = os.path.join(pth, "tutorial_outputs")

# read back your graph from step 2 from you saved pickle
G = pkl.load(open(os.path.join(data_pth, "iceland_network_clean.pickle"), "rb"))

Graph processing#

In this notebook the first processing step we take is to load the largest subgraph.

Identify the largest subgraph#

We will start by identifying the largest subgraph in the network.

# note the use of sorted to sort by number of edges
list_of_subgraphs = [
    G.subgraph(c).copy()
    for c in sorted(nx.strongly_connected_components(G), key=len, reverse=True)
]

G_largest = list_of_subgraphs[0]

print(G_largest)

MultiDiGraph with 14740 nodes and 37322 edges

Plot the largest subgraph#

We will then plot the largest subgraph to visualize the network.

# plotting functions only work if the graphs have a name and a crs attribute
G_largest.graph["crs"] = "epsg:32646"
G_largest.graph["name"] = "Iceland"

fig, ax = plt.subplots(figsize=(10, 14))
ax.set_facecolor("k")
ax.set_title("Iceland - The Largest Network")
# largest connected subgraph
fig, ax = ox.plot_graph(G_largest, ax=ax, edge_linewidth=1, node_size=7)
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[6], line 5
      2 G_largest.graph["crs"] = "epsg:32646"
      3 G_largest.graph["name"] = "Iceland"
----> 5 fig, ax = plt.subplots(figsize=(10, 14))
      6 ax.set_facecolor("k")
      7 ax.set_title("Iceland - The Largest Network")

NameError: name 'plt' is not defined

edges_largest = gn.edge_gdf_from_graph(G_largest)
edges_largest.to_csv(os.path.join(data_pth, "edges_largest.csv"))

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[7], line 1
----> 1 edges_largest = gn.edge_gdf_from_graph(G_largest)
      2 edges_largest.to_csv(os.path.join(data_pth, "edges_largest.csv"))

NameError: name 'gn' is not defined

The next step would be to inspect changes in QGIS, add new paths to improve connectivity, and re-run the graph import notebooks to produce a better connected graph.

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