Note

Interactive online version:

# Street network analysis#

Graph analysis offers three modes, of which the first two are used within momepy (as per v0.2): - node-based - value per node - edge-based - value per edge - network-based - single value per network

[1]:

import momepy
import geopandas as gpd
import osmnx as ox
import matplotlib.pyplot as plt


In this notebook, we will look at PĂ­sek, Czechia. We retrieve its network from OSM and convert it to a GeoDataFrame:

[2]:

streets_graph = ox.graph_from_place('Pisek, Czechia', network_type='drive')
streets_graph = ox.projection.project_graph(streets_graph)

streets = ox.graph_to_gdfs(ox.get_undirected(streets_graph), nodes=False, edges=True,
node_geometry=False, fill_edge_geometry=True)


Note: See the detailed explanation of these steps in the centrality notebook.

[3]:

f, ax = plt.subplots(figsize=(10, 10))
streets.plot(ax=ax, linewidth=0.2)
ax.set_axis_off()
plt.show()


We can generate a networkX.MultiGraph, which is used within momepy for network analysis, using gdf_to_nx.

[4]:

graph = momepy.gdf_to_nx(streets)


## Node-based analysis#

Once we have the graph, we can use momepy functions, like the one measuring clustering:

[5]:

graph = momepy.clustering(graph, name='clustering')


### Using sub-graph#

Momepy includes local characters measured on the network within a certain radius from each node, like meshedness. The function will generate ego_graph for each node so that it might take a while for more extensive networks. Radius can be defined topologically:

[6]:

graph = momepy.meshedness(graph, radius=5, name='meshedness')


Or metrically, using distance which has been saved as an edge argument by gdf_to_nx (or any other weight).

[7]:

graph = momepy.meshedness(graph, radius=400, name='meshedness400',
distance='mm_len')


Once we have finished the graph-based analysis, we can go back to GeoPandas. In this notebook, we are interested in nodes only:

[8]:

nodes = momepy.nx_to_gdf(graph, points=True, lines=False, spatial_weights=False)


Now we can plot our results in a standard way, or link them to other elements (using get_node_id).

Clustering:

[9]:

f, ax = plt.subplots(figsize=(10, 10))
nodes.plot(ax=ax, column='clustering', markersize=100, legend=True, cmap='viridis',
scheme='quantiles', alpha=0.5, zorder=2)
streets.plot(ax=ax, color='lightgrey', alpha=0.5, zorder=1)
ax.set_axis_off()
plt.show()

/opt/miniconda3/envs/geo_dev/lib/python3.9/site-packages/mapclassify/classifiers.py:234: UserWarning: Warning: Not enough unique values in array to form k classes
Warn(
/opt/miniconda3/envs/geo_dev/lib/python3.9/site-packages/mapclassify/classifiers.py:237: UserWarning: Warning: setting k to 3
Warn("Warning: setting k to %d" % k_q, UserWarning)


Meshedness based on topological distance:

[10]:

f, ax = plt.subplots(figsize=(10, 10))
nodes.plot(ax=ax, column='meshedness', markersize=100, legend=True, cmap='viridis',
alpha=0.5, zorder=2, scheme='quantiles')
streets.plot(ax=ax, color='lightgrey', alpha=0.5, zorder=1)
ax.set_axis_off()
plt.show()


And meshedness based on 400 metres:

[11]:

f, ax = plt.subplots(figsize=(10, 10))
nodes.plot(ax=ax, column='meshedness400', markersize=100, legend=True, cmap='viridis',
alpha=0.5, zorder=2, scheme='quantiles')
streets.plot(ax=ax, color='lightgrey', alpha=0.5, zorder=1)
ax.set_axis_off()
plt.show()