Note

# 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 Písek, Czechia. We retrieve its network from OSM and convert it to 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 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')
```

```
100%|██████████| 557/557 [00:00<00:00, 774.73it/s]
```

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')
```

```
100%|██████████| 557/557 [00:00<00:00, 1662.95it/s]
```

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:

```
[14]:
```

```
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/test/lib/python3.8/site-packages/mapclassify/classifiers.py:235: UserWarning: Warning: Not enough unique values in array to form k classes
Warn(
/opt/miniconda3/envs/test/lib/python3.8/site-packages/mapclassify/classifiers.py:238: UserWarning: Warning: setting k to 3
Warn("Warning: setting k to %d" % k_q, UserWarning)
```

Meshedness based on topological distance:

```
[15]:
```

```
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:

```
[16]:
```

```
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()
```