Note

# Street network analysis#

Graph analysis offers three modes, of which the first two are used within `momepy`

: - node-based - value per node - edge-based - value per edge - network-based - single value per network

```
[1]:
```

```
import momepy
import osmnx as ox
```

In this notebook, we will look at Písek, 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.convert.to_undirected(streets_graph),
nodes=False,
edges=True,
node_geometry=False,
fill_edge_geometry=True,
)
```

```
/Users/martin/miniforge3/envs/momepy/lib/python3.11/site-packages/osmnx/graph.py:392: FutureWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.
polygon = gdf_place["geometry"].unary_union
```

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

```
[3]:
```

```
ax = streets.plot(figsize=(8, 8), linewidth=0.2)
ax.set_axis_off()
```

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]:
```

```
ax = nodes.plot(
column="clustering",
markersize=100,
legend=True,
cmap="viridis",
scheme="quantiles",
alpha=0.5,
zorder=2,
figsize=(8, 8),
)
streets.plot(ax=ax, color="lightgrey", alpha=0.5, zorder=1)
ax.set_axis_off()
```

```
/Users/martin/miniforge3/envs/momepy/lib/python3.11/site-packages/mapclassify/classifiers.py:1592: UserWarning: Not enough unique values in array to form 5 classes. Setting k to 3.
self.bins = quantile(y, k=k)
```

Meshedness based on topological distance:

```
[10]:
```

```
ax = nodes.plot(
column="meshedness",
markersize=100,
legend=True,
cmap="viridis",
alpha=0.5,
zorder=2,
scheme="quantiles",
figsize=(8, 8),
)
streets.plot(ax=ax, color="lightgrey", alpha=0.5, zorder=1)
ax.set_axis_off()
```

And meshedness based on 400 metres:

```
[11]:
```

```
ax = nodes.plot(
column="meshedness400",
markersize=100,
legend=True,
cmap="viridis",
alpha=0.5,
zorder=2,
scheme="quantiles",
figsize=(8, 8),
)
streets.plot(ax=ax, color="lightgrey", alpha=0.5, zorder=1)
ax.set_axis_off()
```