Note

# Using two spatial weights matrices¶

Some functions are using spatial weights for two different purposes. Therefore two matrices have to be passed. We will illustrate this case measuring building adjacency and mean interbuilding distance.

```
[1]:
```

```
import momepy
import geopandas as gpd
import matplotlib.pyplot as plt
```

We will again use `osmnx`

to get the data for our example and after preprocessing of building layer will generate tessellation.

```
[2]:
```

```
import osmnx as ox
gdf = ox.footprints.footprints_from_place(place='Kahla, Germany')
gdf_projected = ox.projection.project_gdf(gdf)
buildings = momepy.preprocess(gdf_projected, size=30,
compactness=True, islands=True, verbose=False)
buildings['uID'] = momepy.unique_id(buildings)
limit = momepy.buffered_limit(buildings)
tessellation = momepy.Tessellation(buildings, unique_id='uID', limit=limit,
verbose=False).tessellation
```

## Building adjacency¶

Building adjacency is using `spatial_weights_higher`

to denote the area within which the calculation occurs (required) and `spatial_weights`

to denote adjacency of buildings (optional, the function can do it for us). We can use distance band of 200 meters to define `spatial_weights_higher`

.

```
[3]:
```

```
import libpysal
dist200 = libpysal.weights.DistanceBand.from_dataframe(buildings, 200,
ids='uID')
```

```
[4]:
```

```
adjac = momepy.BuildingAdjacency(
buildings, spatial_weights_higher=dist200, unique_id='uID')
buildings['adjacency'] = adjac.series
```

```
Calculating adjacency: 100%|██████████| 2005/2005 [00:00<00:00, 98379.52it/s]Calculating spatial weights...
Spatial weights ready...
```

```
[5]:
```

```
f, ax = plt.subplots(figsize=(10, 10))
buildings.plot(ax=ax, column='adjacency', legend=True, cmap='viridis', scheme='naturalbreaks', k=10)
ax.set_axis_off()
plt.show()
```

If we want to specify or reuse `spatial_weights`

, we can generate them as Queen contiguity weights. Using `libpysal`

or `momepy`

(momepy will use the same libpysal method, but you don’t need to import libpysal directly):

```
[6]:
```

```
queen = libpysal.weights.Queen.from_dataframe(buildings,
silence_warnings=True,
ids='uID')
queen = momepy.sw_high(k=1, gdf=buildings, ids='uID', contiguity='queen')
```

```
[7]:
```

```
buildings['adj2'] = momepy.BuildingAdjacency(buildings,
spatial_weights_higher=dist200,
unique_id='uID',
spatial_weights=queen).series
```

```
Calculating adjacency: 100%|██████████| 2005/2005 [00:00<00:00, 86549.47it/s]
```

```
[8]:
```

```
f, ax = plt.subplots(figsize=(10, 10))
buildings.plot(ax=ax, column='adj2', legend=True, cmap='viridis')
ax.set_axis_off()
plt.show()
```

## Mean interbuilding distance¶

Mean interbuilding distance is similar to `neighbour_distance`

, but it is calculated within vicinity defined in `spatial_weights_higher`

, while `spatial_weights`

captures immediate neighbours.

```
[9]:
```

```
sw1 = momepy.sw_high(k=1, gdf=tessellation, ids='uID')
sw3 = momepy.sw_high(k=3, gdf=tessellation, ids='uID')
```

```
[10]:
```

```
interblg_distance = momepy.MeanInterbuildingDistance(
buildings, sw1, 'uID', spatial_weights_higher=sw3)
buildings['mean_ib_dist'] = interblg_distance.series
```

```
7%|▋ | 140/2005 [00:00<00:02, 717.84it/s]Computing mean interbuilding distances...
100%|██████████| 2005/2005 [00:02<00:00, 782.58it/s]
```

`spatial_weights_higher`

is optional and can be derived from `spatial_weights`

as weights of higher order defined in `order`

.

```
[11]:
```

```
buildings['mean_ib_dist'] = momepy.MeanInterbuildingDistance(
buildings, sw1, 'uID', order=3).series
```

```
Generating weights matrix (Queen) of 3 topological steps...
4%|▍ | 85/2005 [00:00<00:02, 843.97it/s]Computing mean interbuilding distances...
100%|██████████| 2005/2005 [00:02<00:00, 820.91it/s]
```

```
[12]:
```

```
f, ax = plt.subplots(figsize=(10, 10))
buildings.plot(ax=ax, column='mean_ib_dist', scheme='quantiles', k=10, legend=True, cmap='viridis')
ax.set_axis_off()
plt.show()
```