Network connectivity
[1]:
import pandas as pd
blocks_gdf = pd.read_pickle('./../../data/saint_petersburg/blocks.pickle')
accessibility_matrix = pd.read_pickle('./../../data/saint_petersburg/accessibility_matrix_intermodal.pickle')
We can actually use any accessibility from network.accessibility module
[2]:
from blocksnet.analysis.network.accessibility import mean_accessibility
accessibility_df = mean_accessibility(accessibility_matrix)
accessibility_df.head()
/home/vasilstar/masterplanning/.venv/lib/python3.10/site-packages/pandas/io/formats/format.py:1458: RuntimeWarning: overflow encountered in cast
has_large_values = (abs_vals > 1e6).any()
[2]:
| mean_accessibility | |
|---|---|
| 0 | 102.875 |
| 1 | 101.875 |
| 2 | 135.875 |
| 3 | 138.500 |
| 4 | 124.000 |
[4]:
from blocksnet.analysis.network import calculate_connectivity
connectivity_df = calculate_connectivity(accessibility_df)
connectivity_df.head()
[4]:
| connectivity | |
|---|---|
| 0 | 0.009721 |
| 1 | 0.009816 |
| 2 | 0.007360 |
| 3 | 0.007220 |
| 4 | 0.008065 |
[5]:
blocks_gdf[['geometry']].join(connectivity_df).plot('connectivity', legend=True, figsize=(10,8)).set_axis_off()
