Experimental origin-destination matrix support
@Robinlovelace I'm experimenting with an origin-destination workflow using some of your PCT cycling data as a test case.
A prototype workflow is at analysis/od/prototype_od_weighted.py. It downloads PCT London OD pairs and zone boundaries, maps MSOA centroids to the GLA street network, and computes OD-weighted betweenness — where shortest paths are weighted by bicycle trip counts rather than treated uniformly.
The gist is something like this:
od = build_od_matrix(
od_df,
zones_gdf,
net,
origin_col="msoa1",
destination_col="msoa2",
weight_col="bicycle",
zone_id_col="geo_code",
)
nodes_od = networks.node_betweenness_od(
net,
nodes_gdf.copy(),
od_matrix=od,
distances=DISTANCES,
)Could you take a look and let me know if this is the sort of thing you are thinking of, or if you have ideas for how this can be pointed in the right direction?
This will require v4.23.0b11 for access to the new methods.
Experimental origin-destination matrix support
@Robinlovelace I'm experimenting with an origin-destination workflow using some of your PCT cycling data as a test case.
A prototype workflow is at
analysis/od/prototype_od_weighted.py. It downloads PCT London OD pairs and zone boundaries, maps MSOA centroids to the GLA street network, and computes OD-weighted betweenness — where shortest paths are weighted by bicycle trip counts rather than treated uniformly.The gist is something like this:
Could you take a look and let me know if this is the sort of thing you are thinking of, or if you have ideas for how this can be pointed in the right direction?
This will require
v4.23.0b11for access to the new methods.