momepy - cityseer

Last update

March 23, 2024

from future import annotations

import geopandas as gpd from osmnx import features

from cityseer.tools import io, graphs from cityseer.metrics import networks, layers

This example uses the momepy bubenec.gpkg GeoPandas GeoDataFrame as an example. Please download this file and place it in a locally accessible file. Update the filepaths in the next code block accordingly.

from pathlib import Path

import geopandas as gpd
from cityseer.tools import io, graphs
from cityseer.metrics import networks, layers
from osmnx import features

repo_path = Path.cwd()
if str(repo_path).endswith("examples"):
    repo_path = Path.cwd() / ".."
if not str(repo_path.resolve()).endswith("cityseer-examples"):
    raise ValueError(
        "Please check your notebook working directory relative to your project and data paths."
    )

bubenec_path = Path(repo_path / "temp/bubenec.gpkg")
print("data path:", bubenec_path)
print("path exists:", bubenec_path.exists())

df_streets: gpd.GeoDataFrame = gpd.read_file(bubenec_path, layer="streets")  # type: ignore
nx_momepy = io.nx_from_generic_geopandas(df_streets)

INFO:cityseer.tools.graphs:Merging parallel edges within buffer of 1.
100%|██████████| 35/35 [00:00<00:00, 17268.63it/s]

data path: /Users/gareth/dev/benchmark-urbanism/cityseer-examples/examples/../temp/bubenec.gpkg
path exists: True

At this point the input GeoPandas file has been converted to a networkX MultiGraph with geom attributes for the edges. This graph can now be fed to cityseer graph module methods if wanted. For running optimised network centrality or landuse accessibilities, use the io.network_structure_from_nx method to prepare the necessary structures.

nodes_gdf, edges_gdf, network_structure = io.network_structure_from_nx(
    nx_momepy, crs=df_streets.crs.to_epsg()
)

INFO:cityseer.tools.io:Preparing node and edge arrays from networkX graph.
100%|██████████| 29/29 [00:00<00:00, 14987.04it/s]
100%|██████████| 29/29 [00:00<00:00, 2427.94it/s]

The following block downloads some OSM data which will be used for demonstrating some landuse accessibility methods.

nodes_gdf.head()

	ns_node_idx	x	y	live	weight	geom
x1603585.64-y6464428.774	0	1.603586e+06	6.464429e+06	True	1	POINT (1603585.640 6464428.774)
x1603413.206-y6464228.73	1	1.603413e+06	6.464229e+06	True	1	POINT (1603413.206 6464228.730)
x1603268.502-y6464060.781	2	1.603269e+06	6.464061e+06	True	1	POINT (1603268.502 6464060.781)
x1603363.558-y6464031.885	3	1.603364e+06	6.464032e+06	True	1	POINT (1603363.558 6464031.885)
x1603607.303-y6464181.853	4	1.603607e+06	6.464182e+06	True	1	POINT (1603607.303 6464181.853)

edges_gdf.head(3)

	ns_edge_idx	end_ns_node_idx	nx_start_node_key	nx_end_node_key	length	angle_sum	imp_factor	in_bearing	out_bearing	total_bearing	geom
x1603585.64-y6464428.774-x1603413.206-y6464228.73	0	1	x1603585.64-y6464428.774	x1603413.206-y6464228.73	264.103950	0.000000	1	-130.760729	-130.760729	-130.760729	LINESTRING (1603585.640 6464428.774, 1603413.2...
x1603585.64-y6464428.774-x1603561.74-y6464494.467	1	8	x1603585.64-y6464428.774	x1603561.74-y6464494.467	70.020202	8.483583	1	111.541453	103.057871	109.992304	LINESTRING (1603561.740 6464494.467, 1603564.6...
x1603585.64-y6464428.774-x1603650.45-y6464368.601	2	6	x1603585.64-y6464428.774	x1603650.45-y6464368.601	88.924305	19.246836	1	-42.077556	-55.845266	-42.875276	LINESTRING (1603585.640 6464428.774, 1603603.0...

data_gdf = features.features_from_point(
    (50.1029248, 14.4029967), tags={"amenity": ["pub", "restaurant"]}, dist=400
)
data_gdf.to_crs(3857, inplace=True)
data_gdf = data_gdf.loc["node"]
data_gdf = data_gdf.reset_index(level=0, drop=True)
data_gdf.index = data_gdf.index.astype(str)
data_gdf = data_gdf[["amenity", "geometry"]]
print(data_gdf.head())

      amenity                         geometry
0  restaurant  POINT (1602992.986 6463522.177)
1  restaurant  POINT (1602903.753 6463784.958)
2  restaurant  POINT (1603489.527 6463552.391)
3  restaurant  POINT (1602809.665 6464167.222)
4  restaurant  POINT (1603027.863 6464060.660)

# compute metrics
nodes_gdf = networks.node_centrality_shortest(
    network_structure, nodes_gdf, [250, 500, 1000]
)
nodes_gdf, data_gdf = layers.compute_accessibilities(
    data_gdf,
    landuse_column_label="amenity",
    accessibility_keys=["restaurant"],
    nodes_gdf=nodes_gdf,
    network_structure=network_structure,
    distances=[100, 200, 400],
)
nodes_gdf.head()

INFO:cityseer.metrics.networks:Computing shortest path node centrality.
100%|██████████| 29/29 [00:01<00:00, 28.86it/s]
INFO:cityseer.metrics.layers:Computing land-use accessibility for: restaurant
100%|██████████| 29/29 [00:01<00:00, 28.87it/s]

	ns_node_idx	x	y	live	weight	geom	cc_beta_250	cc_beta_500	cc_beta_1000	cc_cycles_250	...	cc_betweenness_beta_250	cc_betweenness_beta_500	cc_betweenness_beta_1000	cc_restaurant_100_nw	cc_restaurant_100_wt	cc_restaurant_200_nw	cc_restaurant_200_wt	cc_restaurant_400_nw	cc_restaurant_400_wt	cc_restaurant_nearest_max_400
x1603585.64-y6464428.774	0	1.603586e+06	6.464429e+06	True	1	POINT (1603585.640 6464428.774)	0.716697	2.050036	4.991603	0.0	...	0.105017	1.056299	6.454368	0.0	0.000000	0.0	0.000000	1.0	0.043205	314.179047
x1603413.206-y6464228.73	1	1.603413e+06	6.464229e+06	True	1	POINT (1603413.206 6464228.730)	0.121535	1.309901	5.305615	0.0	...	0.000000	0.089877	5.084879	1.0	0.134929	1.0	0.367327	2.0	0.660077	50.075100
x1603268.502-y6464060.781	2	1.603269e+06	6.464061e+06	True	1	POINT (1603268.502 6464060.781)	0.434456	1.921538	5.837987	0.0	...	0.036077	0.693734	4.369751	1.0	0.309082	1.0	0.555951	5.0	0.895136	29.353716
x1603363.558-y6464031.885	3	1.603364e+06	6.464032e+06	True	1	POINT (1603363.558 6464031.885)	0.390683	1.850008	6.036365	1.0	...	0.000000	0.676959	6.886633	0.0	0.000000	1.0	0.075615	2.0	0.354570	129.104904
x1603607.303-y6464181.853	4	1.603607e+06	6.464182e+06	True	1	POINT (1603607.303 6464181.853)	0.284579	1.452106	4.887573	1.0	...	0.000000	0.263284	3.326667	0.0	0.000000	0.0	0.000000	1.0	0.082232	249.821609

5 rows × 37 columns

# if using dual network
nx_dual = graphs.nx_to_dual(nx_momepy)
nodes_gdf_dual, _edges_gdf_dual, network_structure_dual = io.network_structure_from_nx(
    nx_dual, df_streets.crs.to_epsg()
)
nodes_gdf_dual = networks.node_centrality_simplest(
    network_structure_dual, nodes_gdf_dual, [500]
)
nodes_gdf_dual.head()

INFO:cityseer.tools.graphs:Converting graph to dual.
INFO:cityseer.tools.graphs:Preparing dual nodes
100%|██████████| 35/35 [00:00<00:00, 8927.85it/s]
INFO:cityseer.tools.graphs:Preparing dual edges (splitting and welding geoms)
100%|██████████| 35/35 [00:00<00:00, 397.32it/s]
INFO:cityseer.tools.io:Preparing node and edge arrays from networkX graph.
100%|██████████| 35/35 [00:00<00:00, 16494.45it/s]
100%|██████████| 35/35 [00:00<00:00, 1757.82it/s]
INFO:cityseer.metrics.networks:Computing simplest path node centrality.
100%|██████████| 35/35 [00:01<00:00, 34.94it/s]

	ns_node_idx	x	y	live	weight	geom	primal_edge_node_a	primal_edge_node_b	cc_density_500_ang	cc_harmonic_500_ang	cc_hillier_500_ang	cc_farness_500_ang	cc_betweenness_500_ang
x1603413.206-y6464228.73_x1603585.64-y6464428.774_k0	0	1.603499e+06	6.464329e+06	True	1	POINT (1603499.423 6464328.752)	x1603585.64-y6464428.774	x1603413.206-y6464228.73	18.0	11.511215	10.933968	29.632427	10.0
x1603561.74-y6464494.467_x1603585.64-y6464428.774_k0	1	1.603573e+06	6.464461e+06	True	1	POINT (1603572.785 6464461.339)	x1603585.64-y6464428.774	x1603561.74-y6464494.467	13.0	8.308631	7.956708	21.239941	11.0
x1603585.64-y6464428.774_x1603650.45-y6464368.601_k0	2	1.603619e+06	6.464400e+06	True	1	POINT (1603619.295 6464399.737)	x1603585.64-y6464428.774	x1603650.45-y6464368.601	13.0	8.324493	8.047422	21.000513	16.0
x1603413.206-y6464228.73_x1603607.303-y6464181.853_k0	3	1.603510e+06	6.464204e+06	True	1	POINT (1603510.094 6464204.494)	x1603413.206-y6464228.73	x1603607.303-y6464181.853	21.0	13.298367	12.854933	34.305897	10.0
x1603363.558-y6464031.885_x1603413.206-y6464228.73_k0	4	1.603388e+06	6.464130e+06	True	1	POINT (1603388.005 6464130.401)	x1603413.206-y6464228.73	x1603363.558-y6464031.885	24.0	15.826625	15.015126	38.361317	10.0