accessibility_metro – Cityseer Guide

import geopandas as gpd
import matplotlib.pyplot as plt
from cityseer.metrics import layers
from cityseer.tools import graphs, io

streets_gpd = gpd.read_file("data/madrid_streets/street_network.gpkg")
streets_gpd = streets_gpd.explode(reset_index=True)
G = io.nx_from_generic_geopandas(streets_gpd)
G_dual = graphs.nx_to_dual(G)
nodes_gdf, _edges_gdf, network_structure = io.network_structure_from_nx(G_dual)

INFO:cityseer.tools.graphs:Merging parallel edges within buffer of 1.
INFO:cityseer.tools.graphs:Converting graph to dual.
INFO:cityseer.tools.graphs:Preparing dual nodes
INFO:cityseer.tools.graphs:Preparing dual edges (splitting and welding geoms)
INFO:cityseer.tools.io:Preparing node and edge arrays from networkX graph.
INFO:cityseer.graph:Edge R-tree built successfully with 104026 items.

prems_gpd = gpd.read_file("data/madrid_premises/madrid_premises.gpkg")
prems_gpd.head()

	index	local_id	local_distr_id	local_distr_desc	local_neighb_id	local_neighb_desc	local_neighb_code	local_census_section_id	local_census_section_desc	section_id	section_desc	division_id	division_desc	epigraph_id	epigraph_desc	easting	northing	geometry
0	0	10003324	1	CENTRO	105	UNIVERSIDAD	5	1091	91	I	hospitality	56	food_bev	561001	RESTAURANTE	440181.6	4475586.5	POINT (440181.6 4475586.5)
1	1	10003330	1	CENTRO	105	UNIVERSIDAD	5	1115	115	R	art_rec_entert	90	creat_entert	900003	TEATRO Y ACTIVIDADES ESCENICAS REALIZADAS EN D...	440000.6	4474761.5	POINT (440000.6 4474761.5)
2	2	10003356	1	CENTRO	104	JUSTICIA	4	1074	74	I	hospitality	56	food_bev	561004	BAR RESTAURANTE	440618.6	4474692.5	POINT (440618.6 4474692.5)
3	3	10003364	1	CENTRO	104	JUSTICIA	4	1075	75	G	wholesale_retail_motor	47	retail	472401	COMERCIO AL POR MENOR DE PAN Y PRODUCTOS DE PA...	440666.6	4474909.5	POINT (440666.6 4474909.5)
4	4	10003367	1	CENTRO	106	SOL	6	1119	119	G	wholesale_retail_motor	47	retail	477701	COMERCIO AL POR MENOR DE JOYAS, RELOJERIA Y BI...	440378.6	4474380.5	POINT (440378.6 4474380.5)

# compute accessibility
distances = [800, 1600]
nodes_gdf, prems_gpd = layers.compute_accessibilities(
    prems_gpd,
    landuse_column_label="division_desc",
    accessibility_keys=["retail"],
    nodes_gdf=nodes_gdf,
    network_structure=network_structure,
    distances=distances,
)
# keep a copy before adding GTFS for comparison
for col in nodes_gdf.columns:
    if col.startswith("cc_"):
        nodes_gdf[f"{col}_not"] = nodes_gdf[col]

INFO:cityseer.metrics.layers:Computing land-use accessibility for: retail
INFO:cityseer.metrics.layers:Assigning data to network.
INFO:cityseer.data:Assigning 117358 data entries to network nodes (max_dist: 100).
INFO:cityseer.data:Finished assigning data. 198773 assignments added to 24576 nodes.
INFO:cityseer.config:Metrics computed for:
INFO:cityseer.config:Distance: 800m, Beta: 0.005, Walking Time: 10.0 minutes.
INFO:cityseer.config:Distance: 1600m, Beta: 0.0025, Walking Time: 20.0 minutes.

network_structure, stops, avg_stop_pairs = io.add_transport_gtfs(
    "data/madrid_gtfs/madrid_metro",
    network_structure,
    nodes_gdf.crs,
)

INFO:cityseer.tools.io:Loading GTFS data from data/madrid_gtfs/madrid_metro
INFO:cityseer.tools.io:Loaded 1272 stops and 2360 stop times
INFO:cityseer.tools.io:Adding GTFS stops to network nodes (with street linking logic).
INFO:cityseer.tools.io:Generating segment durations between stops.
INFO:cityseer.tools.io:Adding GTFS segments to network edges.
INFO:cityseer.graph:Edge R-tree built successfully with 104026 items.

# convert stops to geopandas
stops_gdf = gpd.GeoDataFrame(
    stops,
    geometry=gpd.points_from_xy(stops["stop_lon"], stops["stop_lat"]),
    crs=4326,  # Adjust the CRS to suit your data if needed
)
stops_gdf = stops_gdf.to_crs(streets_gpd.crs.to_epsg())
stops_gdf

	stop_id	stop_code	stop_name	stop_desc	stop_lat	stop_lon	zone_id	stop_url	location_type	parent_station	stop_timezone	wheelchair_boarding	avg_wait_time	geometry
0	gtfs-data/madrid_gtfs/madrid_metro-par_4_1	1	PLAZA DE CASTILLA	Paseo de la Castellana 189	40.46690	-3.68917	A	http://www.crtm.es	0	est_90_21	NaN	0	430.357143	POINT (441575.052 4479808.7)
1	gtfs-data/madrid_gtfs/madrid_metro-acc_4_1_1	1	Plaza de Castilla	Paseo de la Castellana 189	40.46682	-3.68918	NaN	http://www.crtm.es	2	est_90_21	NaN	0	NaN	POINT (441574.135 4479799.826)
2	gtfs-data/madrid_gtfs/madrid_metro-acc_4_1_1040	1	Ascensor	Plaza de Castilla 9	40.46555	-3.68877	NaN	http://www.crtm.es	2	est_90_21	NaN	0	NaN	POINT (441607.793 4479658.584)
3	gtfs-data/madrid_gtfs/madrid_metro-acc_4_1_1043	1	Intercambiador Superficie	Paseo de la Castellana 191 B	40.46728	-3.68915	NaN	http://www.crtm.es	2	est_90_21	NaN	0	NaN	POINT (441577.077 4479850.867)
4	gtfs-data/madrid_gtfs/madrid_metro-acc_4_1_1044	1	Ascensor	Paseo de la Castellana 189	40.46702	-3.68918	NaN	http://www.crtm.es	2	est_90_21	NaN	0	NaN	POINT (441574.308 4479822.026)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1267	gtfs-data/madrid_gtfs/madrid_metro-est_4_234	234	HOSPITAL SEVERO OCHOA	Avda Orellana 3	40.32177	-3.76797	B1	http://www.crtm.es	1	NaN	Europe/Madrid	2	NaN	POINT (434754.721 4463754.488)
1268	gtfs-data/madrid_gtfs/madrid_metro-est_4_235	235	LEGANES CENTRAL	Calle Virgen del Camino 1	40.32899	-3.77154	B1	http://www.crtm.es	1	NaN	Europe/Madrid	2	NaN	POINT (434458.401 4464558.532)
1269	gtfs-data/madrid_gtfs/madrid_metro-est_4_236	236	SAN NICASIO	Avda Mar Mediterráneo SN	40.33616	-3.77587	B1	http://www.crtm.es	1	NaN	Europe/Madrid	2	NaN	POINT (434097.544 4465357.604)
1270	gtfs-data/madrid_gtfs/madrid_metro-par_4_237	237	OPERA	Plaza de Isabel II 9	40.41809	-3.70928	A	http://www.crtm.es	0	est_4_36	NaN	0	13.214286	POINT (439826.687 4474404.289)
1271	gtfs-data/madrid_gtfs/madrid_metro-par_4_238	238	PRINCIPE PIO	Paseo de la Florida 2	40.42099	-3.72033	A	http://www.crtm.es	0	est_90_18	NaN	1	13.214286	POINT (438891.852 4474733.771)

1272 rows × 14 columns

nodes_gdf, prems_gpd = layers.compute_accessibilities(
    prems_gpd,
    landuse_column_label="division_desc",
    accessibility_keys=["retail"],
    nodes_gdf=nodes_gdf,
    network_structure=network_structure,
    distances=distances,
)

INFO:cityseer.metrics.layers:Computing land-use accessibility for: retail
INFO:cityseer.metrics.layers:Assigning data to network.
INFO:cityseer.data:Assigning 117358 data entries to network nodes (max_dist: 100).
INFO:cityseer.data:Finished assigning data. 198773 assignments added to 24576 nodes.
INFO:cityseer.config:Metrics computed for:
INFO:cityseer.config:Distance: 800m, Beta: 0.005, Walking Time: 10.0 minutes.
INFO:cityseer.config:Distance: 1600m, Beta: 0.0025, Walking Time: 20.0 minutes.

nodes_gdf.columns

Index(['ns_node_idx', 'x', 'y', 'live', 'weight', 'primal_edge',
       'primal_edge_node_a', 'primal_edge_node_b', 'primal_edge_idx',
       'dual_node', 'cc_retail_800_nw', 'cc_retail_800_wt',
       'cc_retail_1600_nw', 'cc_retail_1600_wt', 'cc_retail_nearest_max_1600',
       'cc_retail_800_nw_not', 'cc_retail_800_wt_not', 'cc_retail_1600_nw_not',
       'cc_retail_1600_wt_not', 'cc_retail_nearest_max_1600_not'],
      dtype='object')

nodes_gdf["cc_retail_800_wt_diff"] = (
    nodes_gdf["cc_retail_800_wt"] - nodes_gdf["cc_retail_800_wt_not"]
)

fig, axes = plt.subplots(3, 1, figsize=(8, 20), facecolor="#1d1d1d")
nodes_gdf.plot(
    column="cc_retail_800_wt_not",
    cmap="magma",
    legend=True,
    ax=axes[0],
    vmax=450,
)
axes[0].set_title(
    "Retail Accessibility without Metro Stops",
    fontsize=8,
    color="#ddd",
)
stops_gdf.plot(ax=axes[0], color="red", markersize=1)

nodes_gdf.plot(
    column="cc_retail_800_wt",
    cmap="magma",
    legend=True,
    ax=axes[1],
    vmax=450,
)
axes[1].set_title(
    "Retail Accessibility with Metro Stops",
    fontsize=8,
    color="#ddd",
)
stops_gdf.plot(ax=axes[1], color="red", markersize=1)

nodes_gdf.plot(
    column="cc_retail_800_wt_diff",
    cmap="viridis",
    legend=True,
    ax=axes[2],
)
axes[2].set_title(
    "Retail Accessibility with difference due to Metro Stops",
    fontsize=8,
    color="#ddd",
)

for ax in axes:
    prems_gpd[prems_gpd["division_desc"] == "retail"].plot(
        markersize=1,
        edgecolor=None,
        color="white",
        legend=False,
        ax=ax,
    )
    ax.set_xlim(438500, 438500 + 3500)
    ax.set_ylim(4472500, 4472500 + 3500)
    ax.axis(False)

plt.tight_layout()