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Adding GTFS transport data to accessibility

This notebook demonstrates how GTFS (General Transit Feed Specification) public transport data can be integrated into the street network before computing landuse accessibility metrics. By adding metro stops and segment travel times, the accessibility calculations can account for the extended reach provided by public transport.

Accessibility to retail premises is computed twice: once on the street network alone, and again after adding the Madrid Metro. The difference reveals how the metro network extends the catchment area for retail access.

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 452252 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 baseline retail accessibility (without GTFS data). The _not suffix columns preserve the baseline values for later comparison.

# 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. 199450 assignments added to 26679 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.

Load the Madrid Metro GTFS data into the NetworkStructure using add_transport_gtfs. This adds metro stops as nodes and segment travel times as edges.

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 452252 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

Recompute retail accessibility, now with the metro network included. The shortest-path algorithms can now route through metro segments, extending the effective catchment area.

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. 199450 assignments added to 26679 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.

Compare results

Compute the difference between the metro-integrated and baseline retail accessibility. The three-panel plot below shows weighted retail accessibility within 800m without the metro, with the metro, and the difference. Retail premises are shown in white and metro stops in red.

nodes_gdf.columns

Index(['ns_node_idx', 'x', 'y', 'z', '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()

Conclusion

This notebook demonstrated how to integrate GTFS public transport data (Madrid Metro) into the street network and compare retail accessibility with and without transit connectivity. The before-and-after comparison reveals how the metro extends the effective catchment area for retail access, particularly around metro stop locations.

Next steps: For centrality with GTFS data, see GTFS Centrality.