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Adding GTFS transport data to centralityThis notebook demonstrates how to integrate GTFS (General Transit Feed Specification) public transport data into the street network before computing centrality metrics. By adding metro stops and segment travel times to the network, centrality calculations can account for the connectivity boost provided by public transport.The approach computes centralities twice: once without and once with the GTFS data, so that the effect of the metro network on centrality can be compared.

import geopandas as gpd
import matplotlib.pyplot as plt
from cityseer.metrics import networks
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)

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)

# prepare the data structures
nodes_gdf, edges_gdf, network_structure = io.network_structure_from_nx(G_dual)

INFO:cityseer.tools.io:Preparing node and edge arrays from networkX graph.
INFO:cityseer.graph:Edge R-tree built successfully with 104026 items.

Prepare the network and compute baseline centralities (without GTFS data). The _not suffix columns preserve the baseline values for later comparison.

distances = [1000, 2000, 5000]
nodes_gdf = networks.node_centrality_shortest(
    network_structure=network_structure,
    nodes_gdf=nodes_gdf,
    distances=distances,
)
for col in nodes_gdf.columns:
    if col.startswith("cc_"):
        nodes_gdf[f"{col}_not"] = nodes_gdf[col]

INFO:cityseer.metrics.networks:Computing shortest path node centrality.
INFO:cityseer.config:Metrics computed for:
INFO:cityseer.config:Distance: 1000m, Beta: 0.004, Walking Time: 12.5 minutes.
INFO:cityseer.config:Distance: 2000m, Beta: 0.002, Walking Time: 25.0 minutes.
INFO:cityseer.config:Distance: 5000m, Beta: 0.0008, Walking Time: 62.5 minutes.

Use add_transport_gtfs to load Madrid Metro GTFS data into the NetworkStructure. This adds metro stops as nodes and segment travel times as edges, enabling the shortest-path algorithms to route through the metro network.

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.

stops

	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
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
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
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
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
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
...	...	...	...	...	...	...	...	...	...	...	...	...	...
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
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
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
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
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

1272 rows × 13 columns

# 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

stops.avg_wait_time.describe()
# count nan
stops.avg_wait_time.isna().sum()
# impute

np.int64(982)

avg_stop_pairs

	prev_stop_id	next_stop_id	avg_segment_time
0	gtfs-data/madrid_gtfs/madrid_metro-par_4_1	gtfs-data/madrid_gtfs/madrid_metro-par_4_10	92.0
1	gtfs-data/madrid_gtfs/madrid_metro-par_4_10	gtfs-data/madrid_gtfs/madrid_metro-par_4_11	80.0
2	gtfs-data/madrid_gtfs/madrid_metro-par_4_100	gtfs-data/madrid_gtfs/madrid_metro-par_4_101	86.5
3	gtfs-data/madrid_gtfs/madrid_metro-par_4_101	gtfs-data/madrid_gtfs/madrid_metro-par_4_102	126.5
4	gtfs-data/madrid_gtfs/madrid_metro-par_4_102	gtfs-data/madrid_gtfs/madrid_metro-par_4_103	184.0
...	...	...	...
274	gtfs-data/madrid_gtfs/madrid_metro-par_4_94	gtfs-data/madrid_gtfs/madrid_metro-par_4_95	110.0
275	gtfs-data/madrid_gtfs/madrid_metro-par_4_95	gtfs-data/madrid_gtfs/madrid_metro-par_4_96	107.5
276	gtfs-data/madrid_gtfs/madrid_metro-par_4_96	gtfs-data/madrid_gtfs/madrid_metro-par_4_97	104.5
277	gtfs-data/madrid_gtfs/madrid_metro-par_4_97	gtfs-data/madrid_gtfs/madrid_metro-par_4_98	93.0
278	gtfs-data/madrid_gtfs/madrid_metro-par_4_98	gtfs-data/madrid_gtfs/madrid_metro-par_4_99	116.5

279 rows × 3 columns

Calculate centralities

The centrality methods can now be computed.

nodes_gdf = networks.node_centrality_shortest(
    network_structure=network_structure,
    nodes_gdf=nodes_gdf,
    distances=distances,
)

INFO:cityseer.metrics.networks:Computing shortest path node centrality.
INFO:cityseer.config:Metrics computed for:
INFO:cityseer.config:Distance: 1000m, Beta: 0.004, Walking Time: 12.5 minutes.
INFO:cityseer.config:Distance: 2000m, Beta: 0.002, Walking Time: 25.0 minutes.
INFO:cityseer.config:Distance: 5000m, Beta: 0.0008, Walking Time: 62.5 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_beta_1000', 'cc_beta_2000', 'cc_beta_5000',
       'cc_cycles_1000', 'cc_cycles_2000', 'cc_cycles_5000', 'cc_density_1000',
       'cc_density_2000', 'cc_density_5000', 'cc_farness_1000',
       'cc_farness_2000', 'cc_farness_5000', 'cc_harmonic_1000',
       'cc_harmonic_2000', 'cc_harmonic_5000', 'cc_hillier_1000',
       'cc_hillier_2000', 'cc_hillier_5000', 'cc_betweenness_1000',
       'cc_betweenness_2000', 'cc_betweenness_5000',
       'cc_betweenness_beta_1000', 'cc_betweenness_beta_2000',
       'cc_betweenness_beta_5000', 'cc_beta_1000_not', 'cc_beta_2000_not',
       'cc_beta_5000_not', 'cc_cycles_1000_not', 'cc_cycles_2000_not',
       'cc_cycles_5000_not', 'cc_density_1000_not', 'cc_density_2000_not',
       'cc_density_5000_not', 'cc_farness_1000_not', 'cc_farness_2000_not',
       'cc_farness_5000_not', 'cc_harmonic_1000_not', 'cc_harmonic_2000_not',
       'cc_harmonic_5000_not', 'cc_hillier_1000_not', 'cc_hillier_2000_not',
       'cc_hillier_5000_not', 'cc_betweenness_1000_not',
       'cc_betweenness_2000_not', 'cc_betweenness_5000_not',
       'cc_betweenness_beta_1000_not', 'cc_betweenness_beta_2000_not',
       'cc_betweenness_beta_5000_not'],
      dtype='object')

Compare resultsCompute the difference between the metro-integrated and baseline centralities. The three-panel plot below shows harmonic closeness centrality without the metro, with the metro, and the difference. Metro stop locations are overlaid in red.

nodes_gdf["cc_harmonic_1000_diff"] = (
    nodes_gdf["cc_harmonic_1000"] - nodes_gdf["cc_harmonic_1000_not"]
)

fig, axes = plt.subplots(3, 1, figsize=(8, 20), facecolor="#1d1d1d")

nodes_gdf.plot(
    column="cc_harmonic_1000_not",
    cmap="magma",
    legend=True,
    ax=axes[0],
    vmax=5,
)
axes[0].set_title(
    "Harmonic Closeness Centrality without Metro Stops",
    fontsize=8,
    color="#ddd",
)
stops_gdf.plot(ax=axes[0], color="red", markersize=1)

nodes_gdf.plot(
    column="cc_harmonic_1000",
    cmap="magma",
    legend=True,
    ax=axes[1],
    vmax=5,
)
axes[1].set_title(
    "Harmonic Closeness Centrality with Metro Stops",
    fontsize=8,
    color="#ddd",
)
stops_gdf.plot(ax=axes[1], color="red", markersize=1)

nodes_gdf.plot(
    column="cc_harmonic_1000_diff",
    cmap="viridis",
    legend=True,
    ax=axes[2],
)
axes[2].set_title(
    "Harmonic Closeness Centrality with difference due to Metro Stops",
    fontsize=8,
    color="#ddd",
)

for ax in axes:
    ax.set_xlim(438500, 438500 + 3500)
    ax.set_ylim(4472500, 4472500 + 3500)
    ax.axis(False)

plt.tight_layout()

ConclusionThis notebook demonstrated how to integrate GTFS public transport data (Madrid Metro) into the street network and compare centrality metrics with and without transit connectivity. By adding metro stops and segment travel times, the shortest-path algorithms can route through the transit network, revealing how public transport reshapes the pattern of closeness centrality across the city.Next steps: For GTFS with accessibility metrics, see GTFS Accessibility.