Calculate centralities

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.
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.
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

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')
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()