city2graph documentation#
city2graph is a Python library for converting datasets of geospatial relations into graphs. It is designed to facilitate the graph data analytics in particular for urban studies and spatial analysis. It supports Pytorch Geometric to enable graph representation learning, such as Graph Neural Networks (GNNs).
Features#
Construct graphs from morphological datasets (e.g. buildings, streets, and land use)
Construct graphs from transportation datasets (e.g. public transport of buses, trams, and trains)
Construct graphs from contiguity datasets (e.g. land use, land cover, and administrative boundaries)
Construct graphs from mobility datasets (e.g. bike-sharing, migration, and pedestrian flows)
Convert geospatial data into pytorch tensors for graph representation learning, such as Graph Neural Networks (GNNs)
Examples#
Morphology
morphological_graph = city2graph.morphological_graph(
buildings_gdf,
segments_gdf,
center_point,
distance=500
)
A morphological graph of 500m walking distance in Liverpool#
>> For details, see Constructing Morphological Networks from Overture Maps
Transportation
sample_gtfs_path = Path("./itm_london_gtfs.zip")
gtfs_data = city2graph.load_gtfs(sample_gtfs_path)
travel_summary_gdf = city2graph.travel_summary_network(
gtfs_data, calendar_start="20250401", calendar_end="20250401", as_gdf=True
)
A travel summary graph of bus transportation between stops in London#
>> For details, see Processing GTFS to Obtain Flows of Public Transportation
Proximity
gilbert_graph = city2graph.gilbert_graph(poi_gdf, radius=100)
wax_l1 = city2graph.waxman_graph(poi_gdf,
distance_metric="manhattan",
r0=100,
beta=0.5,
as_gdf=True)
wax_l2 = city2graph.waxman_graph(poi_gdf,
distance_metric="euclidean",
r0=100,
beta=0.5,
as_gdf=True)
wax_net = city2graph.waxman_graph(poi_gdf,
distance_metric="network",
r0=100,
beta=0.5,
network_gdf=segments_gdf.to_crs(epsg=6677),
as_gdf=True)
>> For details, see Generating Various Types of Graphs by Proximity
Documentation#
Contents: