Network Preparation

cityseer uses networkx MultiGraphs to represent pedestrian networks. Spatial information is embedded in the graph nodes and edges so that cityseer can accurately manipulate the graphs and calculate derivative metrics. The workflows in these notebooks automatically prepare the network and should be sufficient for the great majority of cases.

These recipes deliberately teach the lower-level tools.io and tools.graphs route, which offers step-by-step control over network construction and cleaning. If you do not need that control, the higher-level CityNetwork constructors (from_osm, from_geopandas, from_nx) wrap the same preparation behind a single call. Every recipe here ends by handing the prepared graph to CityNetwork.from_nx, so whichever preparation route you take, the analysis workflow lands on the high-level API.

If you wish to prepare the network manually, note the following requirements:

• The network must be a networkx MultiGraph object.
• cityseer is focused on pedestrian networks, which do not ordinarily have directionality. Transportation networks are added at a later step in a way that retains directionality.
• The network must have a coordinate reference system associated with it, e.g. G.graph['crs'] = 27700. When converting from osmnx or geopandas, this is handled automatically.
• Nodes require x and y attributes storing coordinates in a projected CRS. Optional attributes: live (compute metrics for this node or use it for routing only) and weight (relative importance in centrality calculations).
• Edges store a geom attribute containing a shapely LineString. The graph topology should reflect the real-world topology of the street network: curvatures and turns within a street segment belong in the LineString geometry, not as additional nodes and segments, which would distort centrality measures.

Notebook	Description
create_from_bbox	OSM network from a bounding box, including CRS handling.
create_from_buffered_point	OSM network from a buffered coordinate.
network_from_bounds	OSM network from a custom boundary file.
network_from_relation_id	OSM network from an OSM relation id.
network_from_streets	Custom network from a geopandas streets dataset.
osmnx_to_cityseer	Convert a network from osmnx.
momepy_to_cityseer	Convert a network from momepy.
save_to_file	Save a networkx graph to a file.
create_dual_graph	Create a dual graph from a primal graph.
network_simplification	Manually configured network simplification for OSM data.
directed_networks	Directed networks and one-way streets: building directed graphs from OSMnx, oneway columns, and comparing directed against undirected betweenness.
results_to_maps	From results to maps: export computed metrics to a GeoPackage, style them in QGIS, and build a publication-quality matplotlib figure.

On simplification: good sources of street network data, such as Ordnance Survey OS Open Roads, are already simplified to their essential structure with topology kept distinct from street geometry. When a high-quality source is available, it may be best not to attempt additional clean-up. Cleaning and simplification is recommended when working with OSM data; cityseer has an automated cleaning routine, and the simplification notebook shows manual configuration.