CODEWITHSUNDEEP
graphopenstreetmapnetworkx
Kevin
Kevin

Reputation: 597

Reduce openstreetmap graph size in networkx

I have a graph (transformed from OSMNX) of London's walk path, containing 667.588 edges, with different highway attributes (the street types in openstreetmap). Running a shortest_path algorithm is quite slow (4 seconds). To improve the speed, I want to largely reduce the number of edges in a systematic way without losing main connections/city structures, but not sure how to do it? Any suggestions? Is there a way to group some close nodes to a more important one, thus reduce the size?

Upvotes: 2

Views: 2501

Answers (4)

Victor Duraes De Faria
Victor Duraes De Faria

Reputation: 7

The osmnx simplification module worked for me in this case https://osmnx.readthedocs.io/en/stable/osmnx.html#module-osmnx.simplification:

osmnx.simplification module

Simplify, correct, and consolidate network topology.

osmnx.simplification.consolidate_intersections(G, tolerance=10, rebuild_graph=True, dead_ends=False, reconnect_edges=True)

Consolidate intersections comprising clusters of nearby nodes.

osmnx.simplification.simplify_graph(G, strict=True, remove_rings=True)

Simplify a graph’s topology by removing interstitial nodes.

Upvotes: 0

Daki
Daki

Reputation: 31

The above solution does not work anymore since the networkx library has changed. Specifically

H.node[u] = G.node[u]

is not supported anymore. The following solution relies on the osmnx.geo_utils.induce_subgraph and used a node list as an argument to this function.

highways_to_keep = ['motorway', 'trunk', 'primary', 'secondary', 'tertiary']
H = nx.MultiDiGraph() # new graph
Hlist = [] # node list
for u,v,attr in G.edges(data=True):
    if "highway" in attr.keys():
        if attr['highway'] in highways_to_keep : 
            Hlist.append(G.nodes[u]['osmid'])
H = ox.geo_utils.induce_subgraph(G, Hlist)

Upvotes: 0

Alz
Alz

Reputation: 805

You can extract edges with desired highway types from your main graph G:

highways_to_keep = ['motorway', 'trunk', 'primary']
H = nx.MultiDiGraph()
for u,v,attr in G.edges(data=True):
    if attr['highway'] in highways_to_keep:
        H.add_edge(u,v,attr_dict=attr)
        H.node[u] = G.node[u]
        H.node[v] = G.node[v]

Here, we first initialized an empty MultiDiGraph, which is a type of graph used by OSMnx, then populate it with data from the main graph G, if the 'highway' attribute is in our list of highways_to_keep. You can find more about highway types in this OpenStreetMap page.

Our graph is a valid NetworkX graph, but you need to do one more thing before you can take advantage of OSMnx functionalities as well. if you execute G.graph, you will see graph attributes which contains crs (coordinate reference system) and some other things. you should add this information into your newly create graph:

H.graph = G.graph

here is the plot of H , osmnx.plot_graph(H):

enter image description here

Upvotes: 3

gboeing
gboeing

Reputation: 6412

It depends what type of network you're working with (e.g., walk, bike, drive, drive_service, all, etc.). The drive network type would be the smallest and prioritize major routes, but at the expense of pedestrian paths and passageways.

OSMnx also provides the ability to simplify the graph's topology with a built-in function. This is worth doing if you haven't already as it can reduce graph size by 90% sometimes while correctly retaining all intersection and dead-end nodes, as well as edge geometries, faithfully.

Upvotes: 0

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