def simple_link_mapper(graph, strict=True):
"""
CREDIT: https://github.com/gboeing/osmnx/blob/master/osmnx/simplify.py
------- modified for different functionality -------
The default graph extracted has lots of links due to every bend in the road
being classified as a link. Due to graph having lots of links (and the map matcher requiring it to do so),
we need a way to map these smaller links to their simplified ones so we can
model the longer roads as desired. Helper function.
Returns a dictionary unsimplified edges to their corresponding simplified links
as well as the proportion (lengths) of those simplified links they represent.
Parameters:
----------
graph (networkx multidigraph)
strict (bool): if False, allow nodes to be end points even if they fail all other rules
but have edges with different OSM IDs
Returns:
-------
link_mapper (dictionary): Dictionary that maps unsimplified edges to their corresponding simplified links
as well as the proportion (lengths) of those simplified links they represent.
"""
# first identify all the nodes that are endpoints
endpoints = set([
node for node in graph.nodes()
if ox.is_endpoint(graph, node, strict=strict)
])
paths_to_simplify = []
# for each endpoint node, look at each of its successor nodes
for node in endpoints:
for successor in graph.successors(node):
if successor not in endpoints:
# if the successor is not an endpoint, build a path from the
# endpoint node to the next endpoint node
try:
path = ox.build_path(graph,
successor,
endpoints,
path=[node, successor])
paths_to_simplify.append(path)
except RuntimeError:
continue
else:
# just add node, successor into paths to simplify anyways (so it get's added to link_mapper later)
paths_to_simplify.append([node, successor])
# Now we have a list of paths to simplify
# we will now use the edges of a pre-simplified graph for comparison
simplified_graph = ox.simplify_graph(graph, strict=True)
simple_edge_data = return_edge_data(simplified_graph)
#also get the edge data for normal graph
edge_data = return_edge_data(graph)
link_mapper = {}
# now iterate through the paths to simplify
for path in paths_to_simplify:
# simplified edge corresponds to first and last of path
simple_edge = get_edge_name(path[0], path[-1])
simple_edge_length = simple_edge_data[simple_edge]['length']
# now iterate through interstitial nodes
for u, v in zip(path[:-1], path[1:]):
edge_name_1 = get_edge_name(u, v)
# now take into account other direction too
edge_name_2 = get_edge_name(v, u)
# now find what proportion of simple link length this link is
proportion = edge_data[edge_name_1]['length'] / simple_edge_length
link_mapper[edge_name_1] = {
'simple_link': simple_edge,
'proportion': proportion
}
link_mapper[edge_name_2] = {
'simple_link': simple_edge,
'proportion': proportion
}
return link_mapper
import matplotlib.pyplot as plt
import numpy as np
import osmnx as ox
import pandas as pd
import geopandas as gpd
import collections
ox.config(log_console=True, use_cache=True)
weight_by_length = False
print(ox.__version__)
# create a network around some (lat, lon) point but do not simplify it yet
location_point = (39.9057386, 116.3682035)
G = ox.graph_from_point(location_point,
network_type='drive_service',
distance=500,
simplify=False)
# turn off strict mode and see what nodes we'd remove, in red
nc = ['b' if ox.is_endpoint(G, node) else 'r' for node in G.nodes()]
fig, ax = ox.plot_graph(G, node_color=nc, node_zorder=3)
# simplify the network
SG = ox.simplify_graph(G)
fig, ax = ox.plot_graph(SG, node_color='b', node_zorder=3)
G = ox.graph_from_place('Khan Prampi Makara, Cambodia',
network_type='drive',
buffer_dist=200)
ec = [
'r' if data['oneway'] else 'blue'
for u, v, key, data in G.edges(keys=True, data=True)
]
ox.plot_graph(G, node_size=0, edge_color=ec)
# In[18]:
#visualize all the cul-de-sacs (or intersections of any other type) in a city to get a sense of these points of low network connectivity
# We can visualize all the cul-de-sacs for all networks
place = "Dangkao"
G = ox.graph_from_place(place, network_type='drive', simplify=False)
nc = ['r' if ox.is_endpoint(G, node) else 'grey' for node in G.nodes()]
fig, ax = ox.plot_graph(G, node_color=nc, node_zorder=3)
# In[19]:
# highlight one-way streets
ec = [
'r' if data['oneway'] else 'blue'
for u, v, key, data in G.edges(keys=True, data=True)
]
fig, ax = ox.plot_graph(G,
node_color='w',
node_edgecolor='k',
node_size=4,
node_zorder=3,
edge_color=ec,
def get_important_node_ids(G): # get rid of some mid-way shaping nodes nds = [] for nid in G.nodes(): if ox.is_endpoint(G, nid, strict=True): nds.append(nid) return nds