使用KDtree查找最近networkx最近节点
from sklearn.neighbors import KDTree
import networkx as nx
from geopy.distance import geodesic
def create_kdtree(G):
coordinates = []
node_list = []
for node, data in G.nodes(data=True):
coordinates.append([data['lat'], data['lon']])
node_list.append(node)
kd_tree = KDTree(coordinates)
return kd_tree, node_list
def find_nearest_node(G, kd_tree, node_list, point):
dist, ind = kd_tree.query([point], k=1)
nearest_node = node_list[ind[0][0]]
# Get coordinates of nearest node
nearest_point = (G.nodes[nearest_node]['lat'], G.nodes[nearest_node]['lon'])
# Calculate geodesic distance using geopy, 保留两位小数
distance = round(geodesic(point, nearest_point).meters,2)
return nearest_node, distance
def find_nearest_nodes(G, kd_tree, node_list, gdf):
# 批次查找最近的节点
nearest_nodes = []
distances = []
for point in gdf.geometry:
nearest_node, distance = find_nearest_node(G, kd_tree, node_list, (point.y, point.x))
nearest_nodes.append(nearest_node)
distances.append(distance)
gdf['nearest_node'] = nearest_nodes
gdf['distance'] = distances
return gdf