-
Notifications
You must be signed in to change notification settings - Fork 1
/
OSMnx使用教程.py
108 lines (83 loc) · 4.53 KB
/
OSMnx使用教程.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
# -*- codeing = utf-8 -*-
# @Time :2023/2/23 17:22
# @Author :xming
# @Version :1.0
# @Descriptioon :
# @Link :https://zhuanlan.zhihu.com/p/546002046
# @Link :https://blog.csdn.net/qq_39805362/article/details/122233420
# @Link :https://blog.csdn.net/weixin_41512727/article/details/80583157
# @Link :https://blog.csdn.net/qq_40206371/article/details/120407459
# @Link :https://zhuanlan.zhihu.com/p/488015442
# @Link :https://blog.csdn.net/weixin_41194129/article/details/111308381
# @File : OSMnx使用教程.py
import osmnx as ox
# from pint.testsuite.test_matplotlib import plt
# ox.config(request_kwargs={})
# OSMnx，简称ox。其中主要分为两种地图模型，一种是区块数据（gdf_from_place）,另外一种是道路模型（graph_from_place）。
def shortest_path_length():
# 获取最短路径距离
G = ox.graph_from_address("广州大学", network_type='all') # 第一步，获取道路数据
ox.plot_graph(G)
origin_point = (23.039506, 113.364664) # 广州大学校门坐标
destination_point = (23.074058, 113.386148) # 中山大学校门坐标
origin_node = ox.get_nearest_node(G, origin_point) # 获取O最邻近的道路节点
destination_node = ox.get_nearest_node(G, destination_point) # 获取D最邻近的道路节点
route = ox.shortest_path(G, origin_node, destination_node, weight='length') # 请求获取最短路径
distance = ox.shortest_path_length(G, origin_node, destination_node, weight='length') # 并获取路径长度
fig, ax = ox.plot_graph_route(G, route, origin_point=origin_point, destination_point=destination_point) # 可视化结果
print(str(distance)) # 输出最短路径距离
# 一行代码完成下载和建模可步行、可驾驶或可骑自行车的城市网络
def graph_from_place():
G = ox.graph_from_place('Modena, Italy')
# 以openstreetmap底图为背景绘图
G_84 = ox.project_graph(G, to_crs='EPSG:4326')
ox.plot_graph_folium(G_84, tiles='openstreetmap', kwargs={'width': 0.1})
# ox.plot_graph(G)
# 自动下载行政地点边界和 shapefile
def geocode_to_gdf():
city = ox.geocode_to_gdf('Chencang, Baoji, Shaanxi, China')
ax = ox.project_gdf(city).plot()
_ = ax.axis('off')
# 下载和建模街道网络(通过包围盒）
def graph_from_bbox():
G = ox.graph_from_bbox(30.255904285611525, 30.141649815003344, 120.2972282472983, 120.2239798312058,
network_type='drive')
# 形成路网图 https://blog.csdn.net/qq_39805362/article/details/122233420
G = ox.project_graph(G, to_crs='EPSG:4326')
ox.plot_graph_folium(G, tiles='openstreetmap', kwargs={'width': 0.1})
# ox.plot_graph(G)
# plt.rcParams['figure.dpi'] = 400
# fig = plt.figure(figsize=(10, 6)) # 设置画布大小
# ax = plt.gca()
# ox.plot_graph(G, ax=ax, figsize=(8 * 4), bgcolor='white', node_color='blue', edge_color='grey', show=True,
# edge_linewidth=0.3, node_size=5, node_alpha=0.5)
# 获得距离经纬度点 0.75 公里（沿网络）内的街道网络（通过原点）
def graph_from_point():
"""
center_point (tuple) – 构建 graph 的中心点
dist (int) – 仅保留距离图中心 dist 米的节点（ 当 dist_type 的值为 'bbox' 时 ）
"""
G = ox.graph_from_point(
ox.geocoder.geocode('天下汇（高新店）, 宝鸡'),
dist=750,
network_type='all'
)
ox.plot_graph(G)
# 将同一个交叉口的所有nodes合并 原文链接：https://blog.csdn.net/qq_39805362/article/details/122233420
# G = ox.consolidate_intersections(G, tolerance=25, rebuild_graph=True, dead_ends=False,
# reconnect_edges=True)
# 爬取萧山的路网拓扑结果并形成networkx的数据结构
# osmnx.graph_from_***很费时间，下载下来的graph如果需要重复试图用应该存在本地，存成一个.osm file。下次直接graph_from_xml。例子：
def save_graph_xml():
G = ox.graph_from_bbox(30.531952, 30.140702, -81.475139, -81.817727, network_type='drive')
ox.save_graph_xml(G, 'test.osm')
ox.save_graph_shapefile(G, filepath='test.shp')
G = ox.graph_from_xml('test.osm')
xml = ox.graph_from_xml(r"E:\WorkSpace\pyWorkSpace\osmnx-examples\notebooks\input_data\West-Oakland.osm")
ox.plot_graph(xml)
# toDo 下面有问题未解决
# 将同一个交叉口的所有nodes合并
G = ox.consolidate_intersections(xml, tolerance=25, rebuild_graph=False, dead_ends=False)
# 原文链接：https://blog.csdn.net/qq_39805362/article/details/122233420
ox.plot_graph(G)
# shortest_path_length()