Extract graph

# Import libraries
import numpy as np
import pandas as pd

from pytrack.graph import graph, distance
from pytrack.analytics import visualization

df = pd.read_excel("dataset.xlsx")
df

	datetime	latitude	longitude
0	23-04-21 16:46:19:583000	43.759650	11.291561
1	23-04-21 16:46:36:570000	43.759645	11.291544
2	23-04-21 16:46:52:647000	43.759671	11.291162
3	23-04-21 16:47:37:568000	43.759677	11.291148
4	23-04-21 16:47:49:639000	43.759691	11.290932
...	...	...	...
94	23-04-21 17:12:37:573000	43.779596	11.254733
95	23-04-21 17:12:51:592000	43.779583	11.254295
96	23-04-21 17:13:05:572000	43.779206	11.253978
97	23-04-21 17:13:20:592000	43.779205	11.253974
98	23-04-21 17:13:36:590000	43.778464	11.253364

99 rows × 3 columns

latitude = df["latitude"].to_list()
longitude = df["longitude"].to_list()

points = [(lat, lon) for lat, lon in zip(latitude, longitude)]

# Create BBOX
north, east = np.max(np.array([*points]), 0)
south, west = np.min(np.array([*points]), 0)

# Extract road network graph
G = graph.graph_from_bbox(*distance.enlarge_bbox(north, south, west, east, 500), simplify=True, network_type='drive')

Downloaded 1,773.29kB

# Show extracted graph
maps = visualization.Map(location=(np.mean(latitude), np.mean(longitude)))
maps.add_graph(G, plot_nodes=True)
maps

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