基于dbscan算法对文件夹下多个plt格式的Geolife Trajectories1.3的gps轨迹数据文件进行轨迹聚类,并且以folium高德常规地图可视化轨迹数据
时间: 2024-03-09 09:44:23 浏览: 143
好的,我了解了您的问题。首先,您需要安装以下库:
- sklearn
- pandas
- numpy
- folium
然后,您可以按照以下步骤进行轨迹聚类和可视化:
1. 导入所需库:
```
import os
import pandas as pd
import numpy as np
from sklearn.cluster import DBSCAN
import folium
from folium.plugins import HeatMap
```
2. 定义函数来读取数据并将其转换为用于聚类的格式:
```
def read_file(file_path):
data = pd.read_csv(file_path, header=None, names=['lat', 'lon', 'zero', 'alt', 'days', 'date', 'time'])
data = data.drop(['zero', 'alt'], axis=1)
data = data.dropna()
data['datetime'] = pd.to_datetime(data['days'].astype(int).astype(str) + ' ' + data['time'])
data['lat'] = data['lat'].astype(float)
data['lon'] = data['lon'].astype(float)
data = data.drop(['days', 'date', 'time'], axis=1)
return np.array(data[['lat', 'lon']].values.tolist())
```
3. 定义函数来执行聚类:
```
def perform_clustering(data, eps, min_samples):
db = DBSCAN(eps=eps, min_samples=min_samples, algorithm='ball_tree', metric='haversine').fit(np.radians(data))
cluster_labels = db.labels_
num_clusters = len(set(cluster_labels))
return cluster_labels, num_clusters
```
4. 定义函数来可视化聚类结果:
```
def visualize_clusters(data, cluster_labels, num_clusters):
m = folium.Map(location=[data[:, 0].mean(), data[:, 1].mean()], zoom_start=12)
colors = ['red', 'blue', 'green', 'purple', 'orange', 'darkred',
'lightred', 'beige', 'darkblue', 'darkgreen', 'cadetblue',
'darkpurple', 'pink', 'lightblue', 'lightgreen', 'gray',
'black', 'lightgray']
for i in range(num_clusters):
cluster_data = data[cluster_labels == i]
if len(cluster_data) > 0:
HeatMap(cluster_data, radius=15, blur=10, max_zoom=13, gradient={0.4: colors[i % len(colors)]}).add_to(m)
return m
```
5. 遍历文件夹中的所有文件并聚类它们:
```
file_path = 'Geolife Trajectories 1.3/Data'
eps = 0.5 # 聚类半径
min_samples = 100 # 最小样本数
for file in os.listdir(file_path):
if file.endswith('.plt'):
file_full_path = os.path.join(file_path, file)
data = read_file(file_full_path)
cluster_labels, num_clusters = perform_clustering(data, eps, min_samples)
m = visualize_clusters(data, cluster_labels, num_clusters)
m.save(file_full_path + '.html')
```
这将为每个文件创建一个 HTML 文件,其中包含可视化的聚类结果。
希望这可以帮助您实现您的项目!
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