数据集来源Geolife

加载数据

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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
from  matplotlib.colors import  rgb2hex
from shapely.geometry import MultiPoint
from geopy.distance import great_circle
from sklearn.cluster import KMeans
from sklearn.cluster import DBSCAN
userdata = 'd:/Lab-work/Geolife Trajectories 1.3/Data/001/Trajectory/'
filelist = os.listdir(userdata)
names = ['lat','lng','zero','alt','days','date','time']
df_list = [pd.read_csv(userdata + f,header=6,names=names,index_col=False) for f in filelist]  
df = pd.concat(df_list, ignore_index=True)
print(df.head(10))
plt.plot(df.lat, df.lng)
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         lat         lng  zero  alt          days        date      time
0  39.984198  116.319322     0  492  39744.245208  2008-10-23  05:53:06
1  39.984224  116.319402     0  492  39744.245266  2008-10-23  05:53:11
2  39.984211  116.319389     0  492  39744.245324  2008-10-23  05:53:16
3  39.984217  116.319422     0  491  39744.245382  2008-10-23  05:53:21
4  39.984710  116.319865     0  320  39744.245405  2008-10-23  05:53:23
5  39.984674  116.319810     0  325  39744.245463  2008-10-23  05:53:28
6  39.984623  116.319773     0  326  39744.245521  2008-10-23  05:53:33
7  39.984606  116.319732     0  327  39744.245579  2008-10-23  05:53:38
8  39.984555  116.319728     0  324  39744.245637  2008-10-23  05:53:43
9  39.984579  116.319769     0  309  39744.245694  2008-10-23  05:53:48
[<matplotlib.lines.Line2D at 0x17efc43eac8>]
39eBMn.png
39eBMn.png

K-Means

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coords = df[['lat','lng']].values
n_clusters = 100
cls = KMeans(n_clusters).fit(coords)
colors = tuple([(np.random.random(),np.random.random(), np.random.random()) for i in range(n_clusters)])
colors = [rgb2hex(x) for x in colors]
for i, color in enumerate(colors):
  members = cls.labels_ == i
  plt.scatter(coords[members, 0], coords[members, 1], s=60, c=color, alpha=0.5)
plt.show()
39mlYF.png
39mlYF.png

获取 K-Means 聚类结果

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cluster_labels = cls.labels_
num_clusters = len(set(cluster_labels) - set([-1]))
print('Clustered ' + str(len(df_min)) + ' points to ' + str(num_clusters) + ' clusters')
clusters = pd.Series([coords[cluster_labels == n] for n in range(num_clusters)])
print(clusters)
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Clustered 9045 points to 100 clusters
0     [[40.014459, 116.305603], [40.014363, 116.3056...
1     [[39.975246000000006, 116.358976], [39.975244,...
2     [[40.001312, 116.193358], [40.001351, 116.1932...
3     [[39.984559000000004, 116.326696], [39.984669,...
4     [[39.964969, 116.434923], [39.964886, 116.4350...
                            ...                        
95    [[40.004549, 116.260581], [40.004515999999995,...
96    [[39.97964, 116.323856], [39.979701, 116.32396...
97    [[40.0009, 116.23948500000002], [40.000831, 11...
98    [[39.962336, 116.32817800000001], [39.96223300...
99    [[39.9663, 116.353677], [39.966291999999996, 1...
Length: 100, dtype: object

获取每个群集每个中心点

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def get_centermost_point(cluster):
    centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)
    centermost_point = min(cluster, key=lambda point: great_circle(point, centroid).m)
    return tuple(centermost_point)
centermost_points = clusters.map(get_centermost_point)
lats, lons = zip(*centermost_points)
rep_points = pd.DataFrame({'lon':lons, 'lat':lats})
print(rep_points)
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           lon        lat
0   116.306558  40.013751
1   116.353295  39.975357
2   116.190167  40.004290
3   116.326944  39.986492
4   116.438241  39.961273
..         ...        ...
95  116.256309  40.004774
96  116.326462  39.978752
97  116.232672  39.998630
98  116.328847  39.958271
99  116.358655  39.966451

[100 rows x 2 columns]

描绘中心点

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fig, ax = plt.subplots(figsize=[10, 6])
rs_scatter = ax.scatter(rep_points['lon'][0], rep_points['lat'][0], c='#99cc99', edgecolor='None', alpha=0.7, s=450)
ax.scatter(rep_points['lon'][1], rep_points['lat'][1], c='#99cc99', edgecolor='None', alpha=0.7, s=250)
ax.scatter(rep_points['lon'][2], rep_points['lat'][2], c='#99cc99', edgecolor='None', alpha=0.7, s=250)
ax.scatter(rep_points['lon'][3], rep_points['lat'][3], c='#99cc99', edgecolor='None', alpha=0.7, s=150)
df_scatter = ax.scatter(df_min['lng'], df_min['lat'], c='k', alpha=0.9, s=3)
ax.set_title('Full GPS trace vs. DBSCAN clusters')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.legend([df_scatter, rs_scatter], ['GPS points', 'Cluster centers'], loc='upper right')
labels = ['cluster{0}'.format(i) for i in range(1, num_clusters+1)]
for label, x, y in zip(labels, rep_points['lon'], rep_points['lat']):
    plt.annotate(                                                
        label,
        xy = (x, y), xytext = (-25, -30),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'white', alpha = 0.5),
        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
plt.show()
39n7CD.png
39n7CD.png