python中的经度和纬度聚类

我正在处理一个具有纬度和长度数据的数据框，我需要将彼此距离最近的点（200米）聚集在一起。这就是我在Python中所做的。python中的经度和纬度聚类

order_lat order_long 
0 19.111841 72.910729 
1 19.111342 72.908387 
2 19.111342 72.908387 
3 19.137815 72.914085 
4 19.119677 72.905081 
5 19.119677 72.905081 
6 19.119677 72.905081 
7 19.120217 72.907121 
8 19.120217 72.907121 
9 19.119677 72.905081 
10 19.119677 72.905081 
11 19.119677 72.905081 
12 19.111860 72.911346 
13 19.111860 72.911346 
14 19.119677 72.905081 
15 19.119677 72.905081 
16 19.119677 72.905081 
17 19.137815 72.914085 
18 19.115380 72.909144 
19 19.115380 72.909144 
20 19.116168 72.909573 
21 19.119677 72.905081 
22 19.137815 72.914085 
23 19.137815 72.914085 
24 19.112955 72.910102 
25 19.112955 72.910102 
26 19.112955 72.910102 
27 19.119677 72.905081 
28 19.119677 72.905081 
29 19.115380 72.909144 
30 19.119677 72.905081 
31 19.119677 72.905081 
32 19.119677 72.905081 
33 19.119677 72.905081 
34 19.119677 72.905081 
35 19.111860 72.911346 
36 19.111841 72.910729 
37 19.131674 72.918510 
38 19.119677 72.905081 
39 19.111860 72.911346 
40 19.111860 72.911346 
41 19.111841 72.910729 
42 19.111841 72.910729 
43 19.111841 72.910729 
44 19.115380 72.909144 
45 19.116625 72.909185 
46 19.115671 72.908985 
47 19.119677 72.905081 
48 19.119677 72.905081 
49 19.119677 72.905081 
50 19.116183 72.909646 
51 19.113827 72.893833 
52 19.119677 72.905081 
53 19.114100 72.894985 
54 19.107491 72.901760 
55 19.119677 72.905081

然后我发现每对lat和长与每个其它对LAT和长在数据帧之间的距离。

lat_array = np.radians(np.array(order_data['order_lat'])) 
long_array = np.radians(np.array(order_data['order_long'])) 

distance = [] 
pairs_lat1 = [] 
pairs_long1 = [] 
pairs_lat2 = [] 
pairs_long2 = [] 
for i in range(len(lat_array)): 
    for j in range(i+1,len(lat_array)): 
     dlon = long_array[j]-long_array[i] 
     dlat = lat_array[j]-lat_array[i] 
     a = np.sin(dlat/2)**2 + np.cos(lat_array[i]) * np.cos(lat_array[j]) 
      * np.sin(dlon/2)**2 
     c = 2 * 6371 * np.arcsin(np.sqrt(a)) 
     pairs_lat1.append(lat_array[i]) 
     pairs_long1.append(long_array[i]) 
     pairs_lat2.append(lat_array[j]) 
     pairs_long2.append(long_array[j]) 
     distance.append(c) 

df_distance = pd.DataFrame() 
df_distance['lat1'] = np.rad2deg(pairs_lat1) 
df_distance['long1'] = np.rad2deg(pairs_long1) 
df_distance['lat2'] = np.rad2deg(pairs_lat2) 
df_distance['long2'] = np.rad2deg(pairs_long2)  
df_distance['distance'] = distance 


df_distance.head() 

     lat1  long1  lat2  long2  distance 
0  19.111841 72.910729 19.111342 72.908387 2.522482e-01 
1  19.111841 72.910729 19.111342 72.908387 2.522482e-01 
2  19.111841 72.910729 19.137815 72.914085 2.909520e+00 
3  19.111841 72.910729 19.119677 72.905081 1.054209e+00 
4  19.111841 72.910729 19.119677 72.905081 1.054209e+00 
5  19.111841 72.910729 19.119677 72.905081 1.054209e+00

，给了我一对之间的距离（LAT1，long1 & LAT2，long2）252米我怎样才能聚集点？所以最近的点在一起。可以说在250米范围内。我可以在我的情况下使用层次聚类吗？

来源

2016-01-02 Neil

非常相似：http://stackoverflow.com/questions/24617013/convert-latitude-and-longitude-to-x-and-y-grid-system-using-python – jbg

最简单的方法是建立一个包含任意两点之间距离的距离矩阵，然后使用任何经典的聚类算法。 Scikit-learn是最流行的聚类库（其他许多事情）之一。您也可以尝试GVM，这是专门为地理空间聚类设计的。

来源

2016-01-02 08:23:23

python中的经度和纬度聚类

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