如何将 python 中的纬度和经度数据聚类（或删除不需要的数据）？

Question

我在 pandas df 中有一个大小为 (34000 * 2) 的纬度和经度数据

df =

Index       Latitude            Longitude
0           66.36031097267725   23.714807357485936
1           66.36030099322495   23.71479548193769
2
.
.
.
.
34000       66.27918383581169   23.568631229948359

Important Note : The above Lat & Long route has been covered twice which means if I cover the route only once, then my Latitude and Longitude data will be of size (34000/2, 2) for example.

问题

我只需要特定选定区域的纬度和经度数据。所以我在我的 df 中使用开始和结束的 Lat 和 Long 点进行过滤。这样做时，该区域的另一部分也被选中。 (See picture below after filtering)

要求

如何删除附加区域？我相信这个问题会有一些简单的方法。注意 : 过滤后的纬度和经度数据也覆盖了两次。

过滤

def apply_geofence_on_data(interpolated_data, min_latitude=66.27832887852133, max_latitude=66.37098470528755, min_longitude=23.568626549485927,
                               max_longitude=23.71481685393929):

    interpolated_data = interpolated_data[interpolated_data['Latitude'] > min_latitude]
    interpolated_data = interpolated_data[interpolated_data['Latitude'] < max_latitude]
    interpolated_data = interpolated_data[interpolated_data['Longitude'] < max_longitude]
    interpolated_data = interpolated_data[interpolated_data['Longitude'] > min_longitude]

    return interpolated_data

Answer 1

这里是测试的解决方案：想法是将所有点都放在线上方。你选择P的值到select右边的行。

from random import uniform
import matplotlib.pyplot as plt

def newpoint(lon_min = -180.0, lon_max = 180.0, lat_min = -90.0, lat_max = 90.0 ):#long,lat
    return uniform(lon_min, lon_max), uniform(lat_min, lat_max)

lon_min = 23.568626549485927; lon_max = 23.71481685393929
lat_min = 66.27832887852133; lat_max = 66.37098470528755
p = 0.25 # i have taken this value for sample, for your case i think a value nearer from 0.75

# i generate 10 points for sample
n=10
points = (newpoint(lon_min, lon_max, lat_min, lat_max) for x in range(n))
points = [x for x in points]
Lon = [x for x,y in points]
Lat = [x for y,x in points]
df = pd.DataFrame({'Lat': Lat, 'Lon': Lon})
print(df)

#equation of the line using points A and B -> y=m*x + z 
m = (lat_max - lat_min)/(lon_max - lon_min)
z = lat_min - m * (lon_min + p * (lon_max - lon_min))
xa = lon_min + p * (lon_max - lon_min)
xb = lon_max

#you could uncomment to display result 
#df['calcul'] = df['Lon'] * m + z

#select only points above the line
df = df[df['Lon'] * m + z < df['Lat']]
print(df)

#plot to show result
plt.plot([xa, xb] , [m * xa + z, m * xb + z])
plt.plot(df.Lon, df.Lat, 'ro')
plt.show()

初始输出：

         Lat        Lon
0  66.343486  23.674008
1  66.281614  23.678554
2  66.359215  23.637975
3  66.303976  23.659128
4  66.302640  23.589577
5  66.313877  23.634785
6  66.309733  23.683281
7  66.365582  23.667262
8  66.344611  23.688108
9  66.352028  23.673376

最终结果：点索引 1、3 和 6 已被推迟（它们在线下方）

         Lat        Lon
0  66.343486  23.674008
2  66.359215  23.637975
4  66.302640  23.589577
5  66.313877  23.634785
7  66.365582  23.667262
8  66.344611  23.688108
9  66.352028  23.673376

如何将 python 中的纬度和经度数据聚类（或删除不需要的数据）？

How to cluster Latitude and longitude data in python (or remove unwanted data)?

machine-learning

scipy

geopy

python-3.x

pandas

问题

要求

过滤