Python：如何将给定距离内的点组合在一起？

Posted 2023-03-05

【中文标题】Python：如何将给定距离内的点组合在一起？

【英文标题】：Python: how to group together points in a given distance?

【发布时间】：2016-04-29 10:05:37

【问题描述】：

我有一个数据框，其中包含用户在不同点（纬度/经度）之间的原始目的地旅行。所以我们有Origin_X, Origin_Y 和Destination_X, Destination_Y

df:

Trip Origin_X  Origin_Y  Destination_X Destination_Y
1   -33.55682 -70.78614   -33.44007     -70.6552
2   -33.49097 -70.77741   -33.48908     -70.76263
3   -33.37108 -70.6711    -33.73425     -70.76278

我想将起点和终点处半径为1km 的所有Trip 组合在一起。如果两个行程的起点和终点距离为d<=1km，则可以将它们分组。为了计算两个坐标之间的距离，我使用了haversine 函数。

def haversine(lon1, lat1, lon2, lat2):
    """
    Calculate the great circle distance between two points 
    on the earth (specified in decimal degrees)
    """
    # convert decimal degrees to radians 
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine formula 
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    r = 6371 # Radius of earth in kilometers. Use 3956 for miles
    return c * r

【问题讨论】：

请检查此问题以获取计算半正弦的矢量化方法，您可以将其添加为新的距离列，然后对 df 进行存储/过滤：***.com/questions/25767596/…

【参考方案1】：

你可以这样做：

import pandas as pd
from math import *

def haversine(row):
    """
    Calculate the great circle distance between two points 
    on the earth (specified in decimal degrees)
    """
    # convert decimal degrees to radians 
    lon1 = row[1]
    lat1 = row[2]
    lon2 = row[3]
    lat2 = row[4]
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine formula 
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    r = 6371 # Radius of earth in kilometers. Use 3956 for miles
    return c * r

#Copy the trip details provided by in this question

df = pd.read_clipboard()
df['dist'] = df.apply(haversine, axis=1)

print df

   Trip  Origin_X  Origin_Y  Destination_X  Destination_Y       dist
0     1 -33.55682 -70.78614      -33.44007      -70.65520  15.177680
1     2 -33.49097 -70.77741      -33.48908      -70.76263   1.644918
2     3 -33.37108 -70.67110      -33.73425      -70.76278  16.785898
#To group
dfg = df.groupby(df['dist'] < 1)

#Just to select all the trips that are less than 2 radius
df[df['dist']<2]
   Trip  Origin_X  Origin_Y  Destination_X  Destination_Y      dist
1     2 -33.49097 -70.77741      -33.48908      -70.76263  1.644918

【参考方案2】：

您可以遍历每个点，计算到所有其他点的距离，然后检查距离是否小于或等于 1 公里，并将其添加到字典中，其中键是原点，值是所有的数组关闭点...