如何识别彼此之间特定距离内的坐标

Question

我正在尝试确定哪些坐标落在彼此的特定距离内。目前，我的代码将所有点组合在一起，而它应该是两个单独的组。

from sklearn.neighbors import DistanceMetric
from math import radians
import pandas as pd
import numpy as np
from collections import Counter

data = {'Lat': [38.42447, 38.424474, 38.424493, 38.424394, 38.424457, 38.424434],
    'Long': [-77.402199, -77.402228, -77.402186, -77.398625, -77.398602, -77.398459],
    'Name': ['Truck', 'Truck1','Truck2','Truck3','Truck4','Truck5',]}
df = pd.DataFrame(data)

df['Lat'] = np.radians(df['Lat'])
df['Long'] = np.radians(df['Long'])

dist = DistanceMetric.get_metric('haversine')

df[['Lat','Long']].to_numpy()
dist.pairwise(df[['Lat','Long']].to_numpy())*6371000

final_df = pd.DataFrame(dist.pairwise(df[['Lat','Long']].to_numpy())*6371000,  columns=df.Name.unique(), index=df.Name.unique())

potential_grouping = []
for row, col in final_df.items():
for item in col:
    if int(item) < 15:
        potential_grouping.append(row)

outside_features = [k for k, v in Counter(potential_grouping).items() if v == 1]
acceptable_features = [k for k, v in Counter(potential_grouping).items() if v > 1]
print(acceptable_features)
current output: ['Truck', 'Truck1', 'Truck2', 'Truck3', 'Truck4', 'Truck5']
desired output: [['Truck', 'Truck1', 'Truck2'],['Truck3', 'Truck4', 'Truck5']]

这是正在发生的事情的蹩脚图片...... 6 个小圆圈目前正在分组（红色大圆圈），但应该分开（2 个绿色圆圈）。发生这种情况是因为每个坐标（棕色小圆圈）彼此相距不到 15 米。我怎样才能确保得到我想要的输出？

Answer 1

这是使用 DBSCAN 的一种方法：

from sklearn.cluster import DBSCAN

# here Lat and Long are already in radians
X = df[['Lat', 'Long']].to_numpy()

# here 15 is your max distance in meters divided by earth radius in meters
clustering = DBSCAN(eps=15/6373000, min_samples=1, metric='haversine').fit(X)

# see groups
print(clustering.labels_)
# [0 0 0 1 1 1]

# get the result as you want
acceptable_features = df['Name'].groupby(clustering.labels_).agg(list).tolist()
print(acceptable_features)
# [['Truck', 'Truck1', 'Truck2'], ['Truck3', 'Truck4', 'Truck5']]

如何识别彼此之间特定距离内的坐标

How to identify what coordinates that are within a specific distance of eachother

python

pandas

algorithm

cluster-analysis

distance