[英]How can I determine what coordinates if any fall within a specific distance between each other?
[英]How to identify what coordinates that are within a specific distance of eachother
我試圖確定哪些坐標落在彼此的特定距離內。 目前,當它應該是兩個單獨的組時,我的代碼將所有點組合在一起。
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']]
這是正在發生的事情的糟糕圖片...... 6 個小圓圈目前正在分組(大紅色圓圈),但應該分開(2 個綠色圓圈)。 之所以發生這種情況,是因為每個坐標(棕色小圓圈)都在 15 米之內。 如何確保獲得所需的輸出?
這是使用DBSCAN
一種方法:
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']]
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