我尝试实现这个公式：http ://andrew.hedges.name/experiments/haversine/ aplet 对我正在测试的两点有好处：
然而我的代码不起作用。
from math import sin, cos, sqrt, atan2
R = 6373.0
lat1 = 52.2296756
lon1 = 21.0122287
lat2 = 52.406374
lon2 = 16.9251681
dlon = lon2 - lon1
dlat = lat2 - lat1
a = (sin(dlat/2))**2 + cos(lat1) * cos(lat2) * (sin(dlon/2))**2
c = 2 * atan2(sqrt(a), sqrt(1-a))
distance = R * c
print "Result", distance
print "Should be", 278.546
它返回的距离是5447.05546147。为什么？
更新：04/2018： Vincenty 距离自 GeoPy 1.13 版以来已被弃用-您应该geopy.distance.distance()改用！
上面的答案基于Haversine 公式，假设地球是一个球体，导致误差高达约0.5%（根据help(geopy.distance)）。Vincenty distance使用更精确的椭球模型，例如WGS-84，并在geopy中实现。例如，
import geopy.distance
coords_1 = (52.2296756, 21.0122287)
coords_2 = (52.406374, 16.9251681)
print geopy.distance.vincenty(coords_1, coords_2).km
279.352901604将使用默认的椭球 WGS-84打印千米距离。（您也可以选择.miles其他几个距离单位之一）。
编辑：请注意，如果您只需要一种快速简便的方法来查找两点之间的距离，我强烈建议您使用下面库尔特的回答中描述的方法，而不是重新实现 Haversine - 请参阅他的帖子了解基本原理。
该答案仅侧重于回答 OP 遇到的特定错误。
这是因为在 Python 中，所有三角函数都使用弧度，而不是度数。
您可以手动将数字转换为弧度，或使用radians数学模块中的函数：
from math import sin, cos, sqrt, atan2, radians
# approximate radius of earth in km
R = 6373.0
lat1 = radians(52.2296756)
lon1 = radians(21.0122287)
lat2 = radians(52.406374)
lon2 = radians(16.9251681)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance = R * c
print("Result:", distance)
print("Should be:", 278.546, "km")
距离现在返回正确的278.545589351km 值。
对于通过搜索引擎来到这里并只是寻找开箱即用的解决方案的人（像我一样），我建议安装mpu. 通过安装它pip install mpu --user并像这样使用它来获得半正弦距离：
import mpu
# Point one
lat1 = 52.2296756
lon1 = 21.0122287
# Point two
lat2 = 52.406374
lon2 = 16.9251681
# What you were looking for
dist = mpu.haversine_distance((lat1, lon1), (lat2, lon2))
print(dist) # gives 278.45817507541943.
另一种包是gpxpy.
如果您不想要依赖项，可以使用：
import math
def distance(origin, destination):
"""
Calculate the Haversine distance.
Parameters
----------
origin : tuple of float
(lat, long)
destination : tuple of float
(lat, long)
Returns
-------
distance_in_km : float
Examples
--------
>>> origin = (48.1372, 11.5756) # Munich
>>> destination = (52.5186, 13.4083) # Berlin
>>> round(distance(origin, destination), 1)
504.2
"""
lat1, lon1 = origin
lat2, lon2 = destination
radius = 6371 # km
dlat = math.radians(lat2 - lat1)
dlon = math.radians(lon2 - lon1)
a = (math.sin(dlat / 2) * math.sin(dlat / 2) +
math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) *
math.sin(dlon / 2) * math.sin(dlon / 2))
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
d = radius * c
return d
if __name__ == '__main__':
import doctest
doctest.testmod()
另一个替代包是haversine
from haversine import haversine, Unit
lyon = (45.7597, 4.8422) # (lat, lon)
paris = (48.8567, 2.3508)
haversine(lyon, paris)
>> 392.2172595594006 # in kilometers
haversine(lyon, paris, unit=Unit.MILES)
>> 243.71201856934454 # in miles
# you can also use the string abbreviation for units:
haversine(lyon, paris, unit='mi')
>> 243.71201856934454 # in miles
haversine(lyon, paris, unit=Unit.NAUTICAL_MILES)
>> 211.78037755311516 # in nautical miles
他们声称对两个向量中所有点之间的距离进行了性能优化
from haversine import haversine_vector, Unit
lyon = (45.7597, 4.8422) # (lat, lon)
paris = (48.8567, 2.3508)
new_york = (40.7033962, -74.2351462)
haversine_vector([lyon, lyon], [paris, new_york], Unit.KILOMETERS)
>> array([ 392.21725956, 6163.43638211])
我得到了一个更简单、更强大的解决方案，它geodesic从geopy包中使用，因为无论如何你很可能在你的项目中使用它，所以不需要额外的包安装。
这是我的解决方案：
from geopy.distance import geodesic
origin = (30.172705, 31.526725) # (latitude, longitude) don't confuse
dist = (30.288281, 31.732326)
print(geodesic(origin, dist).meters) # 23576.805481751613
print(geodesic(origin, dist).kilometers) # 23.576805481751613
print(geodesic(origin, dist).miles) # 14.64994773134371
有多种方法可以根据坐标计算距离，即纬度和经度
from geopy import distance
from math import sin, cos, sqrt, atan2, radians
from sklearn.neighbors import DistanceMetric
import osrm
import numpy as np
lat1, lon1, lat2, lon2, R = 20.9467,72.9520, 21.1702, 72.8311, 6373.0
coordinates_from = [lat1, lon1]
coordinates_to = [lat2, lon2]
dlon = radians(lon2) - radians(lon1)
dlat = radians(lat2) - radians(lat1)
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance_haversine_formula = R * c
print('distance using haversine formula: ', distance_haversine_formula)
dist = DistanceMetric.get_metric('haversine')
X = [[radians(lat1), radians(lon1)], [radians(lat2), radians(lon2)]]
distance_sklearn = R * dist.pairwise(X)
print('distance using sklearn: ', np.array(distance_sklearn).item(1))
osrm_client = osrm.Client(host='http://router.project-osrm.org')
coordinates_osrm = [[lon1, lat1], [lon2, lat2]] # note that order is lon, lat
osrm_response = osrm_client.route(coordinates=coordinates_osrm, overview=osrm.overview.full)
dist_osrm = osrm_response.get('routes')[0].get('distance')/1000 # in km
print('distance using OSRM: ', dist_osrm)
distance_geopy = distance.distance(coordinates_from, coordinates_to).km
print('distance using geopy: ', distance_geopy)
distance_geopy_great_circle = distance.great_circle(coordinates_from, coordinates_to).km
print('distance using geopy great circle: ', distance_geopy_great_circle)
distance using haversine formula: 26.07547017310917
distance using sklearn: 27.847882224769783
distance using OSRM: 33.091699999999996
distance using geopy: 27.7528030550408
distance using geopy great circle: 27.839182219511834
import numpy as np
def Haversine(lat1,lon1,lat2,lon2, **kwarg):
"""
This uses the ‘haversine’ formula to calculate the great-circle distance between two points – that is,
the shortest distance over the earth’s surface – giving an ‘as-the-crow-flies’ distance between the points
(ignoring any hills they fly over, of course!).
Haversine
formula: a = sin²(Δφ/2) + cos φ1 ⋅ cos φ2 ⋅ sin²(Δλ/2)
c = 2 ⋅ atan2( √a, √(1−a) )
d = R ⋅ c
where φ is latitude, λ is longitude, R is earth’s radius (mean radius = 6,371km);
note that angles need to be in radians to pass to trig functions!
"""
R = 6371.0088
lat1,lon1,lat2,lon2 = map(np.radians, [lat1,lon1,lat2,lon2])
dlat = lat2 - lat1
dlon = lon2 - lon1
a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2) **2
c = 2 * np.arctan2(a**0.5, (1-a)**0.5)
d = R * c
return round(d,4)
您可以使用Uber的H3point_dist()函数来计算两个 (lat, lng) 点之间的球面距离。我们可以设置返回单位（'km'、'm' 或 'rads'）。默认单位是公里。
例子 ：
import h3
coords_1 = (52.2296756, 21.0122287)
coords_2 = (52.406374, 16.9251681)
distance = h3.point_dist(coords_1, coords_2, unit='m') # to get distance in meters
希望这会有用！
最简单的方法是使用 hasrsine 包。
import haversine as hs
coord_1 = (lat, lon)
coord_2 = (lat, lon)
x = hs.haversine(coord_1,coord_2)
print(f'The distance is {x} km')