Getting distance between two points based on latitude longitude

Question

I tried implementing this formula  http   andrew hedges name experiments haversine  The aplet does good for the two points I am testing     Yet my code is not working   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   The distance it returns is 5447 05546147  Why

User · Answer

I arrived at a much simpler and robust solution which is using geodesic from geopy package since you'll be highly likely using it in your project anyways so no extra package installation needed.

Here is my solution:

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

geopy

User · Answer

For people  like me  coming here via search engine and just looking for a solution which works out of the box  I recommend installing mpu  Install it via pip install mpu --user and use it like this to get the haversine distance   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    An alternative package is gpxpy   If you don t want dependencies  you can use   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     --------      gt  gt  gt  origin    48 1372  11 5756     Munich      gt  gt  gt  destination    52 5186  13 4083     Berlin      gt  gt  gt  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     The other alternative package is  haversine  1   from haversine import haversine  Unit  lyon    45 7597  4 8422     lat  lon  paris    48 8567  2 3508   haversine lyon  paris   gt  gt  392 2172595594006    in kilometers  haversine lyon  paris  unit Unit MILES   gt  gt  243 71201856934454    in miles    you can also use the string abbreviation for units  haversine lyon  paris  unit  mi    gt  gt  243 71201856934454    in miles  haversine lyon  paris  unit Unit NAUTICAL MILES   gt  gt  211 78037755311516    in nautical miles   They claim to have performance optimization for distances between all points in two vectors  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    gt  gt  array   392 21725956  6163 43638211

User · Answer

You can use Uber s H3 point dist   function to compute the spherical distance between two  lat  lng  points  We can set return unit   km    m   or  rads    The default unit is Km  Example   import H3  coords 1    52 2296756  21 0122287  coords 2    52 406374  16 9251681  distance   h3 point dist coords 1 coords 2   278 4584889328128  Hope this will usefull

User · Answer

There are multiple ways to calculate the distance based on the coordinates i e latitude and longitude Install and import from geopy import distance from math import sin  cos  sqrt  atan2  radians from sklearn neighbors import DistanceMetric import osrm import numpy as np  Define coordinates lat1  lon1  lat2  lon2  R   20 9467 72 9520  21 1702  72 8311  6373 0 coordinates from    lat1  lon1  coordinates to    lat2  lon2   Using haversine 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   Using haversine with sklearn 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    Using OSRM 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   Using geopy 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   Output 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

User · Answer

Update  04 2018  Note that Vincenty distance is deprecated since GeoPy version 1 13 - you should use geopy distance distance   instead     The answers above are based on the Haversine formula  which assumes the earth is a sphere  which results in errors of up to about 0 5   according to help geopy distance    Vincenty distance uses more accurate ellipsoidal models such as WGS-84  and is implemented in geopy  For example   import geopy distance  coords 1    52 2296756  21 0122287  coords 2    52 406374  16 9251681   print geopy distance vincenty coords 1  coords 2  km   will print the distance of 279 352901604 kilometers using the default ellipsoid WGS-84   You can also choose  miles or one of several other distance units

User · Answer

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   sin    f 2    cos f1    cos f2    sin      2      c   2    atan2  va  v 1-a        d   R    c     where   f is latitude    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

User · Answer

Edit  Just as a note  if you just need a quick and easy way of finding the distance between two points  I strongly recommend using the approach described in Kurt s answer below instead of re-implementing Haversine -- see his post for rationale   This answer focuses just on answering the specific bug OP ran into     It s because in Python  all the trig functions use radians  not degrees   You can either convert the numbers manually to radians  or use the radians function from the math module   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     The distance is now returning the correct value of 278 545589351 km

[python] Getting distance between two points based on latitude/longitude

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