下面是Haversine公式的typescript实现

static getDistanceFromLatLonInKm(lat1: number, lon1: number, lat2: number, lon2: number): number {
    var deg2Rad = deg => {
        return deg * Math.PI / 180;
    }

    var r = 6371; // Radius of the earth in km
    var dLat = deg2Rad(lat2 - lat1);   
    var dLon = deg2Rad(lon2 - lon1);
    var a =
        Math.sin(dLat / 2) * Math.sin(dLat / 2) +
        Math.cos(deg2Rad(lat1)) * Math.cos(deg2Rad(lat2)) *
        Math.sin(dLon / 2) * Math.sin(dLon / 2);
    var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
    var d = r * c; // Distance in km
    return d;
}

在Mysql中使用以下函数传递参数，使用POINT(LONG,LAT)

CREATE FUNCTION `distance`(a POINT, b POINT)
 RETURNS double
    DETERMINISTIC
BEGIN

RETURN

GLength( LineString(( PointFromWKB(a)), (PointFromWKB(b)))) * 100000; -- To Make the distance in meters

END;

你也可以使用像geolib这样的模块:

安装方法:

$ npm install geolib

使用方法:

import { getDistance } from 'geolib'

const distance = getDistance(
    { latitude: 51.5103, longitude: 7.49347 },
    { latitude: "51° 31' N", longitude: "7° 28' E" }
)

console.log(distance)

文档: https://www.npmjs.com/package/geolib

下面是Haversine公式的java实现。

public final static double AVERAGE_RADIUS_OF_EARTH_KM = 6371;
public int calculateDistanceInKilometer(double userLat, double userLng,
  double venueLat, double venueLng) {

    double latDistance = Math.toRadians(userLat - venueLat);
    double lngDistance = Math.toRadians(userLng - venueLng);

    double a = Math.sin(latDistance / 2) * Math.sin(latDistance / 2)
      + Math.cos(Math.toRadians(userLat)) * Math.cos(Math.toRadians(venueLat))
      * Math.sin(lngDistance / 2) * Math.sin(lngDistance / 2);

    double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));

    return (int) (Math.round(AVERAGE_RADIUS_OF_EARTH_KM * c));
}

请注意，这里我们将答案四舍五入到最近的km。

这里有一个用PHP http://www.geodatasource.com/developers/php计算距离的好例子:

 function distance($lat1, $lon1, $lat2, $lon2, $unit) {

     $theta = $lon1 - $lon2;
     $dist = sin(deg2rad($lat1)) * sin(deg2rad($lat2)) +  cos(deg2rad($lat1)) * cos(deg2rad($lat2)) * cos(deg2rad($theta));
     $dist = acos($dist);
     $dist = rad2deg($dist);
     $miles = $dist * 60 * 1.1515;
     $unit = strtoupper($unit);

     if ($unit == "K") {
         return ($miles * 1.609344);
     } else if ($unit == "N") {
          return ($miles * 0.8684);
     } else {
          return $miles;
     }
 }

精确计算中长点之间距离所需的函数是复杂的，陷阱也很多。我不推荐哈弗辛或其他球形的解决方案，因为有很大的不准确性(地球不是一个完美的球体)。vincenty公式更好，但在某些情况下会抛出错误，即使编码正确。

与其自己编写函数，我建议使用geopy，它已经实现了非常精确的地理库来进行距离计算(论文来自作者)。

#pip install geopy
from geopy.distance import geodesic
NY = [40.71278,-74.00594]
Beijing = [39.90421,116.40739]
print("WGS84: ",geodesic(NY, Beijing).km) #WGS84 is Standard
print("Intl24: ",geodesic(NY, Beijing, ellipsoid='Intl 1924').km) #geopy includes different ellipsoids
print("Custom ellipsoid: ",geodesic(NY, Beijing, ellipsoid=(6377., 6356., 1 / 297.)).km) #custom ellipsoid

#supported ellipsoids:
#model             major (km)   minor (km)     flattening
#'WGS-84':        (6378.137,    6356.7523142,  1 / 298.257223563)
#'GRS-80':        (6378.137,    6356.7523141,  1 / 298.257222101)
#'Airy (1830)':   (6377.563396, 6356.256909,   1 / 299.3249646)
#'Intl 1924':     (6378.388,    6356.911946,   1 / 297.0)
#'Clarke (1880)': (6378.249145, 6356.51486955, 1 / 293.465)
#'GRS-67':        (6378.1600,   6356.774719,   1 / 298.25)

这个库的唯一缺点是它不支持向量化计算。 对于向量化计算，您可以使用新的gevectorslib。

#pip install geovectorslib
from geovectorslib import inverse
print(inverse(lats1,lons1,lats2,lons2)['s12'])

lat和lon是numpy数组。Geovectorslib是非常准确和非常快!我还没有找到改变椭球的方法。标准采用WGS84椭球，是大多数用途的最佳选择。