如果你想要驾驶距离/路线(张贴在这里，因为这是谷歌上两点之间距离的第一个结果，但对大多数人来说，驾驶距离更有用)，你可以使用谷歌地图距离矩阵服务:

getDrivingDistanceBetweenTwoLatLong(origin, destination) {

 return new Observable(subscriber => {
  let service = new google.maps.DistanceMatrixService();
  service.getDistanceMatrix(
    {
      origins: [new google.maps.LatLng(origin.lat, origin.long)],
      destinations: [new google.maps.LatLng(destination.lat, destination.long)],
      travelMode: 'DRIVING'
    }, (response, status) => {
      if (status !== google.maps.DistanceMatrixStatus.OK) {
        console.log('Error:', status);
        subscriber.error({error: status, status: status});
      } else {
        console.log(response);
        try {
          let valueInMeters = response.rows[0].elements[0].distance.value;
          let valueInKms = valueInMeters / 1000;
          subscriber.next(valueInKms);
          subscriber.complete();
        }
       catch(error) {
        subscriber.error({error: error, status: status});
       }
      }
    });
});
}

仅限飞镖：

import 'dart:math' show cos, sqrt, asin;

double calculateDistance(LatLng l1, LatLng l2) {
  const p = 0.017453292519943295;
  final a = 0.5 -
      cos((l2.latitude - l1.latitude) * p) / 2 +
      cos(l1.latitude * p) *
          cos(l2.latitude * p) *
          (1 - cos((l2.longitude - l1.longitude) * p)) /
          2;
  return 12742 * asin(sqrt(a));
}

function getDistanceFromLatLonInKm(lat1,lon1,lat2,lon2,units) {
  var R = 6371; // Radius of the earth in km
  var dLat = deg2rad(lat2-lat1);  // deg2rad below
  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; 
  var miles = d / 1.609344; 

if ( units == 'km' ) {  
return d; 
 } else {
return miles;
}}

查克的解决方案，也适用于英里。

这是一个简单的javascript函数，从这个链接可能是有用的。不知何故相关，但我们使用谷歌地球javascript插件而不是地图

function getApproximateDistanceUnits(point1, point2) {

    var xs = 0;
    var ys = 0;

    xs = point2.getX() - point1.getX();
    xs = xs * xs;

    ys = point2.getY() - point1.getY();
    ys = ys * ys;

    return Math.sqrt(xs + ys);
}

单位不是距离，而是相对于坐标的比率。还有其他相关的计算，你可以在这里代替getApproximateDistanceUnits函数链接

然后我使用这个函数来查看经纬度是否在半径内

function isMapPlacemarkInRadius(point1, point2, radi) {
    if (point1 && point2) {
        return getApproximateDistanceUnits(point1, point2) <= radi;
    } else {
        return 0;
    }
}

点可以定义为

 $$.getPoint = function(lati, longi) {
        var location = {
            x: 0,
            y: 0,
            getX: function() { return location.x; },
            getY: function() { return location.y; }
        };
        location.x = lati;
        location.y = longi;

        return location;
    };

然后你可以做你的事情，看看一个点是否在一个半径范围内，比如:

 //put it on the map if within the range of a specified radi assuming 100,000,000 units
        var iconpoint = Map.getPoint(pp.latitude, pp.longitude);
        var centerpoint = Map.getPoint(Settings.CenterLatitude, Settings.CenterLongitude);

        //approx ~200 units to show only half of the globe from the default center radius
        if (isMapPlacemarkInRadius(centerpoint, iconpoint, 120)) {
            addPlacemark(pp.latitude, pp.longitude, pp.name);
        }
        else {
            otherSidePlacemarks.push({
                latitude: pp.latitude,
                longitude: pp.longitude,
                name: pp.name
            });

        }

下面是一个Scala实现:

  def calculateHaversineDistance(lat1: Double, lon1: Double, lat2: Double, lon2: Double): Double = {
    val long2 = lon2 * math.Pi / 180
    val lat2 = lat2 * math.Pi / 180
    val long1 = lon1 * math.Pi / 180
    val lat1 = lat1 * math.Pi / 180

    val dlon = long2 - long1
    val dlat = lat2 - lat1
    val a = math.pow(math.sin(dlat / 2), 2) + math.cos(lat1) * math.cos(lat2) * math.pow(math.sin(dlon / 2), 2)
    val c = 2 * math.atan2(Math.sqrt(a), math.sqrt(1 - a))
    val haversineDistance = 3961 * c // 3961 = radius of earth in miles
    haversineDistance
  }

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。