How do I calculate the distance between two points specified by latitude and longitude?
For clarification, I'd like the distance in kilometers; the points use the WGS84 system and I'd like to understand the relative accuracies of the approaches available.
This link might be helpful to you, as it details the use of the Haversine formula to calculate the distance.
Excerpt:
This script [in Javascript] calculates great-circle distances between the two points –
that is, the shortest distance over the earth’s surface – using the
‘Haversine’ formula.
function getDistanceFromLatLonInKm(lat1,lon1,lat2,lon2) {
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; // Distance in km
return d;
}
function deg2rad(deg) {
return deg * (Math.PI/180)
}
I needed to calculate a lot of distances between the points for my project, so I went ahead and tried to optimize the code, I have found here. On average in different browsers my new implementation runs 2 times faster than the most upvoted answer.
function distance(lat1, lon1, lat2, lon2) {
var p = 0.017453292519943295; // Math.PI / 180
var c = Math.cos;
var a = 0.5 - c((lat2 - lat1) * p)/2 +
c(lat1 * p) * c(lat2 * p) *
(1 - c((lon2 - lon1) * p))/2;
return 12742 * Math.asin(Math.sqrt(a)); // 2 * R; R = 6371 km
}
You can play with my jsPerf and see the results here.
Recently I needed to do the same in python, so here is a python implementation:
from math import cos, asin, sqrt, pi
def distance(lat1, lon1, lat2, lon2):
p = pi/180
a = 0.5 - cos((lat2-lat1)*p)/2 + cos(lat1*p) * cos(lat2*p) * (1-cos((lon2-lon1)*p))/2
return 12742 * asin(sqrt(a)) #2*R*asin...
And for the sake of completeness: Haversine on Wikipedia.
Here is a C# Implementation:
static class DistanceAlgorithm
{
const double PIx = 3.141592653589793;
const double RADIUS = 6378.16;
/// <summary>
/// Convert degrees to Radians
/// </summary>
/// <param name="x">Degrees</param>
/// <returns>The equivalent in radians</returns>
public static double Radians(double x)
{
return x * PIx / 180;
}
/// <summary>
/// Calculate the distance between two places.
/// </summary>
/// <param name="lon1"></param>
/// <param name="lat1"></param>
/// <param name="lon2"></param>
/// <param name="lat2"></param>
/// <returns></returns>
public static double DistanceBetweenPlaces(
double lon1,
double lat1,
double lon2,
double lat2)
{
double dlon = Radians(lon2 - lon1);
double dlat = Radians(lat2 - lat1);
double a = (Math.Sin(dlat / 2) * Math.Sin(dlat / 2)) + Math.Cos(Radians(lat1)) * Math.Cos(Radians(lat2)) * (Math.Sin(dlon / 2) * Math.Sin(dlon / 2));
double angle = 2 * Math.Atan2(Math.Sqrt(a), Math.Sqrt(1 - a));
return angle * RADIUS;
}
}
Here is a java implementation of the Haversine formula.
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));
}
Note that here we are rounding the answer to the nearest km.
Thanks very much for all this. I used the following code in my Objective-C iPhone app:
const double PIx = 3.141592653589793;
const double RADIO = 6371; // Mean radius of Earth in Km
double convertToRadians(double val) {
return val * PIx / 180;
}
-(double)kilometresBetweenPlace1:(CLLocationCoordinate2D) place1 andPlace2:(CLLocationCoordinate2D) place2 {
double dlon = convertToRadians(place2.longitude - place1.longitude);
double dlat = convertToRadians(place2.latitude - place1.latitude);
double a = ( pow(sin(dlat / 2), 2) + cos(convertToRadians(place1.latitude))) * cos(convertToRadians(place2.latitude)) * pow(sin(dlon / 2), 2);
double angle = 2 * asin(sqrt(a));
return angle * RADIO;
}
Latitude and Longitude are in decimal. I didn't use min() for the asin() call as the distances that I'm using are so small that they don't require it.
It gave incorrect answers until I passed in the values in Radians - now it's pretty much the same as the values obtained from Apple's Map app :-)
Extra update:
If you are using iOS4 or later then Apple provide some methods to do this so the same functionality would be achieved with:
-(double)kilometresBetweenPlace1:(CLLocationCoordinate2D) place1 andPlace2:(CLLocationCoordinate2D) place2 {
MKMapPoint start, finish;
start = MKMapPointForCoordinate(place1);
finish = MKMapPointForCoordinate(place2);
return MKMetersBetweenMapPoints(start, finish) / 1000;
}
This is a simple PHP function that will give a very reasonable approximation (under +/-1% error margin).
<?php
function distance($lat1, $lon1, $lat2, $lon2) {
$pi80 = M_PI / 180;
$lat1 *= $pi80;
$lon1 *= $pi80;
$lat2 *= $pi80;
$lon2 *= $pi80;
$r = 6372.797; // mean radius of Earth in km
$dlat = $lat2 - $lat1;
$dlon = $lon2 - $lon1;
$a = sin($dlat / 2) * sin($dlat / 2) + cos($lat1) * cos($lat2) * sin($dlon / 2) * sin($dlon / 2);
$c = 2 * atan2(sqrt($a), sqrt(1 - $a));
$km = $r * $c;
//echo '<br/>'.$km;
return $km;
}
?>
As said before; the earth is NOT a sphere. It is like an old, old baseball that Mark McGwire decided to practice with - it is full of dents and bumps. The simpler calculations (like this) treat it like a sphere.
Different methods may be more or less precise according to where you are on this irregular ovoid AND how far apart your points are (the closer they are the smaller the absolute error margin). The more precise your expectation, the more complex the math.
For more info: wikipedia geographic distance
I post here my working example.
List all points in table having distance between a designated point (we use a random point - lat:45.20327, long:23.7806) less than 50 KM, with latitude & longitude, in MySQL (the table fields are coord_lat and coord_long):
List all having DISTANCE<50, in Kilometres (considered Earth radius 6371 KM):
SELECT denumire, (6371 * acos( cos( radians(45.20327) ) * cos( radians( coord_lat ) ) * cos( radians( 23.7806 ) - radians(coord_long) ) + sin( radians(45.20327) ) * sin( radians(coord_lat) ) )) AS distanta
FROM obiective
WHERE coord_lat<>''
AND coord_long<>''
HAVING distanta<50
ORDER BY distanta desc
The above example was tested in MySQL 5.0.95 and 5.5.16 (Linux).
In the other answers an implementation in r is missing.
Calculating the distance between two point is quite straightforward with the distm function from the geosphere package:
distm(p1, p2, fun = distHaversine)
where:
p1 = longitude/latitude for point(s)
p2 = longitude/latitude for point(s)
# type of distance calculation
fun = distCosine / distHaversine / distVincentySphere / distVincentyEllipsoid
As the earth is not perfectly spherical, the Vincenty formula for ellipsoids is probably the best way to calculate distances. Thus in the geosphere package you use then:
distm(p1, p2, fun = distVincentyEllipsoid)
Off course you don't necessarily have to use geosphere package, you can also calculate the distance in base R with a function:
hav.dist <- function(long1, lat1, long2, lat2) {
R <- 6371
diff.long <- (long2 - long1)
diff.lat <- (lat2 - lat1)
a <- sin(diff.lat/2)^2 + cos(lat1) * cos(lat2) * sin(diff.long/2)^2
b <- 2 * asin(pmin(1, sqrt(a)))
d = R * b
return(d)
}
The haversine is definitely a good formula for probably most cases, other answers already include it so I am not going to take the space. But it is important to note that no matter what formula is used (yes not just one). Because of the huge range of accuracy possible as well as the computation time required. The choice of formula requires a bit more thought than a simple no brainer answer.
This posting from a person at nasa, is the best one I found at discussing the options
http://www.cs.nyu.edu/visual/home/proj/tiger/gisfaq.html
For example, if you are just sorting rows by distance in a 100 miles radius. The flat earth formula will be much faster than the haversine.
HalfPi = 1.5707963;
R = 3956; /* the radius gives you the measurement unit*/
a = HalfPi - latoriginrad;
b = HalfPi - latdestrad;
u = a * a + b * b;
v = - 2 * a * b * cos(longdestrad - longoriginrad);
c = sqrt(abs(u + v));
return R * c;
Notice there is just one cosine and one square root. Vs 9 of them on the Haversine formula.
There could be a simpler solution, and more correct: The perimeter of earth is 40,000Km at the equator, about 37,000 on Greenwich (or any longitude) cycle. Thus:
pythagoras = function (lat1, lon1, lat2, lon2) {
function sqr(x) {return x * x;}
function cosDeg(x) {return Math.cos(x * Math.PI / 180.0);}
var earthCyclePerimeter = 40000000.0 * cosDeg((lat1 + lat2) / 2.0);
var dx = (lon1 - lon2) * earthCyclePerimeter / 360.0;
var dy = 37000000.0 * (lat1 - lat2) / 360.0;
return Math.sqrt(sqr(dx) + sqr(dy));
};
I agree that it should be fine-tuned as, I myself said that it's an ellipsoid, so the radius to be multiplied by the cosine varies. But it's a bit more accurate. Compared with Google Maps and it did reduce the error significantly.
pip install haversine
Python implementation
Origin is the center of the contiguous United States.
from haversine import haversine, Unit
origin = (39.50, 98.35)
paris = (48.8567, 2.3508)
haversine(origin, paris, unit=Unit.MILES)
To get the answer in kilometers simply set unit=Unit.KILOMETERS (that's the default).
There is some errors in the code provided, I've fixed it below.
All the above answers assumes the earth is a sphere. However, a more accurate approximation would be that of an oblate spheroid.
a= 6378.137#equitorial radius in km
b= 6356.752#polar radius in km
def Distance(lat1, lons1, lat2, lons2):
lat1=math.radians(lat1)
lons1=math.radians(lons1)
R1=(((((a**2)*math.cos(lat1))**2)+(((b**2)*math.sin(lat1))**2))/((a*math.cos(lat1))**2+(b*math.sin(lat1))**2))**0.5 #radius of earth at lat1
x1=R1*math.cos(lat1)*math.cos(lons1)
y1=R1*math.cos(lat1)*math.sin(lons1)
z1=R1*math.sin(lat1)
lat2=math.radians(lat2)
lons2=math.radians(lons2)
R2=(((((a**2)*math.cos(lat2))**2)+(((b**2)*math.sin(lat2))**2))/((a*math.cos(lat2))**2+(b*math.sin(lat2))**2))**0.5 #radius of earth at lat2
x2=R2*math.cos(lat2)*math.cos(lons2)
y2=R2*math.cos(lat2)*math.sin(lons2)
z2=R2*math.sin(lat2)
return ((x1-x2)**2+(y1-y2)**2+(z1-z2)**2)**0.5
I don't like adding yet another answer, but the Google maps API v.3 has spherical geometry (and more). After converting your WGS84 to decimal degrees you can do this:
<script src="http://maps.google.com/maps/api/js?sensor=false&libraries=geometry" type="text/javascript"></script>
distance = google.maps.geometry.spherical.computeDistanceBetween(
new google.maps.LatLng(fromLat, fromLng),
new google.maps.LatLng(toLat, toLng));
No word about how accurate Google's calculations are or even what model is used (though it does say "spherical" rather than "geoid". By the way, the "straight line" distance will obviously be different from the distance if one travels on the surface of the earth which is what everyone seems to be presuming.
You can use the build in CLLocationDistance to calculate this:
CLLocation *location1 = [[CLLocation alloc] initWithLatitude:latitude1 longitude:longitude1];
CLLocation *location2 = [[CLLocation alloc] initWithLatitude:latitude2 longitude:longitude2];
[self distanceInMetersFromLocation:location1 toLocation:location2]
- (int)distanceInMetersFromLocation:(CLLocation*)location1 toLocation:(CLLocation*)location2 {
CLLocationDistance distanceInMeters = [location1 distanceFromLocation:location2];
return distanceInMeters;
}
In your case if you want kilometers just divide by 1000.
As pointed out, an accurate calculation should take into account that the earth is not a perfect sphere. Here are some comparisons of the various algorithms offered here:
geoDistance(50,5,58,3)
Haversine: 899 km
Maymenn: 833 km
Keerthana: 897 km
google.maps.geometry.spherical.computeDistanceBetween(): 900 km
geoDistance(50,5,-58,-3)
Haversine: 12030 km
Maymenn: 11135 km
Keerthana: 10310 km
google.maps.geometry.spherical.computeDistanceBetween(): 12044 km
geoDistance(.05,.005,.058,.003)
Haversine: 0.9169 km
Maymenn: 0.851723 km
Keerthana: 0.917964 km
google.maps.geometry.spherical.computeDistanceBetween(): 0.917964 km
geoDistance(.05,80,.058,80.3)
Haversine: 33.37 km
Maymenn: 33.34 km
Keerthana: 33.40767 km
google.maps.geometry.spherical.computeDistanceBetween(): 33.40770 km
Over small distances, Keerthana's algorithm does seem to coincide with that of Google Maps. Google Maps does not seem to follow any simple algorithm, suggesting that it may be the most accurate method here.
Anyway, here is a Javascript implementation of Keerthana's algorithm:
function geoDistance(lat1, lng1, lat2, lng2){
const a = 6378.137; // equitorial radius in km
const b = 6356.752; // polar radius in km
var sq = x => (x*x);
var sqr = x => Math.sqrt(x);
var cos = x => Math.cos(x);
var sin = x => Math.sin(x);
var radius = lat => sqr((sq(a*a*cos(lat))+sq(b*b*sin(lat)))/(sq(a*cos(lat))+sq(b*sin(lat))));
lat1 = lat1 * Math.PI / 180;
lng1 = lng1 * Math.PI / 180;
lat2 = lat2 * Math.PI / 180;
lng2 = lng2 * Math.PI / 180;
var R1 = radius(lat1);
var x1 = R1*cos(lat1)*cos(lng1);
var y1 = R1*cos(lat1)*sin(lng1);
var z1 = R1*sin(lat1);
var R2 = radius(lat2);
var x2 = R2*cos(lat2)*cos(lng2);
var y2 = R2*cos(lat2)*sin(lng2);
var z2 = R2*sin(lat2);
return sqr(sq(x1-x2)+sq(y1-y2)+sq(z1-z2));
}
Here is a typescript implementation of the Haversine formula
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;
}
Here is the SQL Implementation to calculate the distance in km,
SELECT UserId, ( 3959 * acos( cos( radians( your latitude here ) ) * cos( radians(latitude) ) *
cos( radians(longitude) - radians( your longitude here ) ) + sin( radians( your latitude here ) ) *
sin( radians(latitude) ) ) ) AS distance FROM user HAVING
distance < 5 ORDER BY distance LIMIT 0 , 5;
For further details in the implementation by programming langugage, you can just go through the php script given here
This script [in PHP] calculates distances between the two points.
public static function getDistanceOfTwoPoints($source, $dest, $unit='K') {
$lat1 = $source[0];
$lon1 = $source[1];
$lat2 = $dest[0];
$lon2 = $dest[1];
$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 ($unit == "M")
{
return ($miles * 1.609344 * 1000);
}
else if ($unit == "N") {
return ($miles * 0.8684);
}
else {
return $miles;
}
}
here is an example in postgres sql (in km, for miles version, replace 1.609344 by 0.8684 version)
CREATE OR REPLACE FUNCTION public.geodistance(alat float, alng float, blat
float, blng float)
RETURNS float AS
$BODY$
DECLARE
v_distance float;
BEGIN
v_distance = asin( sqrt(
sin(radians(blat-alat)/2)^2
+ (
(sin(radians(blng-alng)/2)^2) *
cos(radians(alat)) *
cos(radians(blat))
)
)
) * cast('7926.3352' as float) * cast('1.609344' as float) ;
RETURN v_distance;
END
$BODY$
language plpgsql VOLATILE SECURITY DEFINER;
alter function geodistance(alat float, alng float, blat float, blng float)
owner to postgres;
Java implementation in according Haversine formula
double calculateDistance(double latPoint1, double lngPoint1,
double latPoint2, double lngPoint2) {
if(latPoint1 == latPoint2 && lngPoint1 == lngPoint2) {
return 0d;
}
final double EARTH_RADIUS = 6371.0; //km value;
//converting to radians
latPoint1 = Math.toRadians(latPoint1);
lngPoint1 = Math.toRadians(lngPoint1);
latPoint2 = Math.toRadians(latPoint2);
lngPoint2 = Math.toRadians(lngPoint2);
double distance = Math.pow(Math.sin((latPoint2 - latPoint1) / 2.0), 2)
+ Math.cos(latPoint1) * Math.cos(latPoint2)
* Math.pow(Math.sin((lngPoint2 - lngPoint1) / 2.0), 2);
distance = 2.0 * EARTH_RADIUS * Math.asin(Math.sqrt(distance));
return distance; //km value
}
I made a custom function in R to calculate haversine distance(km) between two spatial points using functions available in R base package.
custom_hav_dist <- function(lat1, lon1, lat2, lon2) {
R <- 6371
Radian_factor <- 0.0174533
lat_1 <- (90-lat1)*Radian_factor
lat_2 <- (90-lat2)*Radian_factor
diff_long <-(lon1-lon2)*Radian_factor
distance_in_km <- 6371*acos((cos(lat_1)*cos(lat_2))+
(sin(lat_1)*sin(lat_2)*cos(diff_long)))
rm(lat1, lon1, lat2, lon2)
return(distance_in_km)
}
Sample output
custom_hav_dist(50.31,19.08,54.14,19.39)
[1] 426.3987
PS: To calculate distances in miles, substitute R in function (6371) with 3958.756 (and for nautical miles, use 3440.065).
To calculate the distance between two points on a sphere you need to do the Great Circle calculation.
There are a number of C/C++ libraries to help with map projection at MapTools if you need to reproject your distances to a flat surface. To do this you will need the projection string of the various coordinate systems.
You may also find MapWindow a useful tool to visualise the points. Also as its open source its a useful guide to how to use the proj.dll library, which appears to be the core open source projection library.
Here is my java implementation for calculation distance via decimal degrees after some search. I used mean radius of world (from wikipedia) in km. İf you want result miles then use world radius in miles.
public static double distanceLatLong2(double lat1, double lng1, double lat2, double lng2)
{
double earthRadius = 6371.0d; // KM: use mile here if you want mile result
double dLat = toRadian(lat2 - lat1);
double dLng = toRadian(lng2 - lng1);
double a = Math.pow(Math.sin(dLat/2), 2) +
Math.cos(toRadian(lat1)) * Math.cos(toRadian(lat2)) *
Math.pow(Math.sin(dLng/2), 2);
double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a));
return earthRadius * c; // returns result kilometers
}
public static double toRadian(double degrees)
{
return (degrees * Math.PI) / 180.0d;
}
Here's the accepted answer implementation ported to Java in case anyone needs it.
package com.project529.garage.util;
/**
* Mean radius.
*/
private static double EARTH_RADIUS = 6371;
/**
* Returns the distance between two sets of latitudes and longitudes in meters.
* <p/>
* Based from the following JavaScript SO answer:
* http://stackoverflow.com/questions/27928/calculate-distance-between-two-latitude-longitude-points-haversine-formula,
* which is based on https://en.wikipedia.org/wiki/Haversine_formula (error rate: ~0.55%).
*/
public double getDistanceBetween(double lat1, double lon1, double lat2, double lon2) {
double dLat = toRadians(lat2 - lat1);
double dLon = toRadians(lon2 - lon1);
double a = Math.sin(dLat / 2) * Math.sin(dLat / 2) +
Math.cos(toRadians(lat1)) * Math.cos(toRadians(lat2)) *
Math.sin(dLon / 2) * Math.sin(dLon / 2);
double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
double d = EARTH_RADIUS * c;
return d;
}
public double toRadians(double degrees) {
return degrees * (Math.PI / 180);
}
For those looking for an Excel formula based on WGS-84 & GRS-80 standards:
=ACOS(COS(RADIANS(90-Lat1))*COS(RADIANS(90-Lat2))+SIN(RADIANS(90-Lat1))*SIN(RADIANS(90-Lat2))*COS(RADIANS(Long1-Long2)))*6371
Source
there is a good example in here to calculate distance with PHP http://www.geodatasource.com/developers/php :
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;
}
}
Here is the implementation VB.NET, this implementation will give you the result in KM or Miles based on an Enum value you pass.
Public Enum DistanceType
Miles
KiloMeters
End Enum
Public Structure Position
Public Latitude As Double
Public Longitude As Double
End Structure
Public Class Haversine
Public Function Distance(Pos1 As Position,
Pos2 As Position,
DistType As DistanceType) As Double
Dim R As Double = If((DistType = DistanceType.Miles), 3960, 6371)
Dim dLat As Double = Me.toRadian(Pos2.Latitude - Pos1.Latitude)
Dim dLon As Double = Me.toRadian(Pos2.Longitude - Pos1.Longitude)
Dim a As Double = Math.Sin(dLat / 2) * Math.Sin(dLat / 2) + Math.Cos(Me.toRadian(Pos1.Latitude)) * Math.Cos(Me.toRadian(Pos2.Latitude)) * Math.Sin(dLon / 2) * Math.Sin(dLon / 2)
Dim c As Double = 2 * Math.Asin(Math.Min(1, Math.Sqrt(a)))
Dim result As Double = R * c
Return result
End Function
Private Function toRadian(val As Double) As Double
Return (Math.PI / 180) * val
End Function
End Class
I condensed the computation down by simplifying the formula.
Here it is in Ruby:
include Math
earth_radius_mi = 3959
radians = lambda { |deg| deg * PI / 180 }
coord_radians = lambda { |c| { :lat => radians[c[:lat]], :lng => radians[c[:lng]] } }
# from/to = { :lat => (latitude_in_degrees), :lng => (longitude_in_degrees) }
def haversine_distance(from, to)
from, to = coord_radians[from], coord_radians[to]
cosines_product = cos(to[:lat]) * cos(from[:lat]) * cos(from[:lng] - to[:lng])
sines_product = sin(to[:lat]) * sin(from[:lat])
return earth_radius_mi * acos(cosines_product + sines_product)
end
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;
}}
Chuck's solution, valid for miles also.
In Mysql use the following function pass the parameters as using 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;
I'm interested in working with coordinates, and I was wondering, how is it possible to get the distance between two points (coordinates) in meters. After a long search I found the Haversine Formula, and the Objective-C implementation of it here.
It's the following (I modified it a little bit for myself):
- (CGFloat)directMetersFromCoordinate:(CLLocation *)from toCoordinate:(CLLocation *)to {
static const double DEG_TO_RAD = 0.017453292519943295769236907684886;
static const double EARTH_RADIUS_IN_METERS = 6372797.560856;
double latitudeArc = (from.coordinate.latitude - to.coordinate.latitude) * DEG_TO_RAD;
double longitudeArc = (from.coordinate.longitude - to.coordinate.longitude) * DEG_TO_RAD;
double latitudeH = sin(latitudeArc * 0.5);
latitudeH *= latitudeH;
double lontitudeH = sin(longitudeArc * 0.5);
lontitudeH *= lontitudeH;
double tmp = cos(from.coordinate.latitude*DEG_TO_RAD) * cos(to.coordinate.latitude*DEG_TO_RAD);
return EARTH_RADIUS_IN_METERS * 2.0 * asin(sqrt(latitudeH + tmp*lontitudeH)); }
My question is:
How is it possible to get 100 meters distance (for latitude and for longitude) for the current location?
This formula is too complicated for me, I don't understand it, so I can't "code it back" to get the result what I want.
I need only the actual location (Paris, Tokyo, London, New York, whatever), and a (float) number for latitude and a (float) number for longitude, which (float) number represents 100 meters distance from the actual location.
If you open this page, here you can "calculate out" the 100 meters distance between two points ("actual" and the one 100 meters away).
For example:
Point1: 47.0, 19.0
Point2: 47.0, 19.0013190
---That's 0.1000 km (100 m) distance.
Point1: 47.0, 19.0
Point2: 47.0008995, 19.0
---is also 0.1000 km (100 m) distance.
Here you can see, that at that coordinate (latitude 47.0 and longitude 19.0) 100 meters distance is 0.0008995 (latitude) and 0.0013190 (longitude).
And I want to get these data with the help of the Haversine Formula, just don't know, how.
Could you help me to figure it out?
Thanks!
UPDATE:
Thank you for the answers, right now I don't have time to try them out, but as I understood, I didn't explain exactly what I want.
Maybe this is a better example, how I want to use these "100 meters":
So, right now I'm at the coordinate "lat x" and "lon y". There is another point (let's say a hotel) at another given coordinate, "lat a" and "lon b".
My question is: how can I calculate out, if this hotel is (less than) 100 meters from me? So it doesn't matter, if it's only 5 meters or 99 meters, both of them are less (or equal) than 100 meters far from me.
With the code what I provided, I can calculate this out, that's what that formula is for.
But let's say, I have a million of other coordinates (hotel locations) that I want to work with. And I need only a list of them, what are (less than) 100 meters away from me. So yes, that's a circle around me with a radius of 100 meters, and I need the results within that.
It would take much more time and resource to take all the coordinates of these "million" hotels and calculate the distance one by one, that's why I thought it would be much easier to calculate out, how much 100 meters are in latitude and longitude (changes the value as we are on different locations, that' why I can't use simply the ones what I calculated out in the example above).
So if I would know how much 100 meters are in latitude and longitude for example at London's coordinate (if I'm there), I could simply get the list of the hotels what are (less than) 100 meters far from me, by a simple dividing:
if
((hotelLocation.coordinate.latitude <= (myLocation.coordinate.latitude + "100metersInLatitude")) || (hotelLocation.coordinate.latitude >= (myLocation.coordinate.latitude - "100metersInLatitude")))
&&
((hotelLocation.coordinate.longitude <= (myLocation.coordinate.longitude + "100metersInLongitude")) || (hotelLocation.coordinate.longitude >= (myLocation.coordinate.longitude - "100metersInLongitude")))
{
NSLog(#"Be Happy :-) !");
}
I just need these "100metersInLatitude" and "100metersInLongitude", calculated always from "myLocation".
Whoa, I hope, somebody will understand what I just wrote down, because it's not easy for me, neither... :-)))
Assuming you have a point with latitude and longitude, and you want to find another point that is a distance d on a bearing b, when the distance is small (you said "100 meters" which is very small on the surface of the earth) then you can do a simple approximation - treating the earth's surface locally as "flat". Here is a simple C program that implements this (using the numbers you had above). I updated it to include the "accurate" formulation as well - it's just a few more calculations, but it is accurate at all distances (and not just the short ones). The equation I used came from the link you referenced - subheading "Destination point given distance and bearing from start point"
updated - I moved the accurate calculation into a separate function, and added a loop to compute the new point for all integer bearings from 0 to 359, printing out every 30th. This give you the "circle" I talked about in my initial comment.
#include <stdio.h>
#include <math.h>
double radians(double x) {
return acos(0.0) * x / 90.0;
}
void calcNewPosition(double lat, double lon, double bearing, double d, double *newLat, double *newLon) {
double lat1, lon1, br, pi;
double Re = 6371000;
// convert everything to radians first:
lat1 = radians(lat);
lon1 = radians(lon);
br = radians(bearing);
pi = 2 * acos(0.0);
double lat2, lon2;
lat2 = asin( sin(lat1) * cos(d/Re) +
cos( lat1 ) * sin( d / Re ) * cos(br ) );
lon2 = lon1 + atan2(sin(br) * sin( d / Re ) * cos( lat1 ), \
cos( d / Re ) - sin(lat1 ) * sin( lat2 ) );
*newLat = 180. * lat2 / pi;
*newLon = 180. * lon2 / pi;
}
int main(void) {
double lon = 47., lat=19.;
double newLongitude, newLatitude;
double dx, dy, dLong, dLat;
double Re = 6371000, d = 100, bearing = 0.0;
double pi;
double lat1, lon1, br;
// convert everything to radians first:
lat1 = radians(lat);
lon1 = radians(lon);
br = radians(bearing);
pi = 2 * acos(0.0);
// approximate calculation - using equirectangular approximation
// and noting that distance between meridians (lines of longitude)
// get closer at higher latitudes, with cos(latitude).
dx = d * sin(br); // distance in E-W direction
dy = d * cos(br); // distance in N-S direction
dLat = 360 * dy / (2.0 * pi * Re); // convert N-S to degrees latitude
dLong = 360 * dx / (2.0 * pi * Re * cos(lat1)); // convert E-W to degrees longitude
newLatitude = lat + dLat;
newLongitude = lon + dLong;
printf("simple forumula: the new position is %.8lf lon, %.8lf lat\n", newLongitude, newLatitude);
// more accurate formula: based on http://www.movable-type.co.uk/scripts/latlong.html
double lat2, lon2;
calcNewPosition(lat, lon, bearing, d, &lat2, &lon2);
printf("more accurate: the new position is %.8lf lon, %.8lf lat\n", lon2, lat2);
// now loop over all bearings and compute the "circle of points":
int iBearing;
double lonArray[360], latArray[360];
for(iBearing = 0; iBearing < 360; iBearing++) {
calcNewPosition(lat, lon, (double)iBearing, d, &latArray[iBearing], &lonArray[iBearing]);
if (iBearing % 30 == 0) printf("bearing %03d: new lat = %.8lf, new lon = %.8lf\n", iBearing, latArray[iBearing], lonArray[iBearing]);
}
return 0;
}
The output of this is
simple forumula: the new position is 47.00000000 lon, 19.00089932 lat
more accurate: the new position is 47.00000000 lon, 19.00089932 lat
bearing 000: new lat = 19.00089932, new lon = 47.00000000
bearing 030: new lat = 19.00077883, new lon = 47.00047557
bearing 060: new lat = 19.00044966, new lon = 47.00082371
bearing 090: new lat = 19.00000000, new lon = 47.00095114
bearing 120: new lat = 18.99955034, new lon = 47.00082371
bearing 150: new lat = 18.99922116, new lon = 47.00047557
bearing 180: new lat = 18.99910068, new lon = 47.00000000
bearing 210: new lat = 18.99922116, new lon = 46.99952443
bearing 240: new lat = 18.99955034, new lon = 46.99917629
bearing 270: new lat = 19.00000000, new lon = 46.99904886
bearing 300: new lat = 19.00044966, new lon = 46.99917629
bearing 330: new lat = 19.00077883, new lon = 46.99952443
As you can see, it is accurate to within a fraction of a meter (your code gave 19.0008995 - it is actually possible that your result was "wrong" in the last digit as these two methods agree to 8 significant digits even though they use different equations).
The question isn't really answerable if the OP wants a location a distance (100 meters) from the current location without a desired bearing being provided, there being an infinite number of points in a circle around the point.
So, this answer may or may not be what the OP wants, it is a way with CLLocation to calculate the distance between two points.
Create two CLLocation point and use the method Have you looked at theCLLocationmethod- (CLLocationDistance)distanceFromLocation:(const CLLocation *)location`.
CLLocation *location1 = [[CLLocation alloc] initWithLatitude:)latitude1 longitude:longitude1];
CLLocation *location2 = [[CLLocation alloc] initWithLatitude:)latitude2 longitude:longitude2];
double distance = [location1 distanceFromLocation:location2];
The radius of the earth at equator = 6,371 km. The equator is divided into 360 degrees of longitude, so each degree at the equator represents approximately 111.32 km. Moving away from the equator towards a pole this distance decreases to zero at the pole.To calculate the distance at different latitudes multiply it by the cosine of the latitude
3 decimal places,0.001 degrees aproximates to
111.32 meters at equator
96.41meters at 30 degrees N/S
78.71 meters at 45 degrees N/S
55.66 meters at 60 degrees N/S
28.82 meters at 75 degrees N/S
For for small distances (100 meters) Pythagoras’ theorem can be used on an equirectangular projection to calculate distance. This is less complicated than Haversine or Spherical Law of Cosines.
var R = 6371; // km
lat/lng in radians
In pseudo code as I don't know Objective-C
var x = (lng2-lng1) * cos((lat1+lat2)/2);
var y = (lat2-lat1);
var d = sqrt(x*x + y*y) * R;
I'm looking for a smooth way to calculate the distance between two GPS Points, so I get the result like: "You have to go x meters up and y meters to the left - so I can work with a 2d-coordinate system, where I have my position as (0,0) and the other positions is showing the distance in (x, y) in meters from my position.
My idea was to calculate the distance between the points using the haversine formula. (This returns my hypotenuse)
In addition to that, I'm calculating the bearing between this two points. This is my alpha.
With this two values, I wanted to use basic trigonometry functions to resolve my problem.
So I tried to calculate:catheti_1 = sin(alpha) * hypotenuse, catheti_2 = cos(alpha) * hypotenuse.
Maybe I'm doing something wrong, but my results are useless at the moment.
So my question is: How can I calculate the distance in x and y direction between two GPS points?
I'm calculating alpha in the following procedure:
public static double bearingTo(GPSBean point1, GPSBean point2) {
double lat1 = Math.toRadians(point1.latitude);
double lat2 = Math.toRadians(point2.latitude);
double lon1 = Math.toRadians(point1.longitude);
double lon2 = Math.toRadians(point2.longitude);
double deltaLong = lon2 - lon1;
double y = Math.sin(deltaLong) * Math.cos(lat2);
double x = Math.cos(lat1) * Math.sin(lat2) - Math.sin(lat1)
* Math.cos(lat2) * Math.cos(deltaLong);
double bearing = Math.atan2(y, x);
return (Math.toDegrees(bearing) + 360) % 360;
}
I just implemented your code, using approximate coordinates of NYC and Boston as reference points, and implementing the Haversine formula as found at http://www.movable-type.co.uk/scripts/latlong.html (which you didn't show):
long1 = -71.02; lat1 = 42.33;
long2 = -73.94; lat2 = 40.66;
lat1 *=pi/180;
lat2 *=pi/180;
long1*=pi/180;
long2*=pi/180;
dlong = (long2 - long1);
dlat = (lat2 - lat1);
// Haversine formula:
R = 6371;
a = sin(dlat/2)*sin(dlat/2) + cos(lat1)*cos(lat2)*sin(dlong/2)*sin(dlong/2)
c = 2 * atan2( sqrt(a), sqrt(1-a) );
d = R * c;
When I run this code, I get d = 306, which agrees with the answer from the above site.
For the bearing I get 52 deg - again, close to what the site gave.
Without seeing the rest of your code it's hard to know why your answer is different.
Note: when the two points are close together, you could make all kinds of approximations, but this code should still work - the formula has good numerical stability because it's using the sin of the difference between longitudes, latitudes (rather than the difference of the sin).
Addendum:
Using your code for x, y (in your question), I get sensible values for the distance - agreeing with the "proper" answer to within 120 m (which isn't bad since one is a straight line approximation and the other follows the curvature of the earth). So I think your code is basically OK now you fixed the typo.
Use Haversine formula to Calculate distance (in km) between two points specified by latitude/longitude (in numeric degrees)
from: Haversine formula - R. W. Sinnott, "Virtues of the Haversine"
Sky and Telescope, vol 68, no 2, 1984
http://www.census.gov/cgi-bin/geo/gisfaq?Q5.1
Example usage from form:
result.value = LatLon.distHaversine(lat1.value.parseDeg(), long1.value.parseDeg(), * lat2.value.parseDeg(), long2.value.parseDeg());
Javascript :
LatLon.distHaversine = function(lat1, lon1, lat2, lon2) {
var R = 6371; // earth's mean radius in km
var dLat = (lat2-lat1).toRad();
var dLon = (lon2-lon1).toRad();
lat1 = lat1.toRad(), lat2 = lat2.toRad();
var a = Math.sin(dLat/2) * Math.sin(dLat/2) +
Math.cos(lat1) * Math.cos(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;
return d;
}
If anybody is interested to have a simpler formula that anyone can understand. Here is mine, it works for Sweden, but you can adapt it to work anywhere by making a more general formula for calculation of longfactor.
Hope you can understand even if it is written in an odd language.
<gpsDist lat1,long1,lat2,long2> all parameters in 1/100000 degree.
Example: <getDist 5950928,1327120,5958505,1302241> => 16303
Same at https://gps-coordinates.org/distance-between-coordinates.php => 16.35 KM.
<var $latFactor,1.112>
<function getDist,
-<var $longFactor,<calc 0.638 - ($1/100000-55)*0.0171,3>>
-<var $latDist,<calc ($3-$1)*$latFactor>>
-<var $longDist,<calc ($4-$2)*$longFactor>>
-<sqrt $latDist*$latDist + $longDist*$longDist>
->
/Bertil Friman