I need to calculate a distance vector from two GPS coordinates.
The purpose is to calculate the vector of one's change in position,
so the coordinates are not far from each other.
I would like to calculate the latitudinal and longitudinal distances in meters.
I found something here,
but this only gives the direction without distance.
Due to the fact that those coordinates are very close in my case,
I made the approximation that the center of the earth and those two points form a triangle.
Thus, I can use the Al Kashi theorem.
Here is the code:
// Common values
double b = EARTH_RADIUS + destination.altitude;
double c = EARTH_RADIUS + this.altitude;
double b2 = b*b;
double c2 = c*c;
double bc2 = 2*b*c;
// Longitudinal calculations
double alpha = destination.longitude - this.longitude;
// Conversion to radian
alpha = alpha * Math.PI / 180;
// Small-angle approximation
double cos = 1 - alpha*alpha/2; //Math.cos(alpha);
// Use the law of cosines / Al Kashi theorem
double x = Math.sqrt(b2 + c2 - bc2*cos);
// Repeat for latitudinal calculations
alpha = destination.latitude - this.latitude;
alpha = alpha * Math.PI / 180;
double cos = 1 - alpha*alpha/2; //Math.cos(alpha);
double y = Math.sqrt(b2 + c2 - bc2*cos);
// Obtain vertical difference, too
double z = destination.altitude - this.altitude;
return new Vector3D(x, y, z);
As you can see,
I have approximated the cosine because the angles are really small.
I think adding the altitude to the earth's radius doesn't give a better approximation,
but since I have it…
I tested it compared to Google Maps for a distance of 38 meters and I got a result of 37.877.
My result might be more accurate! ^^
I have a service that looks for nearby locations(300m) from a user specified point.
I'm using the haversine formula to check if a location is near the point
https://en.wikipedia.org/wiki/Haversine_formula
My problem is that it's slow since it's checking against all of the points in my DB.
What I want to do is limit the initial query and apply the haversine formula to a list of points in a smaller bounded area
e.g.
results = (SELECT * FROM location WHERE location.latitude BETWEEN 14.223 AND 14.5 )
AND location.longitude BETWEEN 121.5 AND 122
haversine(results, user_point)
Is there a loose way of getting the bounds from a given point?
Or basically a dirty conversion of lat/long to meters?
If you can modify your database structure, there's one super-easy way to do it: instead of (or in addition to) storing latitude and longitude, convert your location coordinates into 3D space, with columns for x, y, and z in meters. Then you can just do
SELECT * FROM location
WHERE location.x BETWEEN center.x - 300 AND center.x + 300
AND location.y BETWEEN center.y - 300 AND center.y + 300
AND location.z BETWEEN center.z - 300 AND center.z + 300
That will trim down your list pretty well, and you can do the haversine calculation on the resulting set.
If you're stuck with using a database that has only longitude and latitude in it, you can still narrow down the search. It's easy for latitude: one degree of latitude due north or south always corresponds to 111 km of distance, as long as you ignore the complications that arise when you get close to the poles. That means a distance of 300 m is 0.0027... degrees of latitude, although you might as well be a bit conservative and use 0.003 or 0.004.
Longitude is a bit trickier because the conversion factor changes depending on how far north or south you are, but it's still not too complicated: you just multiply by the cosine of the latitude.
distance = cos(latitude) * 111.19... km/degree * delta_angle
At the equator, it's the same as with latitude: one degree change in longitude at the equator is 111 km. At 80 degrees north (or south), you multiply by a factor of cos(80 degrees) = 0.17..., with the result that 1 degree change in longitude is only 19.3 km. For your purposes, you could invert this and find the range of longitudes to select as 300 m / cos(latitude) / (111.19... km/degree) = (0.0027... degrees) / cos(latitude). That coefficient is the same quantity from the first paragraph; it's not a coincidence.
The tricky problems come up near the discontinuities of the coordinate system, for example when you get near the poles. You can see why when you start plugging in latitudes like 89.9996 degrees:
0.0027... degrees / cos(89.9996 degrees) = 386... degrees
Well, how can that be when there are only 360 degrees in a whole circle? This is an indicator that you've gotten to the point where your 300 m radius extends all the way around the pole and comes back to include your starting location, in a manner of speaking. At that point, you might as well just search all points in your database close enough to the pole. Of course you should really start doing this at 89.999 degrees or so, because that's where the 600 m diameter of the region you're searching just about encircles the pole completely.
There's another issue at (well, near) the International Date Line, or more precisely the "antimeridian", having to do with the jump from -180 to +180 degrees of longitude. A point at +179.9999 degrees and one at -179.9999 degrees, both on the equator, will have very different coordinates even though they are geographically just a few meters apart. Since you're just doing this as a preliminary filter for a more detailed search, it's probably easiest to just pass through every point within 0.006 degrees (that's roughly the diameter of a 300 m-radius circle) of the antimeridian, and then the haversine calculation will determine whether the points are actually close.
To sum up, you can use the bounds on latitude and longitude I mentioned above and just add special cases for the poles and the antimeridian. In some kind of pseudo-SQL/code hybrid:
IF abs(center.latitude) > 89.999
SELECT * FROM location WHERE abs(location.latitude - center.latitude) < 0.003
ELSE
IF abs(center.longitude) > 179.997
SELECT * FROM location
WHERE abs(location.latitude - center.latitude) < 0.003
AND 180 - abs(location.longitude) < (0.006 / cos(center.latitude))
ELSE
SELECT * FROM location
WHERE abs(location.latitude - center.latitude) < 0.003
AND abs(location.longitude - center.longitude) < (0.003 / cos(center.latitude))
ENDIF
ENDIF
If you want a pithy statement at the expense of having to test potentially twice as many points, you can only compare the absolute values of longitude:
SELECT * FROM location
WHERE abs(location.latitude - center.latitude) < 0.003
AND abs(abs(location.longitude) - abs(center.longitude)) <= min(0.003 / cos(center.latitude), 180)
Approximating the earth with a sphere, the distance between two consecutive latitudes can be calculated by
dPerLat = pi * r / 180°,
where r is the radius of the earth. This will be about 111 km.
So, if your reference point is (lat, long) and your search radius is d then you want to search for latitudes in the range
lat* \in [lat - d / dPerLat, lat + d / dPerLat]
Then, for a given latitude, the distance of consecutive longitudes is:
dPerLong = pi * r * cos(lat) / 180°
Again, the range of longitudes to search is +- d / dPerLong. You should use the lat value that gives you a conservative (maximal) range, i.e. the lat value with the highest absolute value.
Be careful at the poles.
Time for a little bit of math for the end of the day..
I need to project 4 points of the window size:
<0,0> <1024,768>
Into a world space coordinates so it will form a quadrilateral shape that will later be used for terrain culling - without GluUnproject
For test only, I use mouse coordinates - and try to project them onto the world coords
RESOLVED
Here's how to do it exactly, step by step.
Obtain your mouse coordinates within the client area
Get your Projection matrix and View matrix if no Model matrix required.
Multiply Projection * View
Inverse the results of multiplication
Construct a vector4 consisting of
x = mouseposition.x within a range of window x
transform to values between -1 and 1
y = mouseposition.y within a range of window y
transform to values between -1 and 1
remember to invert mouseposition.y if needed
z = the depth value ( this can be obtained with glReadPixel)
you can manually go from -1 to 1 ( zNear, zFar )
w = 1.0
Multiply the vector by inversed matrix created before
Divide result vector by it's w component after matrix multiplication ( perspective division )
POINT mousePos;
GetCursorPos(&mousePos);
ScreenToClient( this->GetWindowHWND(), &mousePos );
CMatrix4x4 matProjection = m_pCamera->getViewMatrix() * m_pCamera->getProjectionMatrix() ;
CMatrix4x4 matInverse = matProjection.inverse();
float in[4];
float winZ = 1.0;
in[0]=(2.0f*((float)(mousePos.x-0)/(this->GetResolution().x-0)))-1.0f,
in[1]=1.0f-(2.0f*((float)(mousePos.y-0)/(this->GetResolution().y-0)));
in[2]=2.0* winZ -1.0;
in[3]=1.0;
CVector4 vIn = CVector4(in[0],in[1],in[2],in[3]);
pos = vIn * matInverse;
pos.w = 1.0 / pos.w;
pos.x *= pos.w;
pos.y *= pos.w;
pos.z *= pos.w;
sprintf(strTitle,"%f %f %f / %f,%f,%f ",m_pCamera->m_vPosition.x,m_pCamera->m_vPosition.y,m_pCamera->m_vPosition.z,pos.x,pos.y,pos.z);
SetWindowText(this->GetWindowHWND(),strTitle);
I had to make some adjustments to the answers provided here. But here's the code I ended up with (Note I'm using GLM, that could affect multiplication order). nearResult is the projected point on the near plane and farResult is the projected point on the far plane. I want to perform a ray cast to see what my mouse is hovering over so I convert them to a direction vector which will then originate from my camera's position.
vec3 getRayFromScreenSpace(const vec2 & pos)
{
mat4 invMat= inverse(m_glData.getPerspective()*m_glData.getView());
vec4 near = vec4((pos.x - Constants::m_halfScreenWidth) / Constants::m_halfScreenWidth, -1*(pos.y - Constants::m_halfScreenHeight) / Constants::m_halfScreenHeight, -1, 1.0);
vec4 far = vec4((pos.x - Constants::m_halfScreenWidth) / Constants::m_halfScreenWidth, -1*(pos.y - Constants::m_halfScreenHeight) / Constants::m_halfScreenHeight, 1, 1.0);
vec4 nearResult = invMat*near;
vec4 farResult = invMat*far;
nearResult /= nearResult.w;
farResult /= farResult.w;
vec3 dir = vec3(farResult - nearResult );
return normalize(dir);
}
Multiply all your matrices. Then invert the result. Point after projection are always in the -1,1. So the four corner screen points are -1,-1; -1,1; 1,-1;1,1. But you still need to choose th z value. If you are in OpenGL, z is between -1 and 1. For directx, the range is 0 to 1. Finally take your points and transform them with the matrix
If you have access to the glu libraries, use gluUnProject(winX, winY, winZ, model, projection, viewport, &objX, &objY, &objZ);
winX and winY will be the corners of your screen in pixels. winZ is a number in [0,1] which will specify where between zNear and zFar (clipping planes) the points should fall. objX-Z will hold the results. The middle variables are the relevant matrices. They can be queried if needed.
If I have a latitude or longitude reading in standard NMEA format is there an easy way / formula to convert that reading to meters, which I can then implement in Java (J9)?
Edit:
Ok seems what I want to do is not possible easily, however what I really want to do is:
Say I have a lat and long of a way point and a lat and long of a user is there an easy way to compare them to decide when to tell the user they are within a reasonably close distance of the way point? I realise reasonable is subject but is this easily do-able or still overly maths-y?
Here is a javascript function:
function measure(lat1, lon1, lat2, lon2){ // generally used geo measurement function
var R = 6378.137; // Radius of earth in KM
var dLat = lat2 * Math.PI / 180 - lat1 * Math.PI / 180;
var dLon = lon2 * Math.PI / 180 - lon1 * Math.PI / 180;
var a = Math.sin(dLat/2) * Math.sin(dLat/2) +
Math.cos(lat1 * Math.PI / 180) * Math.cos(lat2 * Math.PI / 180) *
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 * 1000; // meters
}
Explanation: https://en.wikipedia.org/wiki/Haversine_formula
The haversine formula determines the great-circle distance between two points on a sphere given their longitudes and latitudes.
Given you're looking for a simple formula, this is probably the simplest way to do it, assuming that the Earth is a sphere with a circumference of 40075 km.
Length in km of 1° of latitude = always 111.32 km
Length in km of 1° of longitude = 40075 km * cos( latitude ) / 360
For approximating short distances between two coordinates I used formulas from
http://en.wikipedia.org/wiki/Lat-lon:
m_per_deg_lat = 111132.954 - 559.822 * cos( 2 * latMid ) + 1.175 * cos( 4 * latMid);
m_per_deg_lon = 111132.954 * cos ( latMid );
.
In the code below I've left the raw numbers to show their relation to the formula from wikipedia.
double latMid, m_per_deg_lat, m_per_deg_lon, deltaLat, deltaLon,dist_m;
latMid = (Lat1+Lat2 )/2.0; // or just use Lat1 for slightly less accurate estimate
m_per_deg_lat = 111132.954 - 559.822 * cos( 2.0 * latMid ) + 1.175 * cos( 4.0 * latMid);
m_per_deg_lon = (3.14159265359/180 ) * 6367449 * cos ( latMid );
deltaLat = fabs(Lat1 - Lat2);
deltaLon = fabs(Lon1 - Lon2);
dist_m = sqrt ( pow( deltaLat * m_per_deg_lat,2) + pow( deltaLon * m_per_deg_lon , 2) );
The wikipedia entry states that the distance calcs are within 0.6m for 100km longitudinally and 1cm for 100km latitudinally but I have not verified this as anywhere near that accuracy is fine for my use.
Here is the R version of b-h-'s function, just in case:
measure <- function(lon1,lat1,lon2,lat2) {
R <- 6378.137 # radius of earth in Km
dLat <- (lat2-lat1)*pi/180
dLon <- (lon2-lon1)*pi/180
a <- sin((dLat/2))^2 + cos(lat1*pi/180)*cos(lat2*pi/180)*(sin(dLon/2))^2
c <- 2 * atan2(sqrt(a), sqrt(1-a))
d <- R * c
return (d * 1000) # distance in meters
}
The earth is an annoyingly irregular surface, so there is no simple formula to do this exactly. You have to live with an approximate model of the earth, and project your coordinates onto it. The model I typically see used for this is WGS 84. This is what GPS devices usually use to solve the exact same problem.
NOAA has some software you can download to help with this on their website.
There are many tools that will make this easy. See monjardin's answer for more details about what's involved.
However, doing this isn't necessarily difficult. It sounds like you're using Java, so I would recommend looking into something like GDAL. It provides java wrappers for their routines, and they have all the tools required to convert from Lat/Lon (geographic coordinates) to UTM (projected coordinate system) or some other reasonable map projection.
UTM is nice, because it's meters, so easy to work with. However, you will need to get the appropriate UTM zone for it to do a good job. There are some simple codes available via googling to find an appropriate zone for a lat/long pair.
One nautical mile (1852 meters) is defined as one arcminute of longitude at the equator. However, you need to define a map projection (see also UTM) in which you are working for the conversion to really make sense.
There are quite a few ways to calculate this. All of them use aproximations of spherical trigonometry where the radius is the one of the earth.
try http://www.movable-type.co.uk/scripts/latlong.html for a bit of methods and code in different languages.
'below is from
'http://www.zipcodeworld.com/samples/distance.vbnet.html
Public Function distance(ByVal lat1 As Double, ByVal lon1 As Double, _
ByVal lat2 As Double, ByVal lon2 As Double, _
Optional ByVal unit As Char = "M"c) As Double
Dim theta As Double = lon1 - lon2
Dim dist As Double = Math.Sin(deg2rad(lat1)) * Math.Sin(deg2rad(lat2)) + _
Math.Cos(deg2rad(lat1)) * Math.Cos(deg2rad(lat2)) * _
Math.Cos(deg2rad(theta))
dist = Math.Acos(dist)
dist = rad2deg(dist)
dist = dist * 60 * 1.1515
If unit = "K" Then
dist = dist * 1.609344
ElseIf unit = "N" Then
dist = dist * 0.8684
End If
Return dist
End Function
Public Function Haversine(ByVal lat1 As Double, ByVal lon1 As Double, _
ByVal lat2 As Double, ByVal lon2 As Double, _
Optional ByVal unit As Char = "M"c) As Double
Dim R As Double = 6371 'earth radius in km
Dim dLat As Double
Dim dLon As Double
Dim a As Double
Dim c As Double
Dim d As Double
dLat = deg2rad(lat2 - lat1)
dLon = deg2rad((lon2 - lon1))
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)
c = 2 * Math.Atan2(Math.Sqrt(a), Math.Sqrt(1 - a))
d = R * c
Select Case unit.ToString.ToUpper
Case "M"c
d = d * 0.62137119
Case "N"c
d = d * 0.5399568
End Select
Return d
End Function
Private Function deg2rad(ByVal deg As Double) As Double
Return (deg * Math.PI / 180.0)
End Function
Private Function rad2deg(ByVal rad As Double) As Double
Return rad / Math.PI * 180.0
End Function
To convert latitude and longitude in x and y representation you need to decide what type of map projection to use. As for me, Elliptical Mercator seems very well. Here you can find an implementation (in Java too).
Here is a MySQL function:
SET #radius_of_earth = 6378.137; -- In kilometers
DROP FUNCTION IF EXISTS Measure;
DELIMITER //
CREATE FUNCTION Measure (lat1 REAL, lon1 REAL, lat2 REAL, lon2 REAL) RETURNS REAL
BEGIN
-- Multiply by 1000 to convert millimeters to meters
RETURN 2 * #radius_of_earth * 1000 * ASIN(SQRT(
POW(SIN((lat2 - lat1) / 2 * PI() / 180), 2) +
COS(lat1 * PI() / 180) *
COS(lat2 * PI() / 180) *
POW(SIN((lon2 - lon1) / 2 * PI() / 180), 2)
));
END; //
DELIMITER ;
Why limiting to one degree?
The formula is based on the proportion:
distance[m] : distance[deg] = max circumference[m] : 360[deg]
Lets say you are given an angle for a latitude and one for longitude both in degrees: (longitude[deg], latitude[deg])
For the latitude, the max circumference is always the one passing for the poles. In a spherical model, with radius R (in meters) the max circumference is 2 * pi * R and the proportions resolves to:
latitude[m] = ( 2 * pi * R[m] * latitude[deg] ) / 360[deg]
(note that deg and deg simplifies, and what remains is meters on both sides).
For the longitude the max circumference is proportional to the cosine of the latitude (as you can imagine running in circle the north pole is shorter than running in circle around the equator), so it is 2 * pi * R * cos(latitude[rad]).
Therefore
longitude distance[m] = ( 2 * pi * R[m] * cos(latitude[rad]) * longitude[deg] ) / 360[deg]
Note that you will have to convert the latitude from deg to rad before computing the cos.
Omitting details for who is just looking for the formula:
lat_in_m = 111132.954 * lat_in_degree / 360
lon_in_m = 111132.954 * cos(lat_in_radians) * lon_in_deg ) / 360
If its sufficiently close you can get away with treating them as coordinates on a flat plane. This works on say, street or city level if perfect accuracy isnt required and all you need is a rough guess on the distance involved to compare with an arbitrary limit.
Here is a version in Swift:
func toDegreeAt(point: CLLocationCoordinate2D) -> CLLocationDegrees {
let latitude = point.latitude
let earthRadiusInMetersAtSeaLevel = 6378137.0
let earthRadiusInMetersAtPole = 6356752.314
let r1 = earthRadiusInMetersAtSeaLevel
let r2 = earthRadiusInMetersAtPole
let beta = latitude
let earthRadiuseAtGivenLatitude = (
( pow(pow(r1, 2) * cos(beta), 2) + pow(pow(r2, 2) * sin(beta), 2) ) /
( pow(r1 * cos(beta), 2) + pow(r2 * sin(beta), 2) )
)
.squareRoot()
let metersInOneDegree = (2 * Double.pi * earthRadiuseAtGivenLatitude * 1.0) / 360.0
let value: CLLocationDegrees = self / metersInOneDegree
return value
}
Original poster asked
"If I have a latitude or longitude reading in standard NMEA format is there an easy way / formula to convert that reading to meters"
I haven't used Java in a while so I did the solution here in "PARI".
Just plug your point's latitude and longitudes
into the equations below to get
the exact arc lengths and scales
in meters per (second of Longitude)
and meters per (second of Latitude).
I wrote these equations for
the free-open-source-mac-pc math program "PARI".
You can just paste the following into it
and the I will show how to apply them to two made up points:
\\=======Arc lengths along Latitude and Longitude and the respective scales:
\p300
default(format,"g.42")
dms(u)=[truncate(u),truncate((u-truncate(u))*60),((u-truncate(u))*60-truncate((u-truncate(u))*60))*60];
SpinEarthRadiansPerSec=7.292115e-5;\
GMearth=3986005e8;\
J2earth=108263e-8;\
re=6378137;\
ecc=solve(ecc=.0001,.9999,eccp=ecc/sqrt(1-ecc^2);qecc=(1+3/eccp^2)*atan(eccp)-3/eccp;ecc^2-(3*J2earth+4/15*SpinEarthRadiansPerSec^2*re^3/GMearth*ecc^3/qecc));\
e2=ecc^2;\
b2=1-e2;\
b=sqrt(b2);\
fl=1-b;\
rfl=1/fl;\
U0=GMearth/ecc/re*atan(eccp)+1/3*SpinEarthRadiansPerSec^2*re^2;\
HeightAboveEllipsoid=0;\
reh=re+HeightAboveEllipsoid;\
longscale(lat)=reh*Pi/648000/sqrt(1+b2*(tan(lat))^2);
latscale(lat)=reh*b*Pi/648000/(1-e2*(sin(lat))^2)^(3/2);
longarc(lat,long1,long2)=longscale(lat)*648000/Pi*(long2-long1);
latarc(lat1,lat2)=(intnum(th=lat1,lat2,sqrt(1-e2*(sin(th))^2))+e2/2*sin(2*lat1)/sqrt(1-e2*(sin(lat1))^2)-e2/2*sin(2*lat2)/sqrt(1-e2*(sin(lat2))^2))*reh;
\\=======
To apply that to your type of problem I will make up
that one of your data points was at
[Latitude, Longitude]=[+30, 30]
and the other at
[Latitude, Longitude]=[+30:00:16.237796,30:00:18.655502].
To convert those points to meters in two coordinates:
I can setup a system of coordinates in meters
with the first point being at the origin: [0,0] meters.
Then I can define the coordinate x-axis as due East-West,
and the y-axis as due North-South.
Then the second point's coordinates are:
? [longarc(30*Pi/180,30*Pi/180,((18.655502/60+0)/60+30)*Pi/180),latarc(30*Pi/180,((16.237796/60+0)/60+30)*Pi/180)]
%9 = [499.999998389040060103621525561027349597207, 499.999990137812119668486524932382720606325]
Warning on precision:
Note however:
Since the surface of the Earth is curved,
2-dimensional coordinates obtained on it can't follow
the same rules as cartesian coordinates
such as the Pythagorean Theorem perfectly.
Also lines pointing due North-South
converge in the Northern Hemisphere.
At the North Pole it becomes obvious
that North-South lines won't serve well for
lines parallel to the y-axis on a map.
At 30 degrees Latitude with 500 meter lengths,
the x-coordinate changes by 1.0228 inches if the scale is set from [0,+500] instead of [0,0]:
? [longarc(((18.655502/60+0)/60+30)*Pi/180,30*Pi/180,((18.655502/60+0)/60+30)*Pi/180),latarc(30*Pi/180,((16.237796/60+0)/60+30)*Pi/180)]
%10 = [499.974018595036400823218815901067566617826, 499.999990137812119668486524932382720606325]
? (%10[1]-%9[1])*1000/25.4
%12 = -1.02282653557713702372872677007019603860352
?
The error there of 500meters/1inch is only about 1/20000,
good enough for most diagrams,
but one might want to reduce the 1 inch error.
For a completely general way to convert
lat,long to orthogonal x,y coordinates
for any point on the globe, I would chose to abandon
aligning coordinate lines with East-West
and North-South, except still keeping the center
y-axis pointing due North. For example you could
rotate the globe around the poles (around the 3-D Z-axis)
so the center point in your map is at longitude zero.
Then tilt the globe (around the 3-D y-axis) to
bring your center point to lat,long = [0,0].
On the globe points at lat,long = [0,0] are
farthest from the poles and have a lat,long
grid around them that is most orthogonal
so you can use these new "North-South", "East-West"
lines as coordinate x,y lines without incurring
the stretching that would have occurred doing
that before rotating the center point away from the pole.
Showing an explicit example of that would take a lot more space.
Based on average distance for degress in the Earth.
1° = 111km;
Converting this for radians and dividing for meters, take's a magic number for the RAD, in meters: 0.000008998719243599958;
then:
const RAD = 0.000008998719243599958;
Math.sqrt(Math.pow(lat1 - lat2, 2) + Math.pow(long1 - long2, 2)) / RAD;
If you want a simple solution then use the Haversine formula as outlined by the other comments. If you have an accuracy sensitive application keep in mind the Haversine formula does not guarantee an accuracy better then 0.5% as it is assuming the earth is a sphere. To consider that Earth is a oblate spheroid consider using Vincenty's formulae.
Additionally, I'm not sure what radius we should use with the Haversine formula: {Equator: 6,378.137 km, Polar: 6,356.752 km, Volumetric: 6,371.0088 km}.
You need to convert the coordinates to radians to do the spherical geometry. Once converted, then you can calculate a distance between the two points. The distance then can be converted to any measure you want.