The figure of the Earth can be modelled either by a cartesian plane, a sphere or an (oblate) ellipsoid, in decreasing order with respect to the approximation quality. The shortest path between two points on such a surface is called a geodesic. Studying geodesic problems on ellipsoids dates back to Newton. However, the majority of open-source GIS systems today use methods on the cartesian plane. The main advantages of those approaches are simplicity of implementation and performance. On the other hand, those approaches come with a handicap: accuracy. We experimentally study the accuracy-performance trade-offs of various methods for distance computation (as well as similar geodesic problems such as azimuth and area computation). We test projections paired with cartesian computations, spherical-trigonometric computations and a number of ellipsoidal methods such as [Andoyer'65] and [Thomas'70] formulas, [Vincenty'75] iterative method, great elliptic arc's method, and [Karney'15] series approximation. We also show that some methods from the bibliography (e.g. [Tseng'15]) are neither faster nor more accurate compared to the above list of methods and thus become redundant. For our experiments we use the open source libraries Boost Geometry and GeographicLib. Our results are of independent interest since we are not aware of a similar experimental study. More interestingly, they can be used as a reference for practitioners that want to use the most efficient method with respect to some given accuracy. Geodesic computations (such as distance computations) apart from being a fundamental problem in computational geometry and geography/geodesy are also building blocks for many higher level algorithms such as k-nearest neighbour problems, line interpolation, densification of geometries, area and buffer, to name a few. # References * Some experimental results can be found here: https://github.com/vissarion/geometry/wiki/Accuracy-and-performance-of-geographic-algorithms * A related talk (with some graphs on performance and accuracy) can be found here https://fosdem.org/2019/schedule/event/geo_boostgeometry * The source code of most of the algorithms of the study is in Boost Geometry: https://github.com/boostorg/geometry but we contain to our study GeographicLib https://geographiclib.sourceforge.io

1. Geodesic algorithms: an experimental study
Vissarion Fisikopoulos

2. Models of the Earth and coordinate systems
Flat (local approximation)
Sphere (Widely used e.g. google.maps)
Ellipsoid of revolution (geographic GIS state-of-the-art)
Geoid (Special applications, geophysics etc)

3. Geodesics
Definition: Geodesic = shortest path between a pair of points
flat: geodesic = straight line
sphere: geodesic = great circle
ellipsoid: geodesic = not closed curve (except meridians and
equator)
Note: loxodrome or rhump line is an arc crossing all meridians at
the same angle (=azimuth). These are straight lines in Mercator
projection and not shortest paths.

4. Geodesic problems
Direct given point, azimuth, distance compute new point
Inverse given two points compute distance, azimuth

5. (shortest) Distance between points
or inverse geodesic problem
flat: s = (x1 − x2)2 + (y1 − y2)2 (Pythagoras)
sphere: s = hav(ϕ2 − ϕ1) + cos(ϕ1) cos(ϕ2)hav(λ2 − λ1), where
hav(θ) = sin2
(θ
2) (Haversine formula)
ellipsoid:
s
b
=
σ
0
1 + k2 sin2
σ dσ , (1)
λ = ω − f sin α0
σ
0
2 − f
1 + (1 − f) 1 + k2 sin2
σ
dσ . (2)
λ, φ, s: longitude, latitude, distance
ω, σ: longitude, distance on auxiliary sphere
α0: azimuth at the equatorial
k = e cos α0 and f, e , b constants

6. Algorithms for geodesic problems
1. Higher series approximation (up-to order 8) + iterative
methods [Bessel’1865, Karney’12]
2. Numerical integration [Sjoberg’12, Panou et al.’13]
3. Iterative methods [Vincenty’75] (widely used)
4. Series approximation of order 2 [Thomas’70]
5. Series approximation of order 1 [Andoyer’65,Thomas’65]
6. Spherical (trigonometric approximation)
7. inverse elliptic arc length formulas [Thomas’65]
8. Projection + cartesian methods (local approximation)
(1) GeographicLib (PR in Boost Geometry)
(2) not considered here
(3-8) supported by Boost Geometry

7. Example: distance between points on ellipsoid
Athens (23.725750, 37.971536) — Brussels(4.3826169, 50.8119483)
higher series 2088384.366
vincenty 2088384.366
thomas 2088384.364
andoyer 2088389.079
elliptic-0 2087512.071
elliptic-1 2088385.162
elliptic-2 2088384.422
elliptic-3 2088384.422
spherical 2085992.801
flat approx 2213471.759

8. Algorithms using geodesic problems
distance between geometries
use distance computation, inverse problem
area
use azimuth computation, inverse problem
line interpolate point, densify
use direct and inverse problems
curve similarity: Hausdorff and Fr´echet distance
use distance computation, inverse problem

9. Example: distance between point and segment
L := LINESTRING(-16.42203 -7.52882,4.89998 -6.15568)
P := POINT(3.32696 -6.29345)
Boost.Geometry (version 1.71.0)
spherical: 508.0731159
andoyer: 480.3460262
thomas: 481.7457616
vincenty: 481.7390876
POSTGIS (version 2.5)
spherical: 508.073115931
geographic: 505.320928239
Homework: find the most accurate result!

10. Benchmarks
Dataset:
https://zenodo.org/record/32156#.WOyaMrUmv7C
WGS84 ellipsoid
1. 100000 geodesics uniform distributed in the ellipsoid and
2. 50000 short geodesics (of length less than 10 km).
https://github.com/vissarion/geometry/wiki/
Accuracy-and-performance-of-geographic-algorithms

11. Performance
method distance time azimuth time
flat* 7 35
spherical* 15 27
andoyer* 117 260
thomas* 270 371
vincenty* 485 463
GeographicLib 1224 1228
Table : Average timings for 150000 geodesics in nsec (100 runs for each
geodesic).
* as implemented in Boost Geomerty

12. Distance accuracy
long geodesics (> 1000km): the faster the method is the
lower the accuracy obtained
geodesics with endpoints far from antipodal (geodesics of
length less than 19500km):
spherical, andoyer, thomas, vincenty, high series have accuracy
21km, 811m, 48m, 78µm, 7nm respectively
flat approximation: very low accuracy, only useful for very
short distance e.g. distance between point(10, 10) and
point(10.1, 10.002) w. cm accuracy
The only method that is accurate (nm accuracy) for geodesics
that has endpoints nearly antipodal is high series method

13. Accuracy
distance
The maximum absolute distance error as a function of distance.

14. Area and azimuth accuracy
lower accuracy of short geodesics (< 10km)
For area we also test projection methods followed by cartesian area
computation
aea Albers Equal Area
https://proj4.org/operations/projections/
aea.html)
cea Equal Area Cylindrical
https://proj4.org/operations/projections/
cea.html
leac Lambert Equal Area Conic
https://proj4.org/operations/projections/
leac.html
laea Lambert Azimuthal Equal Area
https://proj4.org/operations/projections/
laea.html

15. Accuracy
azimuth
The maximum absolute azimuth error as a function of distance.

16. Accuracy
area
The maximum absolute area error as a function of distance

17. Thank you! Questions?

18. Solution: distance between point and segment
L := LINESTRING(-16.42203 -7.52882,4.89998 -6.15568)
P1 := POINT(3.32696 -6.29345)
P2 := POINT(3.3262, -6.28451)
Boost.Geometry (version 1.71.0)
P1 spherical: 508.0731159
P1 vincenty: 481.7390876
P2 spherical 489.553212
P2 vincenty 510.4827314
P1
P2
POSTGIS (version 2.5)
P1 spherical: 508.073115931
P1 geographic: 505.320928239
P2 spherical: 489.553211954
P2 geographic: 486.901261892