I'm looking for a proper way to process multiple GPS tracks (so time ordered list of lat/lon points) and match/classify that segments, that are covered by multiple tracks: enter image description here

So it's not about recording, but using known algorithms/open source to do the analysis.
Sadly I didn't found a ready-to-use solution at the web, just scientific conceptual papers. My ideas would be:

  • use spatial index to scan just in point subsets (but what with points at borders?)
  • scan point sets with a buffer (to skip accuracy jitter)
  • classify points that sit in the buffer

But I like to have a real processing that is known to work. I guess this is a very common requirement to fleet monitoring solutions, so there must be some existing solutions that I just don't know :/
Can anyone suggest an existing tool (in QGIS or via a Python lib) that can do this matching?

  • Check out strava
    – mdsumner
    Commented Dec 29, 2013 at 11:34
  • Thank you, but the question isn't about recording hardware or using a commercial solution. I want to learn how I can do the work on the raw data on my own. I updated my post so this get's more clear.
    – Mapper
    Commented Dec 29, 2013 at 11:54
  • What format are your GPS tracks in? This will help determine what type of solution would be best. Commented Dec 29, 2013 at 22:42
  • Currently they are CSV, but a solution should just depend on an ordered list of (lat, lon) tuples.
    – Mapper
    Commented Dec 30, 2013 at 9:48
  • What do you mean by "match/classify" segments? Do you want to statistically identify road segments travelled by the GPS units?
    – Simbamangu
    Commented Dec 30, 2013 at 17:31

5 Answers 5


As @Loxodromes said above, I too am not sure that an open source library exists to do this. But it's simple enough to do in Python if you're happy enough with the scripting. For example, if you have access to numpy and scipy you can use a KDTree to easily calculate points from trail A that are within some tolerance of points from trail B.

With a bit of work you can take this a bit further by stacking the points into a single array and playing with labelled groups. This has the bonus of coping with more than two base data sets for comparison, though note this is not memory friendly - if you've got a lot of points you might need to do some work to make this more memory efficient. This also assumes everything is in the same projection.

import numpy as np
import scipy.spatial

For this example I'll dummy up some data, but take a look at numpy.loadtxt to read in your CSVs.

num_pts = 50
points_a = np.vstack([
    np.linspace(0., 10., num=num_pts),
    np.linspace(10., 0., num=num_pts)

points_b = points_a + np.random.random([num_pts, 2]) - 0.5
points_c = points_a + np.random.random([num_pts, 2]) - 0.5
points_d = points_a + np.vstack([
    np.sin(np.linspace(0., 2 * np.pi, num_pts)),
    np.sin(np.linspace(0., 2 * np.pi, num_pts)),

all_trails = [points_a, points_b, points_c, points_d]

You'll also need to specify a tolerance

tolerance = 0.1

Then, so you can process all the points in bulk but still know what group they're in, stack the arrays.

labelled_pts = np.vstack([
    np.hstack([a, np.ones((a.shape[0], 1)) * i])
    for i, a in enumerate(all_trails)

You can now build a KDTree from the labelled points. Remember that you don't want the labels themselves in the tree - they're used later on to classify results

tree = scipy.spatial.KDTree(labelled_pts[:, :2])

You use the ball point algorithm to get all the points within tolerance of another set of points (which is conveniently also our input points).

points_within_tolerance = tree.query_ball_point(labelled_pts[:, :2], tolerance)

This returns an array of the same length as the incoming points, with each value in the array being a tuple of indexes of the found points in the tree. Because you put in our original set there will always be at least one match. However you can then build a simple vectorisation function to test whether each item in the tree matches a point from a different group.

vfunc = np.vectorize(lambda a: np.any(labelled_pts[a, 2] != labelled_pts[a[0], 2]))

matches = vfunc(points_within_tolerance)
matching_points = labelled_pts[matches, :2]

The vfunc simply returns a numpy array of the results of this function, in this case True or False which we can use to index out our points.

So now you have points on the GPS trails which cross, but you want to group points into contiguous segments of track that overlap. For that you can use the scipy hierarchical clustering methods to group the data into groups which are linked by at most the tolerance distance.

import scipy.cluster.hierarchy

clusters = scipy.cluster.hierarchy.fclusterdata(matching_points, tolerance, 'distance')

clusters is an array of the same length of your matched points containing cluster indexes for each point. This means it's easy to get back a table of x, y, original_trail, segment by stacking the output together.

print np.hstack([
    matching_points,              #x, y
        labelled_pts[matches, 2], #original_trail
        clusters                  #segment

Or you can draw up the clusters.

from itertools import cycle, izip
import matplotlib.pyplot as plt

for pts, colour in izip(all_trails, cycle(['blue', 'red', 'orange', 'green', 'pink'])):
    plt.scatter(pts[:, 0], pts[:, 1], c=colour)

for clust_idx, shape, size in izip(set(clusters), cycle(['o', 'v', '^', '<', '>', 's', 'p', '*', '8', 'd']), cycle([40, 50, 60])):
    plt.scatter(matching_points[clusters == clust_idx, 0], matching_points[clusters == clust_idx, 1], c='yellow', marker=shape, s=size)


Sample output of the clustering. The yellow shapes are the different clusters.

Hopefully this all makes sense!

  • hi @om_henners thank you very much for the extra detailed answer and all the work you spend in creating the snippets. Unfortunatly I hadn't the time to check if it's working, but it's a very good reply that will help me a lot. I never thought about using numpy, as I usually use R (+frontends) for statistical jobs. All in all it's a superb SE answer and worth the bounty :)
    – Mapper
    Commented Jan 4, 2014 at 18:07

If I'm understanding correctly, a quick solution might be to just snap each track point to a grid, then do a boolean AND of the snapped version of each layer. A quick way to snap might be to just round the numbers to whatever accuracy you need:

example: x1=10.123, y1=4.567 x2=9.678, y2=5.123 x3=8.123, y3=8.123

rounding to the nearest unit, x1_rounded=10, y1_rounded=5 x2_rounded=10, y2_rounded=5 x3_rounded=8, y3_rounded=8

so, to the nearest whole unit, points 1 and 2 are at the same location.

Graphically, you'd use a boolean AND; expression-wise it would just be a matter of iterating over all points from all tracks, and for each point, iterating over all points from all other tracks, and doing 'if (x1_rounded=x2_rounded) then match' or such. Optimizing that iteration pattern for speed/efficiency would be possible if needed.

Is this what you were trying to accomplish?


I realize this question has been answered, but I have a slightly different take on it that I figure is worth sharing.

I expect this isn't language or platform specific.

  1. Turn both tracks into linestrings,
  2. Buffer one of the resultant linestrings by your expected/acceptable error margin (may require projecting to an alternate coordinate system), this results in the area that a track would need to be in to "match".
  3. Take the second linestring and intersect it with the area calculated from the first track. This results in a Multilinestring containing the portions of the second track that intersect the first.

in Python using shapely:

import matplotlib.pyplot as plt
from shapely.geometry import LineString
from descartes import PolygonPatch

        (119, 10), (118, 22), (118, 35), (119, 47), (121, 60),
        (124, 72), (128, 84), (133, 95), (139, 106), (145, 117),
        (152, 127), (159, 137), (167, 146), (176, 156), (184, 165),
        (193, 175), (202, 183), (210, 193), (219, 201), (228, 211),
        (236, 220), (244, 230), (252, 239), (259, 249), (266, 259),
        (272, 270), (278, 281), (283, 293), (286, 305), (289, 317),
        (290, 330), (289, 342), (287, 354), (283, 366), (277, 377),
        (269, 387), (259, 395), (248, 401), (236, 404), (224, 404),
        (212, 403), (200, 399), (189, 392), (179, 385), (170, 376),
        (162, 367), (157, 355), (152, 343), (148, 331), (145, 319),
        (144, 307), (142, 295), (142, 282), 
        (299, 30), (290, 21), (280, 14), (269, 8), (257, 4),
        (244, 2), (232, 1), (220, 2),  (208, 5), (196, 9),
        (185, 15), (175, 23),  (167, 32), (159, 42), (153, 53),
        (149, 65), (147, 78), (146, 90), (147, 102), (150, 115),
        (155, 126), (162, 137), (169, 147), (176, 156), (185, 166),
        (194, 174), (202, 183), (212, 191), (220, 200), (229, 209),
        (237, 219), (244, 231), (248, 242), (252, 253), (253, 266),
        (253, 279), (250, 291), (246, 303), (241, 314), (234, 324),
        (225, 333), (215, 340), (204, 347), (193, 351), (180, 354),
        (168, 355), (156, 353), (143, 351), (132, 346), (121, 340), 

this is simply data approximating the original image

track1=LineString([[p[1],p[0]] for p in tracks[0]])
track2=LineString([[p[1],p[0]] for p in tracks[1]])


ax = fig.add_subplot(111)

patch1 = PolygonPatch(track1_buffered, fc='blue', ec='blue', alpha=0.5, zorder=2)


tracks with one track buffered


ax = fig.add_subplot(111)

patch1 = PolygonPatch(match, fc='green', ec='green', alpha=0.5, zorder=2)


Matching regions

if we want we can clean it up further by running the same operation s with the opposite tracks and then intersecting them to cut out extraneous portions


ax = fig.add_subplot(111)

patch1 = PolygonPatch(match, fc='green', ec='green', alpha=0.5, zorder=2)


enter image description here


I'm not aware of any python libraries that do exactly what you're asking for but I imagine it wouldn't be too hard to write a solution using pure python. Python has a builtin CSV module for you to get the coordinates in operable format:


As I'm sure you are aware GPS coordinates are recorded in WGS84 by default so you'll need to make sure those are in numerical decimal format. Buffers could be introduced using a simple numerical addition to coordinates. i.e. x_max_buffer = x + arbitrarybuffervalue

After coordinates were in list format from the CSVs you could get each track into a list. Using for loops you could check where and for how long the two overlap with a tolerance specified by the buffer values. The real question depends on how you want this information outputted. You could take the segements that overlap and put those into lists to be outputted as CSVs for example.


you should check these discussions which already addressed the problem here and there . If you're after a "ready-to-use solution "for map-matching, check TrackMatching API from my profile (disclaimer: my work). Unfortunately I don't think there are QGIs plugins for that. A common problem is that you need to have the whole road network in memory to be efficient. This can be computationally expensive for large networks.

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