Generalizing trajectories¶
To reduce the size (number of points) of trajectory objects, we can generalize them, for example, using:
- Spatial generalization, such as Douglas-Peucker algorithm
- Temporal generalization by down-sampling, i.e. increasing the time interval between records
- Spatiotemporal generalization, e.g. using Top-Down Time Ratio algorithm
A closely related type of operation is trajectory smoothing which is coverd in a separate notebook.
In [1]:
import pandas as pd
import geopandas as gpd
import movingpandas as mpd
import shapely as shp
import hvplot.pandas
import matplotlib.pyplot as plt
from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
from holoviews import opts
import warnings
warnings.filterwarnings('ignore')
plot_defaults = {'linewidth':5, 'capstyle':'round', 'figsize':(9,3), 'legend':True}
opts.defaults(opts.Overlay(active_tools=['wheel_zoom'], frame_width=500, frame_height=400))
mpd.show_versions()
MovingPandas 0.17.0 SYSTEM INFO ----------- python : 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:34:57) [MSC v.1936 64 bit (AMD64)] executable : H:\miniconda3\envs\mpd-ex\python.exe machine : Windows-10-10.0.19045-SP0 GEOS, GDAL, PROJ INFO --------------------- GEOS : None GEOS lib : None GDAL : 3.7.0 GDAL data dir: None PROJ : 9.2.1 PROJ data dir: H:\miniconda3\pkgs\proj-9.0.0-h1cfcee9_1\Library\share\proj PYTHON DEPENDENCIES ------------------- geopandas : 0.13.2 pandas : 2.0.3 fiona : 1.9.4 numpy : 1.24.4 shapely : 2.0.1 rtree : 1.0.1 pyproj : 3.6.0 matplotlib : 3.7.2 mapclassify: 2.5.0 geopy : 2.3.0 holoviews : 1.17.0 hvplot : 0.8.3 geoviews : 1.9.6 stonesoup : 1.0
In [2]:
gdf = read_file('../data/geolife_small.gpkg')
tc = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')
In [3]:
original_traj = tc.trajectories[1]
print(original_traj)
Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 897 | Length: 38764.6m Bounds: (116.319212, 39.971703, 116.592616, 40.082514) LINESTRING (116.590957 40.071961, 116.590905 40.072007, 116.590879 40.072027, 116.590915 40.072004,
In [4]:
original_traj.plot(column='speed', vmax=20, **plot_defaults)
Out[4]:
<Axes: >
Spatial generalization (DouglasPeuckerGeneralizer)¶
Try different tolerance settings and observe the results in line geometry and therefore also length:
In [5]:
dp_generalized = mpd.DouglasPeuckerGeneralizer(original_traj).generalize(tolerance=0.001)
dp_generalized.plot(column='speed', vmax=20, **plot_defaults)
Out[5]:
<Axes: >
In [6]:
dp_generalized
Out[6]:
Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 31 | Length: 36921.9m Bounds: (116.319709, 39.971775, 116.592616, 40.082369) LINESTRING (116.590957 40.071961, 116.590367 40.073957, 116.590367 40.073957, 116.590367 40.073957,
In [7]:
print('Original length: %s'%(original_traj.get_length()))
print('Generalized length: %s'%(dp_generalized.get_length()))
Original length: 38764.575482545886 Generalized length: 36921.91845209718
Temporal generalization (MinTimeDeltaGeneralizer)¶
An alternative generalization method is to down-sample the trajectory to ensure a certain time delta between records:
In [8]:
time_generalized = mpd.MinTimeDeltaGeneralizer(original_traj).generalize(tolerance=timedelta(minutes=1))
time_generalized.plot(column='speed', vmax=20, **plot_defaults)
Out[8]:
<Axes: >
In [9]:
time_generalized.to_point_gdf().head(10)
Out[9]:
| id | sequence | trajectory_id | tracker | geometry | |
|---|---|---|---|---|---|
| t | |||||
| 2009-06-29 07:02:25 | 1556 | 1090 | 2 | 0 | POINT (116.59096 40.07196) |
| 2009-06-29 07:03:25 | 1569 | 1103 | 2 | 0 | POINT (116.59069 40.07225) |
| 2009-06-29 07:04:25 | 1582 | 1116 | 2 | 0 | POINT (116.59037 40.07396) |
| 2009-06-29 07:05:25 | 1595 | 1129 | 2 | 0 | POINT (116.59260 40.07411) |
| 2009-06-29 07:06:25 | 1610 | 1144 | 2 | 0 | POINT (116.59258 40.07420) |
| 2009-06-29 07:07:25 | 1623 | 1157 | 2 | 0 | POINT (116.59235 40.07602) |
| 2009-06-29 07:08:25 | 1635 | 1169 | 2 | 0 | POINT (116.58939 40.07794) |
| 2009-06-29 07:09:25 | 1647 | 1181 | 2 | 0 | POINT (116.58911 40.08171) |
| 2009-06-29 07:10:25 | 1659 | 1193 | 2 | 0 | POINT (116.58829 40.08232) |
| 2009-06-29 07:11:25 | 1672 | 1206 | 2 | 0 | POINT (116.58689 40.08230) |
In [10]:
original_traj.to_point_gdf().head(10)
Out[10]:
| id | sequence | trajectory_id | tracker | geometry | |
|---|---|---|---|---|---|
| t | |||||
| 2009-06-29 07:02:25 | 1556 | 1090 | 2 | 0 | POINT (116.59096 40.07196) |
| 2009-06-29 07:02:30 | 1557 | 1091 | 2 | 0 | POINT (116.59091 40.07201) |
| 2009-06-29 07:02:35 | 1558 | 1092 | 2 | 0 | POINT (116.59088 40.07203) |
| 2009-06-29 07:02:40 | 1559 | 1093 | 2 | 0 | POINT (116.59091 40.07200) |
| 2009-06-29 07:02:45 | 1560 | 1094 | 2 | 0 | POINT (116.59096 40.07198) |
| 2009-06-29 07:02:50 | 1561 | 1095 | 2 | 0 | POINT (116.59101 40.07196) |
| 2009-06-29 07:02:55 | 1562 | 1096 | 2 | 0 | POINT (116.59099 40.07198) |
| 2009-06-29 07:03:00 | 1563 | 1097 | 2 | 0 | POINT (116.59098 40.07199) |
| 2009-06-29 07:03:05 | 1564 | 1098 | 2 | 0 | POINT (116.59097 40.07200) |
| 2009-06-29 07:03:10 | 1565 | 1099 | 2 | 0 | POINT (116.59097 40.07200) |
Spatiotemporal generalization (TopDownTimeRatioGeneralizer)¶
In [11]:
tdtr_generalized = mpd.TopDownTimeRatioGeneralizer(original_traj).generalize(tolerance=0.001)
Let's compare this to the basic Douglas-Peucker result:
In [12]:
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(19,4))
tdtr_generalized.plot(ax=axes[0], column='speed', vmax=20, **plot_defaults)
dp_generalized.plot(ax=axes[1], column='speed', vmax=20, **plot_defaults)
Out[12]:
<Axes: >
In [ ]: