Aggregating trajectories¶
The aggregation approach implemented in TrajectoryCollectionAggregator is based on Andrienko, N., & Andrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on visualization and computer graphics, 17(2), 205-219. and consists of the following main steps:
- Extracting characteristic points from the trajectories
- Grouping the extracted points by spatial proximity
- Computing group centroids and corresponding Voronoi cells
- Dividing trajectories into segments according to the Voronoi cells
- Counting transitions from one cell to another
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
import folium
from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
from holoviews import opts, dim
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 0.20.0 SYSTEM INFO ----------- python : 3.10.15 | packaged by conda-forge | (main, Oct 16 2024, 01:15:49) [MSC v.1941 64 bit (AMD64)] executable : c:\Users\Agarkovam\AppData\Local\miniforge3\envs\mpd-ex\python.exe machine : Windows-10-10.0.19045-SP0 GEOS, GDAL, PROJ INFO --------------------- GEOS : None GEOS lib : None GDAL : None GDAL data dir: None PROJ : 9.5.0 PROJ data dir: C:\Users\Agarkovam\AppData\Local\miniforge3\envs\mpd-ex\Library\share\proj PYTHON DEPENDENCIES ------------------- geopandas : 1.0.1 pandas : 2.2.3 fiona : None numpy : 1.23.1 shapely : 2.0.6 pyproj : 3.7.0 matplotlib : 3.9.2 mapclassify: 2.8.1 geopy : 2.4.1 holoviews : 1.20.0 hvplot : 0.11.1 geoviews : 1.13.0 stonesoup : 1.4
In [2]:
gdf = read_file("../data/geolife_small.gpkg")
tc = mpd.TrajectoryCollection(gdf, "trajectory_id", t="t")
In [3]:
tc.hvplot(line_width=7.0, tiles="CartoLight")
Out[3]:
In [4]:
tc.explore(column="trajectory_id", cmap="plasma", style_kwds={"weight": 4})
Out[4]:
Make this Notebook Trusted to load map: File -> Trust Notebook