This notebook presents a systematic movement data exploration workflow. The workflow consists of five main steps:
The workflow is demonstrated using horse collar tracking data provided by Prof. Lene Fischer (University of Copenhagen) and the Center for Technology & Environment of Guldborgsund Municiplaity in Denmark but should be generic enough to be applied to other tracking datasets.
import numpy as np
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 pyproj import CRS
from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
from holoviews import opts, dim, Layout
from os.path import exists
from urllib.request import urlretrieve
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=300, frame_height=400))
hvplot_defaults = {'tiles':None, 'cmap':'Viridis', 'colorbar':True}
mpd.show_versions()