Horse collar data exploration

This notebook presents a systematic movement data exploration workflow. The workflow consists of five main steps:

1. Establishing an overview by visualizing raw input data records
2. Putting records in context by exploring information from consecutive movement data records (such as: time between records, speed, and direction)
3. Extracting trajectories, locations & events by dividing the raw continuous tracks into individual trajectories, locations, and events
4. Exploring patterns in trajectory and event data by looking at groups of the trajectories or events
5. Analyzing outliers by looking at potential outliers and how they may challenge preconceived assumptions about the dataset characteristics

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()