Gull Tracking#
The Research Institute for Nature and Forest (INBO) has been monitoring GPS locations of herring gulls breeding at the North Sea coast of Belgium since 2013. For 2018, there are 2.4 million bird-tracking coordinates, which are difficult to visualize with most plotting libraries but are easily visualized using Datashader. See DOI 10.5281/zenodo.3541812 for details and how to download the data.
import pandas as pd
import hvplot.pandas # noqa
from holoviews.util.transform import lon_lat_to_easting_northing as ll_en
The CSV file contains latitude and longitude information in degrees. Here we will want to overlay this data onto geographic mapping tiles that use the Web Mercator projection, so we use a utility from HoloViews to project the spherical coordinates into the tiles’ coordinate system to allow them to be overlaid correctly.
We will also center the plot on where most of the data is concentrated by plotting within selected longitude and latitude locations.
df = pd.read_csv('./data/HG_OOSTENDE-gps-2018.csv', usecols=['location-long', 'location-lat'])
df['easting'], df['northing'] = ll_en(df['location-long'], df['location-lat'])
df = df[(df['location-long'] > -14) & (df['location-lat'] < 53.3)]
print(df.head())
print(f"Length of data: {len(df)}")
location-long location-lat easting northing
0 2.819559 50.978461 313871.827614 6.617485e+06
1 2.819568 50.978458 313872.885149 6.617484e+06
2 2.819568 50.978466 313872.918545 6.617486e+06
3 2.819576 50.978476 313873.797969 6.617487e+06
4 2.819571 50.978457 313873.219107 6.617484e+06
Length of data: 2432290
df.hvplot.points(x='easting', y='northing', rasterize=True, tiles='EsriImagery',
cmap='fire', cnorm='eq_hist', dynspread=True, width=800, height=800)
/home/runner/work/examples/examples/gull_tracking/envs/default/lib/python3.11/site-packages/dask/dataframe/__init__.py:31: FutureWarning:
Dask dataframe query planning is disabled because dask-expr is not installed.
You can install it with `pip install dask[dataframe]` or `conda install dask`.
This will raise in a future version.
warnings.warn(msg, FutureWarning)
If you have a live Python process running, you should be able to select the wheel-zoom tool widget and then zoom into the above image and have it re-render the data at the new location, revealing more detail as you zoom in. For instance, here’s a large-scale region followed by a zoomed-in area, indicating that these gulls tend to spend a lot of time around certain man-made features of the landscape:
