Kernel Density Estimate of Species Distributions

This shows an example of a neighbors-based query (in particular a kernel density estimate) on geospatial data, using a Ball Tree built upon the Haversine distance metric – i.e. distances over points in latitude/longitude. The dataset is provided by Phillips et. al. (2006). If available, the example uses basemap to plot the coast lines and national boundaries of South America.

This example does not perform any learning over the data (see Species distribution modeling for an example of classification based on the attributes in this dataset). It simply shows the kernel density estimate of observed data points in geospatial coordinates.

The two species are:

References

Traceback (most recent call last):
  File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/examples/neighbors/plot_species_kde.py", line 57, in <module>
    data = fetch_species_distributions()
  File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/.pybuild/cpython3_3.7/build/sklearn/datasets/species_distributions.py", line 242, in fetch_species_distributions
    samples_path = _fetch_remote(SAMPLES, dirname=data_home)
  File "/build/scikit-learn-qZGLk4/scikit-learn-0.20.2+dfsg/.pybuild/cpython3_3.7/build/sklearn/datasets/base.py", line 916, in _fetch_remote
    urlretrieve(remote.url, file_path)
  File "/usr/lib/python3.7/urllib/request.py", line 247, in urlretrieve
    with contextlib.closing(urlopen(url, data)) as fp:
  File "/usr/lib/python3.7/urllib/request.py", line 222, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib/python3.7/urllib/request.py", line 525, in open
    response = self._open(req, data)
  File "/usr/lib/python3.7/urllib/request.py", line 543, in _open
    '_open', req)
  File "/usr/lib/python3.7/urllib/request.py", line 503, in _call_chain
    result = func(*args)
  File "/usr/lib/python3.7/urllib/request.py", line 1360, in https_open
    context=self._context, check_hostname=self._check_hostname)
  File "/usr/lib/python3.7/urllib/request.py", line 1319, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [Errno 111] Connection refused>
# Author: Jake Vanderplas <jakevdp@cs.washington.edu>
#
# License: BSD 3 clause

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_species_distributions
from sklearn.datasets.species_distributions import construct_grids
from sklearn.neighbors import KernelDensity

# if basemap is available, we'll use it.
# otherwise, we'll improvise later...
try:
    from mpl_toolkits.basemap import Basemap
    basemap = True
except ImportError:
    basemap = False

# Get matrices/arrays of species IDs and locations
data = fetch_species_distributions()
species_names = ['Bradypus Variegatus', 'Microryzomys Minutus']

Xtrain = np.vstack([data['train']['dd lat'],
                    data['train']['dd long']]).T
ytrain = np.array([d.decode('ascii').startswith('micro')
                  for d in data['train']['species']], dtype='int')
Xtrain *= np.pi / 180.  # Convert lat/long to radians

# Set up the data grid for the contour plot
xgrid, ygrid = construct_grids(data)
X, Y = np.meshgrid(xgrid[::5], ygrid[::5][::-1])
land_reference = data.coverages[6][::5, ::5]
land_mask = (land_reference > -9999).ravel()

xy = np.vstack([Y.ravel(), X.ravel()]).T
xy = xy[land_mask]
xy *= np.pi / 180.

# Plot map of South America with distributions of each species
fig = plt.figure()
fig.subplots_adjust(left=0.05, right=0.95, wspace=0.05)

for i in range(2):
    plt.subplot(1, 2, i + 1)

    # construct a kernel density estimate of the distribution
    print(" - computing KDE in spherical coordinates")
    kde = KernelDensity(bandwidth=0.04, metric='haversine',
                        kernel='gaussian', algorithm='ball_tree')
    kde.fit(Xtrain[ytrain == i])

    # evaluate only on the land: -9999 indicates ocean
    Z = np.full(land_mask.shape[0], -9999, dtype='int')
    Z[land_mask] = np.exp(kde.score_samples(xy))
    Z = Z.reshape(X.shape)

    # plot contours of the density
    levels = np.linspace(0, Z.max(), 25)
    plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)

    if basemap:
        print(" - plot coastlines using basemap")
        m = Basemap(projection='cyl', llcrnrlat=Y.min(),
                    urcrnrlat=Y.max(), llcrnrlon=X.min(),
                    urcrnrlon=X.max(), resolution='c')
        m.drawcoastlines()
        m.drawcountries()
    else:
        print(" - plot coastlines from coverage")
        plt.contour(X, Y, land_reference,
                    levels=[-9998], colors="k",
                    linestyles="solid")
        plt.xticks([])
        plt.yticks([])

    plt.title(species_names[i])

plt.show()

Total running time of the script: ( 0 minutes 0.000 seconds)

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