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:
- ?Bradypus variegatus? , the Brown-throated Sloth.
- ?Microryzomys minutus? , also known as the Forest Small Rice Rat, a rodent that lives in Peru, Colombia, Ecuador, Peru, and Venezuela.
References
- ?Maximum entropy modeling of species geographic distributions? S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006.
Out:
- computing KDE in spherical coordinates - plot coastlines from coverage - computing KDE in spherical coordinates - plot coastlines from coverage
# 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 = -9999 + np.zeros(land_mask.shape[0]) 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=[-9999], colors="k", linestyles="solid") plt.xticks([]) plt.yticks([]) plt.title(species_names[i]) plt.show()
Total running time of the script: (0 minutes 9.936 seconds)
plot_species_kde.py
plot_species_kde.ipynb
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