GHSL — population quickstart¶

Download GHS-POP 2020 at 100 m over a small Lisbon AOI and map the residential population grid. The Mollweide source tile is reprojected to WGS84 and cropped to the bbox by pyramids.

Setup¶

Consolidate the imports. earthlens provides the unified EarthLens entry point; pyramids provides Dataset for reading the written GeoTIFF; and we use a temporary directory as the download target.

In [ ]:

import tempfile

import matplotlib.pyplot as plt
import numpy as np
from pyramids.dataset import Dataset

from earthlens import EarthLens

import tempfile import matplotlib.pyplot as plt import numpy as np from pyramids.dataset import Dataset from earthlens import EarthLens

Download GHS-POP¶

Build the request first — source, variable, year window, and a small bounding box over Lisbon — pointing the output at a temporary directory.

In [ ]:

out = tempfile.mkdtemp()
ghsl = EarthLens(
    data_source='ghsl',
    variables=['GHS_POP'],
    start='2020-01-01',
    end='2020-12-31',
    aoi=[-9.20, 38.68, -9.10, 38.78],
    path=out,
)

out = tempfile.mkdtemp() ghsl = EarthLens( data_source='ghsl', variables=['GHS_POP'], start='2020-01-01', end='2020-12-31', aoi=[-9.20, 38.68, -9.10, 38.78], path=out, )

Run the download. GHSL is a raster backend, so download() returns the list of written GeoTIFF paths.

In [ ]:

paths = ghsl.download(progress_bar=False)
paths

paths = ghsl.download(progress_bar=False) paths

Read and plot the grid¶

Read the written GeoTIFF with pyramids, collapse to a 2D array, and mask the no-data cells (negative values) to NaN.

In [ ]:

ds = Dataset.read_file(str(paths[0]))
arr = np.asarray(ds.read_array(), dtype='float32')
arr = arr[0] if arr.ndim == 3 else arr
arr = np.where(arr < 0, np.nan, arr)
print('epsg', ds.epsg, '| shape', arr.shape)

ds = Dataset.read_file(str(paths[0])) arr = np.asarray(ds.read_array(), dtype='float32') arr = arr[0] if arr.ndim == 3 else arr arr = np.where(arr < 0, np.nan, arr) print('epsg', ds.epsg, '| shape', arr.shape)

Map the residential population grid (people per 100 m cell).

In [ ]:

plt.figure(figsize=(6, 5))
plt.imshow(arr, cmap='magma')
plt.colorbar(label='people / cell')
plt.title('GHS-POP 2020 (100 m) — Lisbon')
plt.axis('off')
plt.show()

plt.figure(figsize=(6, 5)) plt.imshow(arr, cmap='magma') plt.colorbar(label='people / cell') plt.title('GHS-POP 2020 (100 m) — Lisbon') plt.axis('off') plt.show()

Headline number¶

A quick total — the number of residents inside the AOI.

In [ ]:

print(f'total population in AOI: {np.nansum(arr):,.0f}')

print(f'total population in AOI: {np.nansum(arr):,.0f}')