In [1]:
import swisslandstats as sls
Read a land statistics file from the Swiss Federal Statistical Office (SFSO) e.g., the Standard nomenclature NOAS04
In [2]:
ldf = sls.read_csv('data/AREA_NOAS04_72_171129.csv')
In [3]:
# Transform the 4-category land statistics of 2009 `AS09_4` to a numpy array
ldf.to_ndarray('AS09R_4')
Out[3]:
array([[3, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 3, 3, 2],
[0, 0, 0, ..., 2, 1, 1],
...,
[3, 3, 2, ..., 0, 0, 0],
[3, 3, 3, ..., 0, 0, 0],
[3, 2, 2, ..., 0, 0, 0]], dtype=uint8)
In [4]:
# Export the same column to a GeoTIFF
ldf.to_geotiff('landstats.tiff', 'AS09R_4')
In [5]:
# Or plot it
ldf.plot('AS09R_4', cmap=sls.noas04_4_cmap, legend=True)
Out[5]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f526e7f85f8>
In [6]:
# Clip the dataframe by a place query to OSM's Nominatim
vaud_ldf = sls.clip_by_nominatim(ldf, 'Vaud, Switzerland')
vaud_ldf.plot('AS09R_4', cmap=sls.noas04_4_cmap, legend=True)
Out[6]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f5288287668>
In [6]:
import matplotlib.pyplot as plt
year_columns = ['85', '97', '09']
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
for year_column, ax in zip(year_columns, axes):
ax = vaud_ldf.plot('AS%sR_4' % year_column, cmap=sls.noas04_4_cmap, ax=ax)
ax.set_title(year_column)
