Convert to common CRS and save to a file
As we have seen in the previous exercises, both datasets are using a different Coordinate Reference System (CRS). This is also illustrated by the first plot in this exercise (for which the code is already provided in the script): both datasets are about the same region, so they should normally overlap in their coordinates; but they don't.
For further analyses in the rest of this chapter, we will convert both datasets to the same CRS, and save both to a new file. To ensure we can do distance-based calculations, we will convert them to a projected CRS: the local UTM zone 35, which is identified by EPSG:32735 (https://epsg.io/32735).
The mining sites (mining_sites) and national parks (national_parks) datasets are already loaded, and GeoPandas and matplotlib are imported.
Este exercício faz parte do curso
Working with Geospatial Data in Python
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Plot the natural parks and mining site data
ax = national_parks.plot()
mining_sites.plot(ax=ax, color='red')
plt.show()
# Convert both datasets to UTM projection
mining_sites_utm = ____
national_parks_utm = ____
# Plot the converted data again
ax = national_parks_utm.plot()
mining_sites_utm.plot(ax=ax, color='red')
plt.show()