Using Python to characterize the International Union for Conservation of Nature (IUCN)’s geospatial data base.
The International Union for Conservation of Nature (IUCN) launched several projects to guard wildlife. One in all these efforts led to a high-quality global geospatial database containing the habitats of greater than 100,000 species. In this text, I explore its subset, specializing in terrestrial mammals.
The IUCN’s Red List of Threatened Species database comprises greater than 150,000 species, with geospatial details about habitats attributed to 80%+ of them. This database’s mere size proposes several challenges, which I could address in a later article. For now, I deal with a smaller subset — the worldwide database consisting of terrestrial mammals with 12,436 records, each corresponding to at least one habitat patch per species. This mammal-habitat database is predicated on around 4 hundred different sources and comprises 5,626 species identified by their binomial names, registered between 2008 and 2022. Moreover, the database includes detailed taxonomic information, equivalent to the order and family of the species. Moreover, a primary strength of the database is that it has detailed geospatial information on habitats in the shape of polygon files, which I’ll explore in additional detail later.
First, I’ll introduce and explore the non-geometric features of this dataset after which conduct a number of analytical steps specific to the geospatial distribution of the various species. With this evaluation, I hope to popularize this data source and encourage future work with potential applications for wildlife protection policies.
It’s possible you’ll find all of the IUCN data sources here, from which I downloaded the Terrestrial Mammals polygon data (search date: 2023–10–02 at 15:30:02)
All images in this text were created by the creator.
1.1. Parse the dataset
First, let’s parse the database using GeoPandas and see what it comprises:
import geopandas as gpd # version: 0.9.0gdf_iucn = gpd.read_file('MAMMALS_TERRESTRIAL_ONLY')
print('Variety of records: ', len(gdf_iucn))…