CLLD – Cross-Linguistic Linked Data

What open means - a case study

posted Wednesday, January 21, 2015 by Robert Forkel

Tsammalex, a multilingual lexical database on plants and animals, uses WWF Ecoregions as one facet to navigate species. Thus whenever new species are added to the database, we have to answer the question which ecoregions are populated by this species?

As it turns out, this question is relatively easy to answer due to Open Source software and Open Data. The remainder of this post will be a fairly technical description how to use the Open Source software Python, fiona and shapely and Open Data to answer this question.

We first download the shapefile containing the GIS data for the WWF Terrestrial Ecoregions of the World (README):

curl -O http://web.mit.edu/11.951/ecoplan/data/ecoregions/wwf_terr_ecos.shp
curl -O http://web.mit.edu/11.951/ecoplan/data/ecoregions/wwf_terr_ecos.dbf
curl -O http://web.mit.edu/11.951/ecoplan/data/ecoregions/wwf_terr_ecos.shx
curl -O http://web.mit.edu/11.951/ecoplan/data/ecoregions/wwf_terr_ecos.prj

Information about observations of species is available from GBIF.org via their great API. In particular we are going to use the species/match method to find a GBIF identifier for a scientific species name and the occurrence/search method to retrieve a list of occurrences for this species.

import requests

def gbif_api(path, **params):
    return requests.get('http://api.gbif.org/v1/' + path, params=params).json()


def occurrences(species):
    # Note: We limit the occurrences to ones observed by humans 
    # and tagged with geo-coordinates.
    kw = dict(
        taxonKey=gbif_api('species/match', name=species)['speciesKey'],
        basisOfRecord='HUMAN_OBSERVATION',
        hasCoordinate='true',
        limit=100)
    return gbif_api('occurrence/search', **kw)['results']

A single occurrence record serialized as JSON looks as follows:

        {
            "basisOfRecord": "HUMAN_OBSERVATION", 
            "catalogNumber": "551402", 
            ...
            "country": "United States", 
            "countryCode": "US", 
            "datasetName": "iNaturalist research-grade observations", 
            ...
            "decimalLatitude": 26.4395, 
            "decimalLongitude": -82.09795, 
            ...
        }

The ecoregion data in the shapefile when read with fiona is presented as records, with properties looking as follows:

           "properties": {
                "AREA": 29.8029417004, 
                "BIOME": 14.0, 
                "ECO_ID": 61404.0, 
                "ECO_NAME": "Northern Mesoamerican Pacific mangroves", 
                "ECO_NUM": 4.0, 
                "ECO_SYM": 119.0, 
                "G200_BIOME": 0.0, 
                "G200_NUM": 0.0, 
                "G200_REGIO": null, 
                "G200_STAT": 0.0, 
                "GBL_STAT": 1.0, 
                "OBJECTID": 1, 
                "PERIMETER": 0.219, 
                "REALM": "NT", 
                "Shape_Area": 0.00276856457255, 
                "Shape_Leng": 0.219475413877, 
                "area_km2": 8174, 
                "eco_code": "NT1404"
            }

Now finding ecoregions in which a species has been observed can be done as follows:

import fiona
from shapely.geometry import shape, Point

def get_ecoregions(species):
    with fiona.collection('wwf_terr_ecos.shp', "r") as source:
        ecoregions = [
            (feature['properties'], shape(feature['geometry']))
            for feature in source if feature['geometry']]

    eco_codes = {}
    for oc in occurrences(species):
        point = Point(oc['decimalLongitude'], oc['decimalLatitude'])
        for props, er in ecoregions:
            eco_code = props['eco_code']
            try:
                if er.contains(point):
                    if eco_code not in eco_codes:
                        eco_codes[eco_code] = props['ECO_NAME']
                        break
            except:
                pass
    return eco_codes

Putting it all together we get a script

which can be run as

$ python ecoregions_for_species.py "Ursus maritimus"
PA1101 Arctic desert
NA0616 Southern Hudson Bay taiga
NA1113 Kalaallit Nunaat low arctic tundra
NA1112 Kalaallit Nunaat high arctic tundra

Which looks about right considering that Ursus maritimus is also known as Polar Bear.

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