Examples of input data

1. Enriching occurrences

If you wish to enrich the area in a buffer around a specific point (such as a species occurrence), you have two choices:

1.1. DarwinCore archive

A DarwinCore archive is a standard format for biodiversity data. In this case, column names and content follow CF conventions, which means that you don’t have to worry about formatting or column names. You can use the geoenrich.dataloader.open_dwca() function straight away.

1.2. CSV file

You may also use a custom csv file that does not follow any standard. In this case, when you use the geoenrich.dataloader.import_occurrences_csv() function, you have to specify the column names of your file.

A column with a unique ID is mandatory, to be able to link downloaded data to the corresponding occurrence. Date, latitude, and longitude columns are mandatory. Here is an exemple of such a file:

turtles.csv

ID	Lat	Lon	Day	Comments
turtle1	-28.752241	154.8926541	2018-07-29	bottom feeding
turtle2	2.5754611	72.964164	2019-02-13	cruising
turtle3	-21.2871554	55.316446	2021-01-05	resting

This file can be imported the following way:

geodf = import_occurrences_csv( path = 'path-to-folder/turtles.csv',
                                id_col = 'ID',
                                date_col = 'Day',
                                lat_col = 'Lat',
                                lon_col = 'Lon')

The date parser should work with any common date format. If you encounter problems with a custom date format, you can try to provide an explicit format string using the date_format parameter. See strptime documentation here.

2. Enriching areas

If you wish to download environmental data for arbitrary areas and dates, you have to provide a csv with predefined column names. Here is an example of such a file:

areas.csv

id	latitude_min	latitude_max	longitude_min	longitude_max	date_min	date_max
corsica	41.2	43.2	8.3	9.7	2015-06-01	2015-06-30
galapagos	-1.5	0.79	-91.9	-89	2022-03-18	2022-03-18
samoa	-14.1	-13.3	-172.9	-171.3	2018-11-21	2018-11-28

You can then use the geoenrich.dataloader.load_areas_file() function straight away.