++ Currently, the examples are being reworked after the latest update because GBIF behaves differently now. Find out more. ++
Welcome to the Canis lupus meets Felis silvestris use case.
In this example the GBIF occurrence data of Canis lupus and Felis silvestris are cut to the extent of Germany and linked to the land use classification of the Ecosystematlas.
To begin, we select the Data Catalogue in the top right-hand corner. Here we have several data catalogues to choose from.
In our case, we start by searching for the individual species in the GBIF data provider. The search function makes it easy to find the species, so we search for Canis lupus and load the dataset by selecting it.
For the spatial selection we also need the German borders, which we found by searching for Germany in the data catalogue.
In order to link the occurrence data with the land use classification, it is also necessary to load the Oekosystematlas by searching for it in the personal data catalogue. The personal data catalogue contains all datasets uploaded by the user as well as a section with all datasets, which also contains datasets not listed.
The next step takes place in the Operators section, located in the top right-hand corner.
First we use a Point in Polygon Filter to restrict our occurrence data to Germany. For better readability it is recommended to name the datasets.
Next, we join the raster data to the vector data using the Raster Vector Join Operator, which takes the occurrence data as a vector and the Ecosystem Atlas as raster data.
The result is that the vector data is spatially linked to the raster data by position. Therefore, a new column is added to the vector data table containing the information.
To visualise the classified data, it is recommended to use the Class Histogram operator, which translates the Ecosystem Atlas numbers into class names using the metadata.
The graph then shows the distribution of occurrences according to class.
Using the same procedure for Felis silvestris, it is possible to compare the occurrence of the two species.
Warning: The VAT system is mainly used for data exploration. Changing the extent of the visual map will recalculate the workflow and could change the results! This must be taken into account when working scientifically with the VAT system. There is also a new window in the bottom left corner. This window must be present when working scientifically with the VAT system, as it allows reproducibility!
Tip: The layers have several options. They can be downloaded to work with the data in other systems. The layers also always have a workflow tree and the workflow_id can be copied to import the workflow directly into Python.