Welcome to the Canis lupus meets Felis silvestris use case.
In this example the GBIF occurrence data of Canis lupus and Felis silvestris are filtered by the extent of Germany and joined to the land use classification of the IÖR land use classification.
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 join the occurrence data with the land use classification, it is also necessary to load the IÖR Landschaftsklassifikation by searching for it in the data catalogue.
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 IÖR Landschaftsklassifikation as raster data.
The result is that the vector data is spatially joined to the raster data by position. Therefore, a new column is added to the vector data table containing the information from the raster/the raster value. The float values are the result of the clustering calculating the mean of all integer encoded classes in classified data. This won't reflect in the downloaded data.
To visualise the classified data, it is recommended to use the Class Histogram operator, which translates the IÖR Landschaftsklassifiation 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.