approved
Predicting and visualising the egg supply chain network in Germany

The network model generates a dataframe as output regarding the estimates of food flows for chicken eggs in Germany on NUTS-3 level according to the selected parameters and a choropleth map for illustrating the distribution of product quantities. The required data for the network model will be obtained from the modelled egg supply chain network data cube and geodata from Eurostat (https://ec.europa.eu/eurostat/de/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts). The two parameters product and actor are implemented into the network model, wherefore different parameter variations can be simulated.The objective of the present network model is the prediction and visualisation of data obtained by a modelled food supply chain network data cube implemented as a graph.

Tags
Data and Resources
To access the resources you must log in
  • ChickenEgg_SCNM-originalFSKX

    Download file here

    The resource: 'ChickenEgg_SCNM-original' is not accessible as guest user. You must login to access it!
  • execute immediatelyURL

    execute model in VRE

    The resource: 'execute immediately' is not accessible as guest user. You must login to access it!
  • execute with new simulation parametersURL

    Change simulation parameters before running the model

    The resource: 'execute with new ...' is not accessible as guest user. You must login to access it!
Model scope
Field Value
Product Eggs Chicken
Model parameters
Field Value
Input Parameter actor: []( STRING), Default: Consumers
Output Parameter outputTable: []( MATRIXOFNUMBERS)
Additional Info
Field Value
Model Author Filter, Matthias, [email protected]
Model Author Fuhrmann, Marcel, [email protected]
Model Author Ganas, Petra, [email protected]
Model Creator Ganas, Petra, [email protected]
Model ID 1401ff44-8463-4cad-b7ac-192c51e8f0b6
Model Language Python 3.4.8
ReadMe This model is made available in the FSK-ML format, i.e. as .fskx file. To execute the model or to perform model-based predictions it is recommended to use the software FSK-Lab. FSK-Lab is an open-source extension of the open-source data analytics platform KNIME. To install FSK-Lab follow the installation instructions available at: https://foodrisklabs.bfr.bund.de/fsk-lab_de/. Once FSK-Lab is installed a new KNIME workflow should be created and the FSKX Reader node should be dragged into it. This FSKX Reader node can be configured to read in the given .fskx file. To perform a model-based prediction connect the out-port of the FSKX Reader node with the FSK Simulation Configurator JS node to adjust if necessary input parameters and store this into a user defined simulation setting, After that connect the output port with the input of a FSK Runner node that perform the simulation and look at the results at the node's outport.
Reference Description Dynamic freight flow modelling for risk evaluation in food supply DOI: https://doi.org/10.1016/j.tre.2018.03.002
system:type FSKXModel
Management Info
Field Value
Author Thomas Schueler
Maintainer Thomas Schueler
Last Updated 21 July 2022, 19:00 (CEST)
Created 11 November 2021, 03:00 (CET)