Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN

Authors

Ahmed Laatabi, UMR 7266 LIENSs, CNRS - University of La RochelleFollow
Nicolas Becu, UMR 7266 LIENSs, CNRS - University of La Rochelle
Nicolas Marilleau, UMI 209 UMMISCO, IRD
Cécilia Pignon-Mussaud, UMR 7266 LIENSs, CNRS - University of La Rochelle
Marion Amalric, UMR 7324 CITERES, University of Tours
Xavier Bertin, UMR 7266 LIENSs, CNRS - University of La Rochelle
Brice Anselme, UMR 8586 PRODIG, University of Paris 1
Elise Beck, UMR 5194 PACTE, University of Grenoble

Corresponding Author

Ahmed Laatabi

Abstract

For some scientific questions, empirical data are essential to develop reliable simulation models. These data usually come from different sources with diverse and heterogeneous formats. The design of complex data-driven models is often shaped by the structure of the data available in research projects. Hence, applying such models to other case studies requires either to get similar data or to transform new data to fit the model inputs. It is the case of agent-based models (ABMs) that use advanced data structures such as Geographic Information Systems data. We faced this problem in the LittoSIM-GEN project when generalizing our participatory flooding model (LittoSIM) to new territories. From this experience, we provide a mapping approach to structure, describe, and automatize the integration of geospatial data into ABMs.

Recommended Citation

Laatabi, Ahmed; Becu, Nicolas; Marilleau, Nicolas; Pignon-Mussaud, Cécilia; Amalric, Marion; Bertin, Xavier; Anselme, Brice; and Beck, Elise (2020) "Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN," International Journal of Geospatial and Environmental Research: Vol. 7: No. 1, Article 6.
Available at: https://dc.uwm.edu/ijger/vol7/iss1/6