HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

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

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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03095900
Contributor : Nicolas Becu Connect in order to contact the contributor
Submitted on : Monday, January 18, 2021 - 1:41:11 PM
Last modification on : Wednesday, May 18, 2022 - 11:20:05 AM
Long-term archiving on: : Monday, April 19, 2021 - 6:20:37 PM

File

littodata (1).pdf
Files produced by the author(s)

Identifiers

Citation

Ahmed Laatabi, Nicolas Becu, Nicolas Marilleau, Cécilia Pignon-Mussaud, Marion Amalric, et al.. Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN Models. International Journal of Geospatial and Environmental Research, Korea-America Association for Geospatial and Environmental Sciences (KAGES), 2020, 7 (1), pp.1-21. ⟨hal-03095900⟩

Share

Metrics

Record views

883

Files downloads

27