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

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