  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.
