Table 1.

An overview of different types of concept- and data-driven models and their characteristics

		Requirement for creating the model
Name	Description	Conceptual understanding of the system	Numerical and data processing skills	Observations on state variables	Possibilities for calibration	Frequency of use
Concept-driven
SC	Static Concept-based modela	Intermediate	Low	Few	Easy, many methods	Intermediate
DI	Dynamic IBMb	Intermediate	Intermediate	Intermediate	Difficult, few methods	Intermediate
DC	Dynamic Continuum-based modelb	High	High	Intermediate	Intermediate, few methods	Low
Data-driven
SD	Static Data-based model	Low	Low	Intermediate	Easy, many methods	High
DD	Dynamic Data-based model	Low	High	Many	Intermediate, few methods	Low

Frequency of use in migration studies is provided in the last column; for references to specific studies see Table 2.

^aIn this context, static means that the process being studied is either in steady state or that there is no influence of previous states on the current state.

^bIndividual-based: model state variables refer to properties of an individual; continuum based: model state variables refer to population properties. State variables are model-entities which are updated at each model time step with a difference equation in dynamic models and are usually comparable to the dependent variables in static models.