Agent-based modelling has been used in many studies of urban expansion, land use and land cover change patterns. While representing a powerful tool for depicting and formulating predictions about the evolution of interconnected complex systems, this method also poses a series of challenges to the researcher community, most notably in terms of model calibration and validation, and output data visualisation. Based on these findings, we present an agent-based model developed in GAMA, coupled with a data exploration platform coded in python, for analysing commuting patterns in the canton of Geneva, Switzerland. Output datasets generated from a set of simple evolution rules for the agents, are distributed in open access together with the code for the associated data visualisation platform. This prototype is early work in developing a series of agent-based models for simulating urban expansion and land cover change dynamics, together with their own data exploration platforms for calibration, validation and output data analysis purposes. These toolboxes will be developed with the intent of addressing the various shortcomings in agent-based modelling research discussed in this paper.