Modelling spatial dynamics may be used to gather understanding on how insect populations develop in a given environment. Hypotheses and independent knowledge inferred from ground observations can be confronted for consistency, and the mechanisms requiring finer descriptions can also be identified. Different scenarios of pest management can then be simulated and the possible consequences of the measures taken assessed. However, spatial dynamics are expressions of multiple and complex ongoing processes, and their modelling at different temporal and spatial scales remains a challenging task. Various approaches have been proposed to address this, including cellular automata, agent-based systems, discrete event systems, system dynamics and geographic information systems, each displaying specific benefits in some domains of application, and weaknesses in others. In this area of research, we are exploring an approach based on the manipulation of graphs (mathematical object expressing a set of entities, some of which are linked) that are employed here in an innovative way for modelling landscape dynamics. Concepts essential for modellers had to be identified and formally defined. A modelling computer language (called Ocelet) was then developed, together with the grammar and syntax needed to manipulate these concepts, the compiler, and the environment/interface for building models and running simulations. Ocelet is thus both a modelling language and a simulation tool. To illustrate its usage, two case studies possibly pertinent for pest management are presented: 1) the dissemination of a pathogen among neighbouring agricultural plots, and 2) temporary pond and mosquito population dynamics for understanding Rift Valley Fever (RVF) occurrence. (Texte intégral)