It is often essential to create datasets with foreseeable characteristics. For the design and testing of advanced spatiotemporal pattern mining algorithms,adaptable and large datasets are needed. In this paper, we present a synthetic dataset generator, ERMO-DG, that is intended for creating spatiotemporal patterns. Generated datasets consist of spatiotemporal object instances of different feature types, where these instances are represented by spatial regions evolving over time. The generator allows researchers to systematically create spatiotemporal datasets with predictable characteristics such as number of patterns, cardinality of patterns, velocity, acceleration, lifetime and spatialareas of instances.