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Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.[sup 1] Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model. [ABSTRACT FROM AUTHOR]