This paper presents a method to evaluate a geographic knowledge discovery approach for exploring the motion of point objects. The goal is to provide a means of considering the significance of motion patterns, described through their interestingness. We use Monte-Carlo simulations of constrained random walks to generate populations of synthetic lifelines, using the statistical properties of real observational data as constraints. Pattern occurrence in the synthetic data is then compared with observational data to assess the potential interestingness of the found patterns. We use motion data from wildlife biology and spatialisation in political science for the evaluation. The results of the numerical experiments show that the interestingness of found motion patterns is largely dependant on the configuration of the pattern matching process, which includes the pattern extent, the temporal granularity, and the classification schema used for the motion attributes azimuth and speed. The results of the numerical experiments allow interestingness to be attached only to some of the patterns found — other patterns were suggested to be not interesting. The evaluation method helps in estimating useful configurations of the pattern detection process. This work emphasises the need to further investigate the statistical aspects of the problem under study in (geographic) knowledge discovery.