Visualizing time-dependent and location-based data is a challenging problem but highly relevant for areas like intelligence analysis, traffic control, or social network analysis. In this context, we address the problem of visualizing meetings between persons, groups of persons, vehicles, or other entities. However, the temporal dimension inherent in such data makes traditional map representations less well suited for this kind of problem as they easily become cluttered. To overcome this issue we developed a modified map representation and three alternative representations (two matrix-based visualizations and one based on Gantt charts). An empirical evaluation comparing these four visualizations and assessing correctness, recognition rates of groups, and subjective preference indicates that the alternative visualizations perform significantly better than the map-based representation when meetings need to be identified. In addition, we identify specific strengths and weaknesses of the investigated visualizations and propose design considerations.