Understanding the way people move through urban areas is an important problem that has implications for a range of societal challenges such as city planning, public transportation, or crime analysis. We present a visualization tool called VizTrails for exploring and understanding such human movement [2]. For the explored movement, we utilize the Bayesian approach HypTrails to formulate and compare di erent hypotheses explaining the underlying processes [1]. VizTrails features aggregated statistics of trails for geographic areas on a map, e.g., the number of users passing through or the locations commonly visited next. Amongst other tools, VizTrails also allows to visualize the results of SPARQL queries in order to relate the observed statistics with its geo-spatial context, e.g., considering a city's points of interest. The insights from exploring the corresponding trajectories and features can be directly applied to modelling hypotheses about how the observed patterns can be explained. Then, the Bayesian approach HypTrails allows to compare the plausibility of such hypotheses with each other.