Volunteered Geographic Information in the form of actively and passively generated spatial content offers great potential to study people's activities, emotional perceptions, and mobility behavior. Realizing this potential requires methods which take into account the specific properties of such data, for example, its heterogeneity, subjectivity, and spatial resolution but also temporal relevance and bias. The aim of the chapter is to show how insights into human behavior can be gained from location-based social media and movement data using visual analysis methods. A conceptual behavioral model is introduced that summarizes people's reactions under the influence of one or more events. In addition, influencing factors are described using a context model, which makes it possible to analyze visitation and mobility patterns with regard to spatial, temporal, and thematic-attribute changes. Selected generic methods are presented, such as extended time curves and the cobridge metaphor to perform comparative analysis along time axes. Furthermore, it is shown that emojis can be used as contextual indicants to analyze sentiment and emotions in relation to events and locations.