VGI is often generated as point data representing points of interest (POIs) and semantic qualities (such as accident locations) or quantities (such as noise levels), which can lead to geometric and thematic clutter in visual presentations of regions with numerous VGI contributions. As a solution, cartography provides several point generalization operations that reduce the total number of points and therefore increase the readability of a map. However, these operations are applied rather general and could remove specific spatial pattern, possibly leading to false interpretations in tasks where these spatial patterns are of interest. In this chapter, we want to tackle this problem by defining task-oriented sets of map generalization constraints that help to maintain spatial pattern characteristics during the generalization process. Therefore, we conduct a study to analyze the user behavior while solving interpretation tasks and use the findings as constraints in the following point generalization process, which is implemented through agent-based modeling.