Point datasets that relate to highly populated places, such as ones retrieved from social media or volunteered geographic information in general, can often result in dense point clusters when presented on maps. Therefore, it can be useful to visualize the relevant point density information directly on the urban geometry to tackle the problem of point counting and density range identification in highly cluttered areas. One solution is to relate each point to the nearest geometry object. While this is a straightforward approach, its major drawback is that local point clusters could disappear by assigning them to larger objects, e.g., long roads. To address this issue, we introduce two new point density visualization approaches by which points are related to the underlying geometry objects. In this process, we use grid cells and heatmap contour lines to divide roads, squares, and pedestrian zones into subgeometry units. Comparison of our visualization approaches with conventional density visualization methods shows that our approaches provide a more comprehensive insight into the point distribution over space, i.e., over existing urban geometry.