A fundamental problem in indoor location-based services is to compute the meaning of location with respect to an indoor location model. One specific challenge in this area is represented by the central tradeoff between two philosophies: a decent amount of the community tries to provide high-quality, high-fidelity models investing specialized knowledge and a lot of time in building such models for each building thereby increasing simplicity and quality of location-based services such as navigation or guidance. In contrast to that, other people argue that crowd sourcing and very simple representations of environmental information are the only way of generating indoor environmental information at scale. However, applications then have to tolerate errors and deal with oversimplified models. With this paper, we show for a specific widely accepted simple environmental model in which building floorplans are represented as black-and-white bitmaps, how we can provide algorithms for extracting higher order topological concepts from these trivial maps. We further illustrate how these can be applied to the hard problem of indoor shortest path calculation, indoor alternative path calculation, indoor spatial statistics, and path segmentation.