On Shape Metrics in Cartographic Generalization: A Case Study of the Building Footprint Geometry

There are various methods, algorithms and automated tools for geometry generalization, however cartographers’ knowledge and experiences are crucial for a decision of what generalization level is suitable for the final visualization. This decision of experienced cartographer is usually correct but still quite subjective. Due to this reason we introduce the shape metrics as a tool to support objective evaluation of a generalization level. Shape metrics were originally applied in the landscape ecology in order to quantify landscape patches. Since then many scientific disciplines, including Geosciences, have adopted their principles. Generally, shape metrics serves as a quantitative description of any planar object (e.g. ground projection of a building) in order to measure its shape complexity or compactness. We used 15 shape metrics to calculate complexity of four buildings ground plans at 22 generalization levels in our case study. First, we performed shape generalization of four architecturally different buildings ground plans in consecutive levels. Then, we calculated shape metrics for each generalization level to quantify generalized shape complexity. We found out that shape metrics confirmed the fact that the higher level of geometry generalization the lower shape complexity. Nevertheless some shape metrics revealed that, in certain levels of generalization, the shape was not simplified. Aim of this paper is to introduce shape metrics application on geometry generalization in a case study and to propose suitable shape metrics to identify particular levels of an improper geometry generalization.