The advancement of computational tools for cartometric analysis has opened new avenues for the identification and understanding of stemmatic relationships between historical maps through the analysis of their planimetric deformations. The 19th-century western cartographic depiction of Jerusalem serves as an exemplary case study in this context, since the difficulties in conducting comprehensive onsite surveys and the fascination towards the depiction of this area led to a proliferation of maps characterized by frequent errors, distortions, and widespread copying. How can similarities and differences between maps be measured, and what insights can be derived from these comparisons? This paper introduces a methodology aimed at detecting and segmenting regions of local similarity across maps, corresponding to the portions that were either copied between them or derived from a common source. To detect these areas, the Ground Control Points from the georeferencing process are employed to deform a common lattice grid for each map. These grids, triangulated to maintain shape rigidity, can be partitioned under conditions of geometric similarity, allowing for the segmentation and clustering of locally similar regions that represent shared areas between the maps. By integrating this segmentation with a filter on the intensity of deformation, the areas of the grid that are almost non-deformed, and thus not relevant for the study, can be excluded. Our analysis of fifty 19th-century maps of Jerusalem, the most extensive dataset computationally explored to date, reveals the effectiveness of this method in selecting areas of local similarity, even when limited. The methodology is applied to the maps in the dataset representing the Russian Compound and used as a tool to aid in the creation of the stemmata of the area's representation and to uncover new historical insight. These findings underscore the potential of computational cartometry in revealing hidden layers of cartographic knowledge and contributing to the digital genealogy of map collections.