Due to the advantages of being low cost and low power consumption, pyroelectric infrared sensors are widely employed in many applications, such as target location and tracking. In such applications, reference structure which is used to modulate views of binary sensors can help improve spatial resolutions by segmenting monitoring spaces into many cells which are identified by states (called signatures). A spatial resolution determines localization or tracking accuracy, which depends on the sizes, the shapes, and the signatures of the cells, and is drastically impacted by deployment of sensors and reference structure. However, in order to obtain a better deployment formation and a better spatial resolution, researchers have to spend lots of time to deploy and measure the deployment results of cells (such as their locations, sizes, shapes, and signatures), i.e., a huge amount of measurements which cost time and money in practice. Hence, in this paper, we propose a tool to visually and efficiently represent and analyze the deployment formation and spatial resolution. Generally, the location of a cell is represented by a list of vertices scattered in a cartesian coordinate system and can help researchers get its size, shape, and signature. A cell reconstruction algorithm is proposed to reconstruct cells generated by the deployment formation in which lists of vertices are generated by regarding cells as shortest path rings. We further provide some theoretical analysis and computational complexity analysis of the proposed algorithm. We conduct experiments using our tool to study sensor efficiency and spatial resolution easily and effectively.